Proceedings of the 7th International Conference on Advances in Energy Research [1st ed.] 9789811559549, 9789811559556

Table of contents :
Front Matter ....Pages i-xxi
Determination of Steam Energy Factor for Wort Kettle as a Tool for Optimization of the Steam Energy (Shripad Kulkarni, Alex Bernard)....Pages 1-13
CMG-Based Simulation Study of Water Flooding of Petroleum Reservoir (Pratiksha D. Khurpade, Somnath Nandi, Pradeep B. Jadhav, Lalit K. Kshirsagar)....Pages 15-24
Exergy-Based Comparison of Two Gas Turbine Plants with Naphtha and Naphtha-RFG Mixture as Fuels (Sankalp Arpit, Sagar Saren, Prasanta Kumar Das, Sukanta Kumar Dash)....Pages 25-34
Decentralized Solid Waste Management for Educational-Cum-Residential Campus: A Pilot Study (Deepak Singh Baghel, Yogesh Bafna)....Pages 35-44
Does the Criteria of Instability Thresholds During Density Wave Oscillations Need to Be Redefined? (Subhanker Paul, Suparna Paul, Maria Fernandino, Carlos Alberto Dorao)....Pages 45-54
Solar Energy for Meeting Service Hot Water Demand in Hotels: Potential and Economic Feasibility in India (Niranjan Rao Deevela, Tara C. Kandpal)....Pages 55-69
Techno-economic Feasibility of Condenser Cooling Options for Solar Thermal Power Plants in India (Tarun Kumar Aseri, Chandan Sharma, Tara C. Kandpal)....Pages 71-79
Optical Modeling of Parabolic Trough Solar Collector (Anish Malan, K. Ravi Kumar)....Pages 81-89
Cooling Energy-Saving Potential of Naturally Ventilated Interior Design in Low-Income Tenement Unit (Ahana Sarkar, Ronita Bardhan)....Pages 91-101
Development of an Improved Cookstove: An Experimental Study ( Himanshu, S. K. Tyagi, Sanjeev Jain)....Pages 103-109
Impact of Demand Response Implementation in India with Focus on Analysis of Consumer Baseline Load (Jayesh Priolkar, E. S. Sreeraj)....Pages 111-121
Double Dielectric Barrier Discharge-Assisted Conversion of Biogas to Synthesis Gas ( Bharathi Raja, R. Sarathi, Ravikrishnan Vinu)....Pages 123-129
Thermo-Hydrodynamic Modeling of Direct Steam Generation in Parabolic Trough Solar Collector (Ram Kumar Pal, K. Ravi Kumar)....Pages 131-140
Hydrodeoxygenation of Bio-Oil from Fast Pyrolysis of Pinewood Over Various Catalysts (Kavimonica Venkatesan, Parasuraman Selvam, Ravikrishnan Vinu)....Pages 141-148
Simulation of Horizontal Axis Wind Turbine Using NREL FAST Solver (Asmelash Haftu Amaha, Prabhu Ramachandran, Shivasubramanian Gopalakrishnan)....Pages 149-158
Do Energy Policies with Disclosure Requirement Improve Firms’ Energy Management? Evidence from Indian Metal Sector (Mousami Prasad)....Pages 159-168
Power Management of Non-conventional Energy Resources-Based DC Microgrid Supported by Hybrid Energy Storage (Jaynendra Kumar, Anshul Agarwal, Nitin Singh)....Pages 169-180
Sizing of a Solar-Powered Adsorption Cooling System for Comfort Cooling (Sai Yagnamurthy, Dibakar Rakshit, Sanjeev Jain)....Pages 181-190
Experimental Evaluation of Common Rail Direct Injection Compression Ignition Engine with EGR Using Biodiesel (Suresh D. Mane, Chinna Bathulla)....Pages 191-199
Emission Measurement Considerations for Power Industry (A. Bekal, S. K. Karthick, Y. Rajeshirke, G. Balasubramaniam, M. Upadhyay, S. Bhandarkar et al.)....Pages 201-210
Impact of Growing Share of Renewable Energy Sources on Locational Marginal Prices (Leena Heistrene, Yash Shukla, Yaman Kalra, Poonam Mishra, Makarand Lokhande)....Pages 211-221
Performance Evaluation of Wind-Solar Hybrid System in Indian Context (Rahul Shityalkar, Ranjan Dey, Anagha Pathak, Niranjan Kurhe, Sandesh Jadkar)....Pages 223-230
Structural, Electrical and Cell Performance Study on Lithium Germanium Phosphate Glass Ceramics-Based Solid-State Li-Electrolyte (Anurup Das, Madhumita Goswami, P. Preetham, S. K. Deshpande, Sagar Mitra, M. Krishnan)....Pages 231-239
Adaptive Relaying Scheme for a Distribution Network with Highly Penetrated Inverter Based Distributed Generations (Kirti Gupta, Saumendra Sarangi)....Pages 241-251
Optimization in the Operation of Cabinet-Type Solar Dryer for Industrial Applications (Vishal D. Chaudhari, Govind N. Kulkarni, C. M. Sewatkar)....Pages 253-263
Modeling of Solar Photovoltaic-Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell to Charge Nissan Leaf Battery of Lithium Ion Type of Electric Vehicle (Kamaljyoti Talukdar)....Pages 265-273
Performance Study of an Anode Flow Field Design Used in PEMFC Application (S. A. Yogesha, Prakash C. Ghosh, Raja Munusamy)....Pages 275-284
Effect of Top Losses and Imperfect Regeneration on Power Output and Thermal Efficiency of a Solar Low Delta-T Stirling Engine (Siddharth Ramachandran, Naveen Kumar, Mallina Venkata Timmaraju)....Pages 285-294
Investigations on Recovery of Apparent Viscosity of Crude Oil After Magnetic Fluid Conditioning (A. D. Kulkarni, K. S. Wani)....Pages 295-304
Investigation on Different Types of Electric Storage Batteries Used in Off-grid Solar Power Plants and Procedures for Their Performance Improvement (Anindita Roy, Rajarshi Sen, Rupesh Shete)....Pages 305-315
Saving Electricity, One Consumer at a Time (K. Ravichandran, Sumathy Krishnan, Santhosh Cibi, Sumedha Malaviya)....Pages 317-326
Study of Effects of Water Inlet Temperature and Flow Rate on the Performance of Rotating Packed Bed ( Saurabh, D. S. Murthy)....Pages 327-337
Integrated Thermal Analysis of an All-Electric Vehicle (Vinayak Kulkarni, Shankar Krishnan)....Pages 339-348
Computation of Higher Eigenmodes Using Subspace Iteration Scheme and Its Application to Flux Mapping System of AHWR (B. Anupreethi, Anurag Gupta, Umasankari Kannan, Akhilanand Pati Tiwari)....Pages 349-357
ESCO Model for Energy-Efficient Pump Installation Scheme: A Case Study (Saurabh Khobaragade, Priyanka Bhosale, Priya Jadhav)....Pages 359-369
Transient Numerical Model for Natural Convection Flow in Flat Plate Solar Collector (Nagesh B. Balam, Tabish Alam, Akhilesh Gupta)....Pages 371-381
Rice Paddy as a Source of Sustainable Energy in India (Mohnish Borker, T. V. Suchithra)....Pages 383-392
Cost and Emission Trade-Offs in Electricity Supply for the State of Maharashtra (Pankaj Kumar, Trupti Mishra, Rangan Banerjee)....Pages 393-402
Technological Interventions in Sun Drying of Grapes in Tropical Climate for Enhanced and Hygienic Drying (Mallikarjun Pujari, P. G. Tewari, M. B. Gorawar, Ajitkumar P. Madival, Rakesh Tapaskar, V. G. Balikai et al.)....Pages 403-415
Effect of Temperature on the Hydrodynamics of Steam Reactor in a Chemical Looping Reforming System (Agnideep Baidya, Saptashwa Biswas, Avinash Singh, Debodipta Moitra, Pooja Chaubdar, Atal Bihari Harichandan)....Pages 417-425
Enhancement in Product Value of Potato Through Chemical Pre-treatment and Drying Process (M. B. Gorawar, S. V. Desai, V. G. Balikai, P. P. Revankar)....Pages 427-437
Desalination Using Waste Heat Recovery with Active Solar Still (Sandeep Kumar Singh, S. K. Tyagi, S. C. Kaushik)....Pages 439-447
Incorporating Battery Degradation in Stand-alone PV Microgrid with Hybrid Energy Storage (Ammu Susanna Jacob, Rangan Banerjee, Prakash C. Ghosh)....Pages 449-462
Simulation Studies on Design and Performance Evaluation of SAPV System for Domestic Application (M. R. Dhivyashree, M. B. Gorawar, V. G. Balikai, P. P. Revankar)....Pages 463-479
Development of a Dynamic Battery Model and Estimation of Equivalent Electrical Circuit Parameters (Sourish Ganguly, Subhrasish Pal, Ankur Bhattacharjee)....Pages 481-491
A Novel Switched Inductor Switched Capacitor-Based Quasi-Switched-Boost Inverter (P. Sriramalakshmi, Sreedevi V. T.)....Pages 493-503
Investigation of Energy Performance of a High-Rise Residential Building in Kolkata Through Performance Levels of Energy Conservation Building Code, 2017 (Gunjan Kumar, Biswajit Thakur, Sudipta De)....Pages 505-513
Addressing Last Mile Electricity Distribution Problems: Study of Performance of SHGs in Odisha (Sneha Swami, Subodh Wagle)....Pages 515-523
Transient Stability Analysis of Wind Integrated Power Network Using STATCOM and BESS Using DIgSILENT PowerFactory (Neha Manjul, Mahiraj Singh Rawat)....Pages 525-536
Experimental Investigation of Solar Drying Characteristics of Grapes (S. P. Komble, Govind N. Kulkarni, C. M. Sewatkar)....Pages 537-546
Feedback and Feedforward Control of Dual Active Bridge DC-DC Converter Using Generalized Average Modelling (Shipra Tiwari, Saumendra Sarangi)....Pages 547-557
Performance Assessment and Parametric Study of Multiple Effect Evaporator (Pranaynil Saikia, Soundaram Ramanathan, Dibakar Rakshit)....Pages 559-574
An Approach Towards Sustainable Energy Education in India (Pankaj Kalita, Rabindra Kangsha Banik, Samar Das, Dudul Das)....Pages 575-585
Simulation-Based Economic Optimization of Nuclear Renewable Hybrid Energy Systems with Reliability Constraints (Saikrishna Nadella, Anil Antony, N. K. Maheshwari)....Pages 587-597
Exergy Analysis and Cost Optimization of Solar Flat Pate Collector for a Two-Stage Absorption Refrigeration System with Water-Lithium Bromide as a Working Pair (Abhishek Verma, S. K. Tyagi, S. C. Kaushik)....Pages 599-610
Characterizing the Helical Vortex Frequency of HAWT (Ojing Siram, Niranjan Sahoo)....Pages 611-620
Design and Development of Concentrated Solar Cooker with Parabolic Dish Concentrator (Susant Kumar Sahu, Natarajan Sendhil Kumar, K. Arjun Singh)....Pages 621-631
Thermal and Electrical Performance Assessment of Elongated Compound Parabolic Concentrator ( Chandan, Sumon Dey, V. Suresh, M. Iqbal, K. S. Reddy, Bala Pesala)....Pages 633-643
Thermodynamic Analysis of a 500 MWe Coal-Fired Supercritical Thermal Power Plant Integrated with Molten Carbonate Fuel Cell (MCFC) at Flue Gas Stream (Akshini More, A. Pruthvi Deep, Sujit Karmakar)....Pages 645-654
Three-Dimensional Investigation on Energy Separation in a Ranque–Hilsch Vortex Tube (Nilotpala Bej, Pooja Chaubdar, Anish Pandey, K. P. Sinhamahapatra)....Pages 655-663
Bamboo Plant Intellect Deeds Optimization Algorithm for Solving Optimal Reactive Power Problem (Kanagasabai Lenin)....Pages 665-672
Actuator Fault Detection and Isolation for PEM Fuel Cell Systems Using Unknown Input Observers (Vikash Sinha, Sharifuddin Mondal)....Pages 673-683
Analysis of Heating and Cooling Energy Demand of School Buildings (Tshewang Lhendup, Samten Lhendup, Hideaki Ohgaki)....Pages 685-694
Thermodynamic Performance Analysis of Adsorption Cooling and Resorption Heating System Using Ammoniated Halide Salts (Rakesh Sharma, K. Sarath Babu, E. Anil Kumar)....Pages 695-705
Correlating Partial Shading and Operating Conditions to the Performance of PV Panels (S. Gairola, M. K. Sharma, J. Bhattacharya)....Pages 707-716
Engineering of O2 Electrodes by Surface Modification for Corrosion Resistance in Zinc–Air Batteries (Imran Karajagi, K. Ramya, Prakash C. Ghosh, A. Sarkar, N. Rajalakshmi)....Pages 717-723
Energy Farming—A Green Solution for Indian Cement Industry (Kapil Kukreja, Manoj Kumar Soni, B. N. Mohapatra, Ashutosh Saxena)....Pages 725-734
Energetic and Exergetic Performance Comparison of a Hybrid Solar Kalina Cycle at Solar and Solar Storage Mode of Operations (P. Bhuyan, P. Borah, T. K. Gogoi)....Pages 735-745
Assessment of Different Multiclass SVM Strategies for Fault Classification in a PV System (Rahul Kumar Mandal, Paresh G. Kale)....Pages 747-756
Performance Analysis of Double Glass Water Based Photovoltaic/Thermal System (Ajay Sharma, S. Vaishak, Purnanand V. Bhale)....Pages 757-766
Modeling Polarization Losses in HTPEM Fuel Cells (Vamsi Ambala, Anusree Unnikrishnan, N. Rajalakshmi, Vinod M. Janardhanan)....Pages 767-773
Effect of Diesel Injection Timings on the Nature of Cyclic Combustion Variations in a RCCI Engine (Ajay Singh, Rakesh Kumar Maurya, Mohit Raj Saxena)....Pages 775-784
Investigating the Impact of Energy Use on Carbon Emissions: Evidence from a Non-parametric Panel Data Approach (Barsha Nibedita, Mohd Irfan)....Pages 785-794
Studies on the Use of Thorium in PWR (Devesh Raj, Umasankari Kannan)....Pages 795-804
Coaxial Thermal Probe for High-Frequency Periodic Response in an IC Engine Test Rig (Anil Kumar Rout, Santosh Kumar Hotta, Niranjan Sahoo, Pankaj Kalita, Vinayak Kulkarni)....Pages 805-813
Effect of Injection Pressure on the Performance Characteristics of Double Cylinder Four-Stroke CI Engine Using Neem Bio-diesel (Sushant S. Satputaley, Iheteshamhusain Jafri, Gauravkumar Bangare, Rahul P. Kavishwar)....Pages 815-824
Experimental Study of a Helical Coil Receiver Using Fresnel Lens (Sumit Sharma, Sandip K. Saha)....Pages 825-835
Substrate-Assisted Electrosynthesis of Patterned Lamellar Type Indium Selenide (InSe) Layer for Photovoltaic Application (A. B. Bhalerao, S. B. Jambure, R. N. Bulakhe, S. S. Kahandal, S. D. Jagtap, A. G. Banpurkar et al.)....Pages 837-845
Optimization of Injector Location on the Cylinder Head in a Direct Injection Spark Ignition Engine (Srinibas Tripathy, Sridhar Sahoo, Dhananjay Kumar Srivastava)....Pages 847-855
Automated Cleaning of PV Panels Using the Comparative Algorithm and Arduino (Huzefa Lightwala, Dipesh Kumar, Nidhi Mehta)....Pages 857-868
Performance and Degradation Analysis of High-Efficiency SPV Modules Under Composite Climatic Condition (Shubham Sanyal, Arpan Tewary, Rakesh Kumar, Birinchi Bora, Supriya Rai, Manander Bangar et al.)....Pages 869-878
Energy Literacy of University Graduate Students: A Multidimensional Assessment in Terms of Content Knowledge, Attitude and Behavior (Divya Chandrasenan, Jaison Mammen, Vaisakh Yesodharan)....Pages 879-889
Waste-to-Energy: Issues, Challenges, and Opportunities for RDF Utilization in Indian Cement Industry (Prateek Sharma, Pratik N. Sheth, B. N. Mohapatra)....Pages 891-900
Predict the Effect of Combustion Parameter on Performance and Combustion Characteristics of Small Single Cylinder Diesel Engine (D. K. Dond, N. P. Gulhane)....Pages 901-912
Experimental Investigation of a Biogas-Fueled Diesel Engine at Different Biogas Flow Rates (Naseem Khayum, S. Anbarasu, S. Murugan)....Pages 913-921
Characteristics of an Indigenously Developed 1 KW Vanadium Redox Flow Battery Stack (Sreenivas Jayanti, Ravendra Gundlapalli, Raghuram Chetty, C. R. Jeevandoss, Kothandaraman Ramanujam, D. S. Monder et al.)....Pages 923-929
Dynamic Demand Response Through Decentralized Intelligent Control of Resources (M. T. Arvind, Anoop R. Kulkarni)....Pages 931-943
Transient Analysis of Pressurizer Steam Bleed Valves Stuck Open for 700 MWe PHWRs (Deepraj Paul, S. Pahari, S. Hajela, M. Singhal)....Pages 945-952
Transient Analysis of Net Load Rejection for 700 MWe IPHWRs (S. Phani Krishna, S. Pahari, S. Hajela, M. Singhal)....Pages 953-959
A Comparative Experimental Investigation of Improved Biomass Cookstoves for Higher Efficiency with Lower Emissions (Sandip Bhatta, Dhananjay Pratap, Nikhil Gakkhar, J. P. S. Rajput)....Pages 961-971
Assessment of Floating Solar Photovoltaic (FSPV) Potential in India (Ashish Kumar, Ishan Purohit, Tara C. Kandpal)....Pages 973-982
Effect of Non-Revenue Water Reduction in the Life Cycle of Water–Energy Nexus: A Case Study in India (Rajhans Negi, Vipin Singh, Munish K. Chandel)....Pages 983-990
Policy Intervention for Promoting Effective Adaptation of Rooftop Solar PV Systems (Sabreen Ahmed, C. Vijayakumar, Arjun D. Shetty)....Pages 991-1001
Improved Dispatchability of Solar Photovoltaic System with Battery Energy Storage (Sheikh Suhail Mohammad, S. J. Iqbal)....Pages 1003-1013
Numerical Investigation of the Performance of Pump as Turbine with Back Cavity Filling (Rahul Gaji, Ashish Doshi, Mukund Bade)....Pages 1015-1024
Mining Representative Load Profiles in Commercial Buildings (Kakuli Mishra, Srinka Basu, Ujjwal Maulik)....Pages 1025-1036
A Simplified Non-iterative Method for Extraction of Parameters of Photovoltaic Cell/Module (Kumar Gaurav, Neha Kumari, S. K. Samdarshi, A. S. Bhattacharyya)....Pages 1037-1047
Design, Analysis and Hardware Implementation of Modified Bipolar Solid-State Marx Generator (Neelam S. Pinjari, S. Bindu)....Pages 1049-1059
Viability Study of Stand-Alone Hybrid Energy Systems for Telecom Base Station (M. Siva Subrahmanyam, E. Anil Kumar)....Pages 1061-1070
Effect of Temperature and Salt Concentration on the Properties of Electrolyte for Sodium-Ion Batteries (Bharath Ravikumar, Surbhi Kumari, Mahesh Mynam, Beena Rai)....Pages 1071-1081
Carbon Deposition on the Anode of a Solid Oxide Fuel Cell Fueled by Syngas—A Thermodynamic Analysis (N. Rakesh, S. Dasappa)....Pages 1083-1090
Numerical Study on CO2 Injection in Indian Geothermal Reservoirs Using COMSOL Multiphysics 5.2a (Nandlal Gupta, Manvendra Vashistha)....Pages 1091-1102
Modification in the Rotor of Savonius Turbine to Reduce Reverse Force on the Returning Blade (J. Ramarajan, S. Jayavel)....Pages 1103-1111
Design and Fabrication of Grating-Based Filters for Micro-thermophotovoltaic Systems (M. V. N. Surendra Gupta, E. Ameen, Ananthanarayanan Veeraragavan, Bala Pesala)....Pages 1113-1119
A Systematic Investigation on Evaporation, Condensation and Production of Sustainable Water from Novel-Designed Tubular Solar Still (Mihir Lad, Nikunj Usadadia, Sagar Paneliya, Sakshum Khanna, Vishwakumar Bhavsar, Indrajit Mukhopadhyay et al.)....Pages 1121-1130
Novel Design of PV Integrated Solar Still for Cogeneration of Power and Sustainable Water Using PVT Technology (Nikunj Usadadia, Mihir Lad, Sagar Paneliya, Sakshum Khanna, Abhijit Ray, Indrajit Mukhopadhyay)....Pages 1131-1143
Cellulose Nanocrystals Incorporated Proton Exchange Membranes for Fuel Cell Application (Saleheen Bano, Asif Ali, Sauraj, Yuvraj Singh Negi)....Pages 1145-1153
Study of the Effect of Biomass-Derived N-Self Doped Porous Carbon in Microbial Fuel Cell (Saswati Sarmah, Minakshi Gohain, Dhanapati Deka)....Pages 1155-1163
Analysis of Nature-Inspired Spirals for Design of Solar Tree (Sumon Dey, Madan Kumar Lakshmanan, Bala Pesala)....Pages 1165-1173
Effective Use of Existing Efficient Variable Frequency Drives (VFD) Technology for HVAC Systems—Consultative Research Case Studies (Rahul Raju Dusa, Atulkumar Auti, Vijay Mohan Rachabhattuni)....Pages 1175-1184
Thermodynamic Analysis of a Combined Power and Cooling System Integrated with CO2 Capture Unit of a 500 MWe SupC Coal-Fired Power Plant (Rajesh Kumar, Goutam Khankari, Sujit Karmakar)....Pages 1185-1198
DFT Studies on Electronic and Optical Properties of Inorganic CsPbI3 Perovskite Absorber for Solar Cell Application (Abhijeet Kale, Rajneesh Chaurasiya, Ambesh Dixit)....Pages 1199-1206
Biowaste Derived Highly Porous Carbon for Energy Storage (Dinesh J. Ahirrao, Shreerang D. Datar, Neetu Jha)....Pages 1207-1214
Bio-Ethanol Production from Carbohydrate-Rich Microalgal Biomass: Scenedesmus Obliquus (Maskura Hasin, Minakshi Gohain, Dhanapati Deka)....Pages 1215-1224
Safety Analysis of Loss of NPP Off-Site Power with Failure of Reactor SCRAM (ATWS) for VVER-1000 (Manish Mehta, Sanuj Chaudhary, Anirban Biswangri, P. Krishna Kumar, Y. K. Pandey, Gautam Biswas)....Pages 1225-1236
P-type Crystalline Silicon Surface Passivation Using Silicon Oxynitride/SiN Stack for PERC Solar Cell Application (Irfan M. Khorakiwala, Vikas Nandal, Pradeep Nair, Aldrin Antony)....Pages 1237-1244
Pressure Propagation and Flow Restart in the Subsea Pipeline Network (Lomesh Tikariha, Lalit Kumar)....Pages 1245-1253
Electrodeposition of Cu2O: Determination of Limiting Potential Towards Solar Water Splitting (Iqra Reyaz Hamdani, Ashok N. Bhaskarwar)....Pages 1255-1264
Design and Development of an Economical and Reliable Solar-Powered Trash Compactor (Ridhi Lakhotia, Abu Fazal, Ajay Yadav, Ankur Bhattacharjee)....Pages 1265-1273
Performance and Emission Characteristics of CI Engine Fueled with Plastic Oil Blended with Jatropha Methyl Ester and Diesel (S. Babu, K. Kavin, S. Niju)....Pages 1275-1286
Performance Analysis of Hybrid Photovoltaic Array Configurations Under Randomly Distributed Shading Patterns (Vandana Jha)....Pages 1287-1296
Flow Improvement Aspect with Stagger Angle Variation of the Subsequent Rotor in Contra-rotating Axial Flow Turbine (Rayapati Subbarao)....Pages 1297-1307
Performance Assessment of Pelton Turbine with Traditional and Novel Hooped Runner by Experimental Investigation (Vimal K. Patel, Hemal N. Lakdawala, Sureel Dohare, Gaurang Chaudhary)....Pages 1309-1318
Evaluation of LVRT Control Strategies for Offshore Wind Farms (M. M. Kabsha, Zakir Rather)....Pages 1319-1330
An Experimental and CFD Analysis on Heat Transfer and Fluid Flow Characteristics of a Tube Equipped with X-Shaped Tape Insert in a U-Shaped Heat Exchanger (Sagar Paneliya, Sakshum Khanna, Jeet Mehta, Vishal Kathiriya, Umang Patel, Parth Prajapati et al.)....Pages 1331-1348
Single-Particle Analysis of Thermally Thick Wood Particles in O2, N2, CO2 Atmosphere (Shruti Vikram, Sandeep Kumar)....Pages 1349-1359
An Analysis for Management of End-of-Life Solar PV in India (Snehalata Pankadan, Swapnil Nikam, Naqui Anwer)....Pages 1361-1371
Localized Energy Self-sufficiency (Energy Swaraj) for Energy Sustainability and Mitigating Climate Change (Chetan Singh Solanki, Sayli Shiradkar, Rohit Sharma, Jayendran Venkateswaran, Nikita Lihinar, Harshad Supal et al.)....Pages 1373-1381
Pseudocapacitive Energy Storage in Copper Oxide and Hydroxide Nanostructures Casted Over Nickel-Foam (Priyanka Marathey, Sakshum Khanna, Roma Patel, Indrajit Mukhopadhyay, Abhijit Ray)....Pages 1383-1391
Validation of Computer Code Based on Nodal Integral Method Against KAPS-2 Phase-B Data (Manish Raj, Sherly Ray, A. S. Pradhan, Suneet Singh)....Pages 1393-1401
Bipolar DC Micro-Grid Based Wind Energy Systems (Dodda Satish Reddy, Suman Kumar, Bonala Anil Kumar, Sandepudi Srinivasa Rao)....Pages 1403-1413
Processing Thermogravimetric Analysis Data for Pyrolysis Kinetic Study of Microalgae Biomass (Pravin G. Suryawanshi, Vaibhav V. Goud)....Pages 1415-1424
Photovoltaic Thermal Collectors with Phase Change Material for Southeast of England (Preeti Singh, Rajvikram Madurai Elavarasan, Nallapaneni Manoj Kumar, Sourav Khanna, Victor Becerra, Sanjeev Newar et al.)....Pages 1425-1430
Modeling and Simulation of Hollow Fiber Biocatalyst Membrane Reactor (Nooram Anjum, Mohammad Danish, Sarah Anjum)....Pages 1431-1440
Efficient Alkaline Peroxide Pretreatment of Sterculia foetida Fruit Shells for Production of Reducing Sugar: Effect of Process Parameters on Lignin Removal (S. Sardar, A. Das, S. Saha, C. Mondal)....Pages 1441-1451
Performance Enhancement of Savonius Hydrokinetic Turbine with a Unique Vane Shape: An Experimental Investigation (Vimal K. Patel, Kushal Shah, Vikram Rathod)....Pages 1453-1463
Techno-economic Analysis for Production of Biodiesel and Green Diesel from Microalgal Oil (Swarnalatha Mailaram, Nitesh Dobhal, Sunil K. Maity)....Pages 1465-1475
Numerical Investigation on the Effect of EGR in a Premixed Natural Gas SI Engine (Sridhar Sahoo, Srinibas Tripathy, Dhananjay Kumar Srivastava)....Pages 1477-1487
Transitions in the Indian Electricity Sector: Impacts of High Renewable Share (Aishwarya V. Iyer, Rangan Banerjee)....Pages 1489-1500
Comparison of Physics Characteristics of Pressurized Water Reactor Type Advanced Light Water Reactors (L. Thilagam, D. K. Mohapatra)....Pages 1501-1511
Development of a Python Module “SARRA” for Refuelling Analysis of MSR Using DRAGON Code (A. K. Srivastava, Anurag Gupta, Umasankari Kannan)....Pages 1513-1519
The Effect of Concentration Ratio and Number of P-N Thermocouples on Photovoltaic-Thermoelectric Hybrid Power Generation System (Abhishek Tiwari, Shruti Aggarwal)....Pages 1521-1532
Evaluation of Annual Electrical Energy Through Semitransparent (Glass to Glass) and Opaque Photovoltaic Module in Clear Sky Condition at Composite Climate: A Comparative Study (Rohit Tripathi, Nitin K. Gupta, Deepak Sharma, G. N. Tiwari, T. S. Bhatti)....Pages 1533-1542
Current Practices and Emerging Trends in Safety Analysis of NPPs (K. Obaidurrahman)....Pages 1543-1549
Electrochemical Reduction of CO2 on Ionic Liquid Stabilized Reverse Pulse Electrodeposited Copper Oxides (Nusrat Rashid, Pravin P. Ingole)....Pages 1551-1558
Performance of Flux Mapping System During Spatial Xenon Induced Oscillations in PHWRs (Abhishek Chakraborty, M. P. S. Fernando, A. S. Pradhan)....Pages 1559-1570
Forecasting of Electricity Demand and Renewable Energy Generation for Grid Stability (Joel Titus, Urvi Shah, T. Siva Rama Sarma, Bhushan Jagyasi, Pallavi Gawade, Mamta Bhagwat et al.)....Pages 1571-1581
Platooning of Flat Solar-Panel-Mounted Mini Bus Model—A Numerical Investigation (Mohammad Rafiq B. Agrewale, R. S. Maurya)....Pages 1583-1593
Co-sensitization of Perovskite Solar Cells by Organometallic Compounds: Mechanism and Photovoltaic Characterization (Nisha Balachandran, Temina Mary Robert, Dona Mathew, Jobin Cyriac)....Pages 1595-1601
Nuclear Power Plants and Human Resources Development in South Asia (Firoz Alam, Rashid Sarkar, Akshoy Ranjan Paul, Abdulaziz Aldiab)....Pages 1603-1613
Highly Stable Pt/CVD-Graphene-Coated Superstrate Cu2O Photocathode for Water Reduction (Chandan Das, Akhilender Jeet Singh, K. R. Balasubramaniam)....Pages 1615-1621
Thermodynamic Studies on Steel Slag Waste Heat Utilization for Generation of Synthesis Gas Using Coke Oven Gas (COG) as Feedstock (M. Srinivasarao, Nilu Kumar, A. Syamsundar)....Pages 1623-1632
Reactivity-Initiated Transients for 700 MWe PHWR (Suresh Kandpal, M. P. S. Fernando, A. S. Pradhan)....Pages 1633-1643
Multi-field Solar Thermal Power Plant with Linear Fresnel Reflector and Solar Power Tower (Shridhar Karandikar, Irfan Shaikh, Anish Modi, Shireesh B. Kedare, Balwant Bhasme)....Pages 1645-1655
Experimental Investigation Using Enriched Biogas in S-I Engine for Stable Rural Electrification (Antony P. Pallan, Deepak Mathew, Yohans Varghese)....Pages 1657-1666
Solar Autoclave for Rural Hospitals Using Aerogel as Transparent Insulation Material (Manoj Kumar Yadav, Anish Modi, Shireesh B. Kedare)....Pages 1667-1677
Numerical Investigation on the Influence of Reactant Gas Concentration on the Performance of a PEM Fuel Cell (Brajesh Kumar Kanchan, Pitambar R. Randive, Sukumar Pati)....Pages 1679-1689
Energy Efficiency Analysis of a Building Envelope (M. Y. Khan, A. Baqi, A. Talib)....Pages 1691-1702

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Springer Proceedings in Energy

Manaswita Bose Anish Modi   Editors

Proceedings of the 7th International Conference on Advances in Energy Research

Springer Proceedings in Energy

The series Springer Proceedings in Energy covers a broad range of multidisciplinary subjects in those research fields closely related to present and future forms of energy as a resource for human societies. Typically based on material presented at conferences, workshops and similar scientific meetings, volumes published in this series will constitute comprehensive state-of-the-art references on energy-related science and technology studies. The subjects of these conferences will fall typically within these broad categories: – – – – – – –

Energy Efficiency Fossil Fuels Nuclear Energy Policy, Economics, Management & Transport Renewable and Green Energy Systems, Storage and Harvesting Materials for Energy

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Manaswita Bose Anish Modi •

Editors

Proceedings of the 7th International Conference on Advances in Energy Research

123

Editors Manaswita Bose Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai, Maharashtra, India

Anish Modi Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai, Maharashtra, India

ISSN 2352-2534 ISSN 2352-2542 (electronic) Springer Proceedings in Energy ISBN 978-981-15-5954-9 ISBN 978-981-15-5955-6 (eBook) https://doi.org/10.1007/978-981-15-5955-6 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The 7th International Conference on Advances in Energy Research (ICAER 2019) was organized by the Department of Energy Science and Engineering, Indian Institute of Technology Bombay between 10 and 12 December 2019 in Mumbai, India. The conference received around 350 submissions. Of these, around 165 submissions were accepted for oral presentation and 25 submissions were accepted for poster presentations after a rigorous peer review. The conference was attended by over 450 participants. This book is a compendium of selected papers presented at the conference. Two pre-conference workshops, the International Workshop on Hydrogen Storage and the Springer Author Workshop on Scientific Writing for Journals, were also organized on 09 December 2019. The conference had ONGC as the title sponsor, Coal India Limited as the gold sponsor, Department of Science and Technology (DST-SERB) as sponsor, Springer as best paper award sponsors, and ARCI, TCI Chemicals, NCPRE, and Energy Swaraj Foundation as exhibition stall partners. Prof. Ajit Kolar, IIT Madras sponsored three best paper awards. The pre-conference workshops and the conference hosted 31 invited lectures and presentations by academics and industry personnel from all over the world. Two special sessions on ‘Future of coal research’ and ‘Industry innovations in energy’ were also organized. The conference took several unique steps to ensure long term sustainability. These included minimizing the use of single-use plastic, registration kits made of cloth and notebooks made of recycled paper by local self-help groups, and planting of 100 medicinal plants to offset the carbon footprint from the flights of the invited speakers. The conference concluded with a panel discussion on energy transitions and energy security. Mumbai, India

Manaswita Bose Anish Modi

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Contents

Determination of Steam Energy Factor for Wort Kettle as a Tool for Optimization of the Steam Energy . . . . . . . . . . . . . . . . . . . . . . . . . Shripad Kulkarni and Alex Bernard CMG-Based Simulation Study of Water Flooding of Petroleum Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pratiksha D. Khurpade, Somnath Nandi, Pradeep B. Jadhav, and Lalit K. Kshirsagar Exergy-Based Comparison of Two Gas Turbine Plants with Naphtha and Naphtha-RFG Mixture as Fuels . . . . . . . . . . . . . . . . . . . . . . . . . . Sankalp Arpit, Sagar Saren, Prasanta Kumar Das, and Sukanta Kumar Dash Decentralized Solid Waste Management for EducationalCum-Residential Campus: A Pilot Study . . . . . . . . . . . . . . . . . . . . . . . Deepak Singh Baghel and Yogesh Bafna Does the Criteria of Instability Thresholds During Density Wave Oscillations Need to Be Redefined? . . . . . . . . . . . . . . . . . . . . . . . . . . . Subhanker Paul, Suparna Paul, Maria Fernandino, and Carlos Alberto Dorao

1

15

25

35

45

Solar Energy for Meeting Service Hot Water Demand in Hotels: Potential and Economic Feasibility in India . . . . . . . . . . . . . . . . . . . . . Niranjan Rao Deevela and Tara C. Kandpal

55

Techno-economic Feasibility of Condenser Cooling Options for Solar Thermal Power Plants in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tarun Kumar Aseri, Chandan Sharma, and Tara C. Kandpal

71

Optical Modeling of Parabolic Trough Solar Collector . . . . . . . . . . . . Anish Malan and K. Ravi Kumar

81

vii

viii

Contents

Cooling Energy-Saving Potential of Naturally Ventilated Interior Design in Low-Income Tenement Unit . . . . . . . . . . . . . . . . . . . . . . . . . Ahana Sarkar and Ronita Bardhan Development of an Improved Cookstove: An Experimental Study . . . . Himanshu, S. K. Tyagi, and Sanjeev Jain

91 103

Impact of Demand Response Implementation in India with Focus on Analysis of Consumer Baseline Load . . . . . . . . . . . . . . . . . . . . . . . Jayesh Priolkar and E. S. Sreeraj

111

Double Dielectric Barrier Discharge-Assisted Conversion of Biogas to Synthesis Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bharathi Raja, R. Sarathi, and Ravikrishnan Vinu

123

Thermo-Hydrodynamic Modeling of Direct Steam Generation in Parabolic Trough Solar Collector . . . . . . . . . . . . . . . . . . . . . . . . . . Ram Kumar Pal and K. Ravi Kumar

131

Hydrodeoxygenation of Bio-Oil from Fast Pyrolysis of Pinewood Over Various Catalysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kavimonica Venkatesan, Parasuraman Selvam, and Ravikrishnan Vinu

141

Simulation of Horizontal Axis Wind Turbine Using NREL FAST Solver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asmelash Haftu Amaha, Prabhu Ramachandran, and Shivasubramanian Gopalakrishnan Do Energy Policies with Disclosure Requirement Improve Firms’ Energy Management? Evidence from Indian Metal Sector . . . . . . . . . Mousami Prasad Power Management of Non-conventional Energy Resources-Based DC Microgrid Supported by Hybrid Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaynendra Kumar, Anshul Agarwal, and Nitin Singh

149

159

169

Sizing of a Solar-Powered Adsorption Cooling System for Comfort Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sai Yagnamurthy, Dibakar Rakshit, and Sanjeev Jain

181

Experimental Evaluation of Common Rail Direct Injection Compression Ignition Engine with EGR Using Biodiesel . . . . . . . . . . . Suresh D. Mane and Chinna Bathulla

191

Emission Measurement Considerations for Power Industry . . . . . . . . . A. Bekal, S. K. Karthick, Y. Rajeshirke, G. Balasubramaniam, M. Upadhyay, S. Bhandarkar, D. Kuvalekar, and C. Mitra

201

Contents

Impact of Growing Share of Renewable Energy Sources on Locational Marginal Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leena Heistrene, Yash Shukla, Yaman Kalra, Poonam Mishra, and Makarand Lokhande Performance Evaluation of Wind-Solar Hybrid System in Indian Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rahul Shityalkar, Ranjan Dey, Anagha Pathak, Niranjan Kurhe, and Sandesh Jadkar

ix

211

223

Structural, Electrical and Cell Performance Study on Lithium Germanium Phosphate Glass Ceramics-Based Solid-State Li-Electrolyte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anurup Das, Madhumita Goswami, P. Preetham, S. K. Deshpande, Sagar Mitra, and M. Krishnan

231

Adaptive Relaying Scheme for a Distribution Network with Highly Penetrated Inverter Based Distributed Generations . . . . . . . . . . . . . . . Kirti Gupta and Saumendra Sarangi

241

Optimization in the Operation of Cabinet-Type Solar Dryer for Industrial Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vishal D. Chaudhari, Govind N. Kulkarni, and C. M. Sewatkar

253

Modeling of Solar Photovoltaic-Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell to Charge Nissan Leaf Battery of Lithium Ion Type of Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . Kamaljyoti Talukdar Performance Study of an Anode Flow Field Design Used in PEMFC Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. A. Yogesha, Prakash C. Ghosh, and Raja Munusamy Effect of Top Losses and Imperfect Regeneration on Power Output and Thermal Efficiency of a Solar Low Delta-T Stirling Engine . . . . . Siddharth Ramachandran, Naveen Kumar, and Mallina Venkata Timmaraju Investigations on Recovery of Apparent Viscosity of Crude Oil After Magnetic Fluid Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. D. Kulkarni and K. S. Wani Investigation on Different Types of Electric Storage Batteries Used in Off-grid Solar Power Plants and Procedures for Their Performance Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . Anindita Roy, Rajarshi Sen, and Rupesh Shete Saving Electricity, One Consumer at a Time . . . . . . . . . . . . . . . . . . . . K. Ravichandran, Sumathy Krishnan, Santhosh Cibi, and Sumedha Malaviya

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285

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305 317

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Contents

Study of Effects of Water Inlet Temperature and Flow Rate on the Performance of Rotating Packed Bed . . . . . . . . . . . . . . . . . . . . Saurabh and D. S. Murthy Integrated Thermal Analysis of an All-Electric Vehicle . . . . . . . . . . . . Vinayak Kulkarni and Shankar Krishnan Computation of Higher Eigenmodes Using Subspace Iteration Scheme and Its Application to Flux Mapping System of AHWR . . . . . B. Anupreethi, Anurag Gupta, Umasankari Kannan, and Akhilanand Pati Tiwari

327 339

349

ESCO Model for Energy-Efficient Pump Installation Scheme: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saurabh Khobaragade, Priyanka Bhosale, and Priya Jadhav

359

Transient Numerical Model for Natural Convection Flow in Flat Plate Solar Collector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nagesh B. Balam, Tabish Alam, and Akhilesh Gupta

371

Rice Paddy as a Source of Sustainable Energy in India . . . . . . . . . . . . Mohnish Borker and T. V. Suchithra Cost and Emission Trade-Offs in Electricity Supply for the State of Maharashtra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pankaj Kumar, Trupti Mishra, and Rangan Banerjee Technological Interventions in Sun Drying of Grapes in Tropical Climate for Enhanced and Hygienic Drying . . . . . . . . . . . . . . . . . . . . . Mallikarjun Pujari, P. G. Tewari, M. B. Gorawar, Ajitkumar P. Madival, Rakesh Tapaskar, V. G. Balikai, and P. P. Revankar Effect of Temperature on the Hydrodynamics of Steam Reactor in a Chemical Looping Reforming System . . . . . . . . . . . . . . . . . . . . . . Agnideep Baidya, Saptashwa Biswas, Avinash Singh, Debodipta Moitra, Pooja Chaubdar, and Atal Bihari Harichandan Enhancement in Product Value of Potato Through Chemical Pre-treatment and Drying Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. B. Gorawar, S. V. Desai, V. G. Balikai, and P. P. Revankar Desalination Using Waste Heat Recovery with Active Solar Still . . . . . Sandeep Kumar Singh, S. K. Tyagi, and S. C. Kaushik

383

393

403

417

427 439

Incorporating Battery Degradation in Stand-alone PV Microgrid with Hybrid Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ammu Susanna Jacob, Rangan Banerjee, and Prakash C. Ghosh

449

Simulation Studies on Design and Performance Evaluation of SAPV System for Domestic Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. R. Dhivyashree, M. B. Gorawar, V. G. Balikai, and P. P. Revankar

463

Contents

xi

Development of a Dynamic Battery Model and Estimation of Equivalent Electrical Circuit Parameters . . . . . . . . . . . . . . . . . . . . . Sourish Ganguly, Subhrasish Pal, and Ankur Bhattacharjee

481

A Novel Switched Inductor Switched Capacitor-Based Quasi-Switched-Boost Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Sriramalakshmi and Sreedevi V. T.

493

Investigation of Energy Performance of a High-Rise Residential Building in Kolkata Through Performance Levels of Energy Conservation Building Code, 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gunjan Kumar, Biswajit Thakur, and Sudipta De

505

Addressing Last Mile Electricity Distribution Problems: Study of Performance of SHGs in Odisha . . . . . . . . . . . . . . . . . . . . . . . . . . . Sneha Swami and Subodh Wagle

515

Transient Stability Analysis of Wind Integrated Power Network Using STATCOM and BESS Using DIgSILENT PowerFactory . . . . . . Neha Manjul and Mahiraj Singh Rawat

525

Experimental Investigation of Solar Drying Characteristics of Grapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. P. Komble, Govind N. Kulkarni, and C. M. Sewatkar

537

Feedback and Feedforward Control of Dual Active Bridge DC-DC Converter Using Generalized Average Modelling . . . . . . . . . . . . . . . . . Shipra Tiwari and Saumendra Sarangi

547

Performance Assessment and Parametric Study of Multiple Effect Evaporator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pranaynil Saikia, Soundaram Ramanathan, and Dibakar Rakshit

559

An Approach Towards Sustainable Energy Education in India . . . . . . Pankaj Kalita, Rabindra Kangsha Banik, Samar Das, and Dudul Das Simulation-Based Economic Optimization of Nuclear Renewable Hybrid Energy Systems with Reliability Constraints . . . . . . . . . . . . . . Saikrishna Nadella, Anil Antony, and N. K. Maheshwari Exergy Analysis and Cost Optimization of Solar Flat Pate Collector for a Two-Stage Absorption Refrigeration System with Water-Lithium Bromide as a Working Pair . . . . . . . . . . . . . . . . . Abhishek Verma, S. K. Tyagi, and S. C. Kaushik Characterizing the Helical Vortex Frequency of HAWT . . . . . . . . . . . Ojing Siram and Niranjan Sahoo Design and Development of Concentrated Solar Cooker with Parabolic Dish Concentrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . Susant Kumar Sahu, Natarajan Sendhil Kumar, and K. Arjun Singh

575

587

599 611

621

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Contents

Thermal and Electrical Performance Assessment of Elongated Compound Parabolic Concentrator . . . . . . . . . . . . . . . . . . . . . . . . . . . Chandan, Sumon Dey, V. Suresh, M. Iqbal, K. S. Reddy, and Bala Pesala Thermodynamic Analysis of a 500 MWe Coal-Fired Supercritical Thermal Power Plant Integrated with Molten Carbonate Fuel Cell (MCFC) at Flue Gas Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshini More, A. Pruthvi Deep, and Sujit Karmakar

633

645

Three-Dimensional Investigation on Energy Separation in a Ranque–Hilsch Vortex Tube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nilotpala Bej, Pooja Chaubdar, Anish Pandey, and K. P. Sinhamahapatra

655

Bamboo Plant Intellect Deeds Optimization Algorithm for Solving Optimal Reactive Power Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanagasabai Lenin

665

Actuator Fault Detection and Isolation for PEM Fuel Cell Systems Using Unknown Input Observers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vikash Sinha and Sharifuddin Mondal

673

Analysis of Heating and Cooling Energy Demand of School Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tshewang Lhendup, Samten Lhendup, and Hideaki Ohgaki

685

Thermodynamic Performance Analysis of Adsorption Cooling and Resorption Heating System Using Ammoniated Halide Salts . . . . . Rakesh Sharma, K. Sarath Babu, and E. Anil Kumar

695

Correlating Partial Shading and Operating Conditions to the Performance of PV Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Gairola, M. K. Sharma, and J. Bhattacharya

707

Engineering of O2 Electrodes by Surface Modification for Corrosion Resistance in Zinc–Air Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imran Karajagi, K. Ramya, Prakash C. Ghosh, A. Sarkar, and N. Rajalakshmi Energy Farming—A Green Solution for Indian Cement Industry . . . . Kapil Kukreja, Manoj Kumar Soni, B. N. Mohapatra, and Ashutosh Saxena

717

725

Energetic and Exergetic Performance Comparison of a Hybrid Solar Kalina Cycle at Solar and Solar Storage Mode of Operations . . . . . . . P. Bhuyan, P. Borah, and T. K. Gogoi

735

Assessment of Different Multiclass SVM Strategies for Fault Classification in a PV System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rahul Kumar Mandal and Paresh G. Kale

747

Contents

Performance Analysis of Double Glass Water Based Photovoltaic/ Thermal System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ajay Sharma, S. Vaishak, and Purnanand V. Bhale Modeling Polarization Losses in HTPEM Fuel Cells . . . . . . . . . . . . . . Vamsi Ambala, Anusree Unnikrishnan, N. Rajalakshmi, and Vinod M. Janardhanan

xiii

757 767

Effect of Diesel Injection Timings on the Nature of Cyclic Combustion Variations in a RCCI Engine . . . . . . . . . . . . . . . . . . . . . . Ajay Singh, Rakesh Kumar Maurya, and Mohit Raj Saxena

775

Investigating the Impact of Energy Use on Carbon Emissions: Evidence from a Non-parametric Panel Data Approach . . . . . . . . . . . Barsha Nibedita and Mohd Irfan

785

Studies on the Use of Thorium in PWR . . . . . . . . . . . . . . . . . . . . . . . . Devesh Raj and Umasankari Kannan Coaxial Thermal Probe for High-Frequency Periodic Response in an IC Engine Test Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anil Kumar Rout, Santosh Kumar Hotta, Niranjan Sahoo, Pankaj Kalita, and Vinayak Kulkarni

795

805

Effect of Injection Pressure on the Performance Characteristics of Double Cylinder Four-Stroke CI Engine Using Neem Bio-diesel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sushant S. Satputaley, Iheteshamhusain Jafri, Gauravkumar Bangare, and Rahul P. Kavishwar

815

Experimental Study of a Helical Coil Receiver Using Fresnel Lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sumit Sharma and Sandip K. Saha

825

Substrate-Assisted Electrosynthesis of Patterned Lamellar Type Indium Selenide (InSe) Layer for Photovoltaic Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. B. Bhalerao, S. B. Jambure, R. N. Bulakhe, S. S. Kahandal, S. D. Jagtap, A. G. Banpurkar, A. W. M. H. Ansari, Insik In, and C. D. Lokhande

837

Optimization of Injector Location on the Cylinder Head in a Direct Injection Spark Ignition Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Srinibas Tripathy, Sridhar Sahoo, and Dhananjay Kumar Srivastava

847

Automated Cleaning of PV Panels Using the Comparative Algorithm and Arduino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huzefa Lightwala, Dipesh Kumar, and Nidhi Mehta

857

xiv

Contents

Performance and Degradation Analysis of High-Efficiency SPV Modules Under Composite Climatic Condition . . . . . . . . . . . . . . . . . . Shubham Sanyal, Arpan Tewary, Rakesh Kumar, Birinchi Bora, Supriya Rai, Manander Bangar, and Sanjay Kumar Energy Literacy of University Graduate Students: A Multidimensional Assessment in Terms of Content Knowledge, Attitude and Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . Divya Chandrasenan, Jaison Mammen, and Vaisakh Yesodharan Waste-to-Energy: Issues, Challenges, and Opportunities for RDF Utilization in Indian Cement Industry . . . . . . . . . . . . . . . . . . . . . . . . . Prateek Sharma, Pratik N. Sheth, and B. N. Mohapatra Predict the Effect of Combustion Parameter on Performance and Combustion Characteristics of Small Single Cylinder Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. K. Dond and N. P. Gulhane Experimental Investigation of a Biogas-Fueled Diesel Engine at Different Biogas Flow Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Naseem Khayum, S. Anbarasu, and S. Murugan Characteristics of an Indigenously Developed 1 KW Vanadium Redox Flow Battery Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sreenivas Jayanti, Ravendra Gundlapalli, Raghuram Chetty, C. R. Jeevandoss, Kothandaraman Ramanujam, D. S. Monder, Raghunathan Rengaswamy, P. V. Suresh, K. S. Swarup, U. V. Varadaraju, Vasu Gollangi, and L. Satpathy

869

879

891

901

913

923

Dynamic Demand Response Through Decentralized Intelligent Control of Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. T. Arvind and Anoop R. Kulkarni

931

Transient Analysis of Pressurizer Steam Bleed Valves Stuck Open for 700 MWe PHWRs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deepraj Paul, S. Pahari, S. Hajela, and M. Singhal

945

Transient Analysis of Net Load Rejection for 700 MWe IPHWRs . . . . S. Phani Krishna, S. Pahari, S. Hajela, and M. Singhal

953

A Comparative Experimental Investigation of Improved Biomass Cookstoves for Higher Efficiency with Lower Emissions . . . . . . . . . . . Sandip Bhatta, Dhananjay Pratap, Nikhil Gakkhar, and J. P. S. Rajput

961

Assessment of Floating Solar Photovoltaic (FSPV) Potential in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashish Kumar, Ishan Purohit, and Tara C. Kandpal

973

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xv

Effect of Non-Revenue Water Reduction in the Life Cycle of Water–Energy Nexus: A Case Study in India . . . . . . . . . . . . . . . . . Rajhans Negi, Vipin Singh, and Munish K. Chandel

983

Policy Intervention for Promoting Effective Adaptation of Rooftop Solar PV Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sabreen Ahmed, C. Vijayakumar, and Arjun D. Shetty

991

Improved Dispatchability of Solar Photovoltaic System with Battery Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1003 Sheikh Suhail Mohammad and S. J. Iqbal Numerical Investigation of the Performance of Pump as Turbine with Back Cavity Filling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015 Rahul Gaji, Ashish Doshi, and Mukund Bade Mining Representative Load Profiles in Commercial Buildings . . . . . . 1025 Kakuli Mishra, Srinka Basu, and Ujjwal Maulik A Simplified Non-iterative Method for Extraction of Parameters of Photovoltaic Cell/Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037 Kumar Gaurav, Neha Kumari, S. K. Samdarshi, and A. S. Bhattacharyya Design, Analysis and Hardware Implementation of Modified Bipolar Solid-State Marx Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1049 Neelam S. Pinjari and S. Bindu Viability Study of Stand-Alone Hybrid Energy Systems for Telecom Base Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1061 M. Siva Subrahmanyam and E. Anil Kumar Effect of Temperature and Salt Concentration on the Properties of Electrolyte for Sodium-Ion Batteries . . . . . . . . . . . . . . . . . . . . . . . . 1071 Bharath Ravikumar, Surbhi Kumari, Mahesh Mynam, and Beena Rai Carbon Deposition on the Anode of a Solid Oxide Fuel Cell Fueled by Syngas—A Thermodynamic Analysis . . . . . . . . . . . . . . . . . . . . . . . 1083 N. Rakesh and S. Dasappa Numerical Study on CO2 Injection in Indian Geothermal Reservoirs Using COMSOL Multiphysics 5.2a . . . . . . . . . . . . . . . . . . . . . . . . . . . 1091 Nandlal Gupta and Manvendra Vashistha Modification in the Rotor of Savonius Turbine to Reduce Reverse Force on the Returning Blade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1103 J. Ramarajan and S. Jayavel Design and Fabrication of Grating-Based Filters for Micro-thermophotovoltaic Systems . . . . . . . . . . . . . . . . . . . . . . . . . 1113 M. V. N. Surendra Gupta, E. Ameen, Ananthanarayanan Veeraragavan, and Bala Pesala

xvi

Contents

A Systematic Investigation on Evaporation, Condensation and Production of Sustainable Water from Novel-Designed Tubular Solar Still . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 Mihir Lad, Nikunj Usadadia, Sagar Paneliya, Sakshum Khanna, Vishwakumar Bhavsar, Indrajit Mukhopadhyay, Devang Joshi, and Abhijit Ray Novel Design of PV Integrated Solar Still for Cogeneration of Power and Sustainable Water Using PVT Technology . . . . . . . . . . . . . . . . . . 1131 Nikunj Usadadia, Mihir Lad, Sagar Paneliya, Sakshum Khanna, Abhijit Ray, and Indrajit Mukhopadhyay Cellulose Nanocrystals Incorporated Proton Exchange Membranes for Fuel Cell Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145 Saleheen Bano, Asif Ali, Sauraj, and Yuvraj Singh Negi Study of the Effect of Biomass-Derived N-Self Doped Porous Carbon in Microbial Fuel Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155 Saswati Sarmah, Minakshi Gohain, and Dhanapati Deka Analysis of Nature-Inspired Spirals for Design of Solar Tree . . . . . . . 1165 Sumon Dey, Madan Kumar Lakshmanan, and Bala Pesala Effective Use of Existing Efficient Variable Frequency Drives (VFD) Technology for HVAC Systems—Consultative Research Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175 Rahul Raju Dusa, Atulkumar Auti, and Vijay Mohan Rachabhattuni Thermodynamic Analysis of a Combined Power and Cooling System Integrated with CO2 Capture Unit of a 500 MWe SupC Coal-Fired Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1185 Rajesh Kumar, Goutam Khankari, and Sujit Karmakar DFT Studies on Electronic and Optical Properties of Inorganic CsPbI3 Perovskite Absorber for Solar Cell Application . . . . . . . . . . . . 1199 Abhijeet Kale, Rajneesh Chaurasiya, and Ambesh Dixit Biowaste Derived Highly Porous Carbon for Energy Storage . . . . . . . 1207 Dinesh J. Ahirrao, Shreerang D. Datar, and Neetu Jha Bio-Ethanol Production from Carbohydrate-Rich Microalgal Biomass: Scenedesmus Obliquus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1215 Maskura Hasin, Minakshi Gohain, and Dhanapati Deka Safety Analysis of Loss of NPP Off-Site Power with Failure of Reactor SCRAM (ATWS) for VVER-1000 . . . . . . . . . . . . . . . . . . . 1225 Manish Mehta, Sanuj Chaudhary, Anirban Biswangri, P. Krishna Kumar, Y. K. Pandey, and Gautam Biswas

Contents

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P-type Crystalline Silicon Surface Passivation Using Silicon Oxynitride/SiN Stack for PERC Solar Cell Application . . . . . . . . . . . . 1237 Irfan M. Khorakiwala, Vikas Nandal, Pradeep Nair, and Aldrin Antony Pressure Propagation and Flow Restart in the Subsea Pipeline Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245 Lomesh Tikariha and Lalit Kumar Electrodeposition of Cu2O: Determination of Limiting Potential Towards Solar Water Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1255 Iqra Reyaz Hamdani and Ashok N. Bhaskarwar Design and Development of an Economical and Reliable Solar-Powered Trash Compactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1265 Ridhi Lakhotia, Abu Fazal, Ajay Yadav, and Ankur Bhattacharjee Performance and Emission Characteristics of CI Engine Fueled with Plastic Oil Blended with Jatropha Methyl Ester and Diesel . . . . . 1275 S. Babu, K. Kavin, and S. Niju Performance Analysis of Hybrid Photovoltaic Array Configurations Under Randomly Distributed Shading Patterns . . . . . . . . . . . . . . . . . . 1287 Vandana Jha Flow Improvement Aspect with Stagger Angle Variation of the Subsequent Rotor in Contra-rotating Axial Flow Turbine . . . . . 1297 Rayapati Subbarao Performance Assessment of Pelton Turbine with Traditional and Novel Hooped Runner by Experimental Investigation . . . . . . . . . . 1309 Vimal K. Patel, Hemal N. Lakdawala, Sureel Dohare, and Gaurang Chaudhary Evaluation of LVRT Control Strategies for Offshore Wind Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1319 M. M. Kabsha and Zakir Rather An Experimental and CFD Analysis on Heat Transfer and Fluid Flow Characteristics of a Tube Equipped with X-Shaped Tape Insert in a U-Shaped Heat Exchanger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1331 Sagar Paneliya, Sakshum Khanna, Jeet Mehta, Vishal Kathiriya, Umang Patel, Parth Prajapati, and Indrajit Mukhopdhyay Single-Particle Analysis of Thermally Thick Wood Particles in O2, N2, CO2 Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1349 Shruti Vikram and Sandeep Kumar An Analysis for Management of End-of-Life Solar PV in India . . . . . . 1361 Snehalata Pankadan, Swapnil Nikam, and Naqui Anwer

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Contents

Localized Energy Self-sufficiency (Energy Swaraj) for Energy Sustainability and Mitigating Climate Change . . . . . . . . . . . . . . . . . . . 1373 Chetan Singh Solanki, Sayli Shiradkar, Rohit Sharma, Jayendran Venkateswaran, Nikita Lihinar, Harshad Supal, and Swati Kalwar Pseudocapacitive Energy Storage in Copper Oxide and Hydroxide Nanostructures Casted Over Nickel-Foam . . . . . . . . . . . . . . . . . . . . . . 1383 Priyanka Marathey, Sakshum Khanna, Roma Patel, Indrajit Mukhopadhyay, and Abhijit Ray Validation of Computer Code Based on Nodal Integral Method Against KAPS-2 Phase-B Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1393 Manish Raj, Sherly Ray, A. S. Pradhan, and Suneet Singh Bipolar DC Micro-Grid Based Wind Energy Systems . . . . . . . . . . . . . 1403 Dodda Satish Reddy, Suman Kumar, Bonala Anil Kumar, and Sandepudi Srinivasa Rao Processing Thermogravimetric Analysis Data for Pyrolysis Kinetic Study of Microalgae Biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415 Pravin G. Suryawanshi and Vaibhav V. Goud Photovoltaic Thermal Collectors with Phase Change Material for Southeast of England . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1425 Preeti Singh, Rajvikram Madurai Elavarasan, Nallapaneni Manoj Kumar, Sourav Khanna, Victor Becerra, Sanjeev Newar, Vashi Sharma, Jovana Radulovic, Rinat Khusainov, and David Hutchinson Modeling and Simulation of Hollow Fiber Biocatalyst Membrane Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1431 Nooram Anjum, Mohammad Danish, and Sarah Anjum Efficient Alkaline Peroxide Pretreatment of Sterculia foetida Fruit Shells for Production of Reducing Sugar: Effect of Process Parameters on Lignin Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1441 S. Sardar, A. Das, S. Saha, and C. Mondal Performance Enhancement of Savonius Hydrokinetic Turbine with a Unique Vane Shape: An Experimental Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1453 Vimal K. Patel, Kushal Shah, and Vikram Rathod Techno-economic Analysis for Production of Biodiesel and Green Diesel from Microalgal Oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1465 Swarnalatha Mailaram, Nitesh Dobhal, and Sunil K. Maity Numerical Investigation on the Effect of EGR in a Premixed Natural Gas SI Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1477 Sridhar Sahoo, Srinibas Tripathy, and Dhananjay Kumar Srivastava

Contents

xix

Transitions in the Indian Electricity Sector: Impacts of High Renewable Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1489 Aishwarya V. Iyer and Rangan Banerjee Comparison of Physics Characteristics of Pressurized Water Reactor Type Advanced Light Water Reactors . . . . . . . . . . . . . . . . . . . . . . . . . 1501 L. Thilagam and D. K. Mohapatra Development of a Python Module “SARRA” for Refuelling Analysis of MSR Using DRAGON Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1513 A. K. Srivastava, Anurag Gupta, and Umasankari Kannan The Effect of Concentration Ratio and Number of P-N Thermocouples on Photovoltaic-Thermoelectric Hybrid Power Generation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1521 Abhishek Tiwari and Shruti Aggarwal Evaluation of Annual Electrical Energy Through Semitransparent (Glass to Glass) and Opaque Photovoltaic Module in Clear Sky Condition at Composite Climate: A Comparative Study . . . . . . . . . . . 1533 Rohit Tripathi, Nitin K. Gupta, Deepak Sharma, G. N. Tiwari, and T. S. Bhatti Current Practices and Emerging Trends in Safety Analysis of NPPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1543 K. Obaidurrahman Electrochemical Reduction of CO2 on Ionic Liquid Stabilized Reverse Pulse Electrodeposited Copper Oxides . . . . . . . . . . . . . . . . . . 1551 Nusrat Rashid and Pravin P. Ingole Performance of Flux Mapping System During Spatial Xenon Induced Oscillations in PHWRs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1559 Abhishek Chakraborty, M. P. S. Fernando, and A. S. Pradhan Forecasting of Electricity Demand and Renewable Energy Generation for Grid Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1571 Joel Titus, Urvi Shah, T. Siva Rama Sarma, Bhushan Jagyasi, Pallavi Gawade, Mamta Bhagwat, and Arnab De Platooning of Flat Solar-Panel-Mounted Mini Bus Model—A Numerical Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1583 Mohammad Rafiq B. Agrewale and R. S. Maurya Co-sensitization of Perovskite Solar Cells by Organometallic Compounds: Mechanism and Photovoltaic Characterization . . . . . . . . 1595 Nisha Balachandran, Temina Mary Robert, Dona Mathew, and Jobin Cyriac

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Contents

Nuclear Power Plants and Human Resources Development in South Asia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1603 Firoz Alam, Rashid Sarkar, Akshoy Ranjan Paul, and Abdulaziz Aldiab Highly Stable Pt/CVD-Graphene-Coated Superstrate Cu2O Photocathode for Water Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1615 Chandan Das, Akhilender Jeet Singh, and K. R. Balasubramaniam Thermodynamic Studies on Steel Slag Waste Heat Utilization for Generation of Synthesis Gas Using Coke Oven Gas (COG) as Feedstock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1623 M. Srinivasarao, Nilu Kumar, and A. Syamsundar Reactivity-Initiated Transients for 700 MWe PHWR . . . . . . . . . . . . . . 1633 Suresh Kandpal, M. P. S. Fernando, and A. S. Pradhan Multi-field Solar Thermal Power Plant with Linear Fresnel Reflector and Solar Power Tower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1645 Shridhar Karandikar, Irfan Shaikh, Anish Modi, Shireesh B. Kedare, and Balwant Bhasme Experimental Investigation Using Enriched Biogas in S-I Engine for Stable Rural Electrification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1657 Antony P. Pallan, Deepak Mathew, and Yohans Varghese Solar Autoclave for Rural Hospitals Using Aerogel as Transparent Insulation Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1667 Manoj Kumar Yadav, Anish Modi, and Shireesh B. Kedare Numerical Investigation on the Influence of Reactant Gas Concentration on the Performance of a PEM Fuel Cell . . . . . . . . . . . . 1679 Brajesh Kumar Kanchan, Pitambar R. Randive, and Sukumar Pati Energy Efficiency Analysis of a Building Envelope . . . . . . . . . . . . . . . 1691 M. Y. Khan, A. Baqi, and A. Talib

About the Editors

Manaswita Bose is an Associate Professor in the Department of Energy Science and Engineering at the Indian Institute of Technology Bombay, India, and has previously worked in Monash University (Australia), Reliance Industries (India), and Zeus Numerix Pvt Ltd (India). She has done her M.Sc. and Ph.D. from the Indian Institute of Science, Bangalore. Her research interests include the study of the flow of granular materials, solar thermal storage, and coal gasification. She has co-authored 23 research papers in reputed journals, proceedings, and published 1 book chapter and 25 papers in international conferences, having also filed 3 patents. Anish Modi is an Assistant Professor in the Department of Energy Science and Engineering at the Indian Institute of Technology Bombay, India and has previously worked at the Technical University of Denmark (DTU) and Alstom Projects India Limited. He pursued his M.Sc. from the Royal Institute of Technology (KTH), Sweden and Polytechnic University of Catalonia (UPC), Spain and his Ph.D. from DTU, Denmark. His research interests include thermal energy systems, solar thermal energy, concentrating solar power, polygeneration, and energy sustainability, and he has published 19 papers in reputed journals and international conferences.

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Determination of Steam Energy Factor for Wort Kettle as a Tool for Optimization of the Steam Energy Shripad Kulkarni and Alex Bernard

Nomenclature SSC WK DP V.L. H.R.L. C.L. F.L. B.E. HL ML S.E.

Specific steam consumption Wort kettle Degree Plato Vapor loss Heat recovery loss Condensate loss Flash loss Losses in brew energy Hectoliter Machine learning Losses through steam pressure variation

1 Introduction Stringent energy conservation laws have been driving new ways and methods to reduce energy demand either by developing energy efficient systems or by reducing the total energy consumption directly. A significant portion of the world’s energy requirement occurs in the form of steam. Steam is used in almost every industry ranging from manufacturing to process industries at different capacities and conditions. The steam requirement at different sectors may vary with regards to application S. Kulkarni · A. Bernard (B) Department of Research and Development, Forbes Marshall Pvt. Ltd, Pune, Maharashtra 411034, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_1

1

2

S. Kulkarni and A. Bernard

and capacity. Thus, steam distribution systems lack a generic structure which would have helped in optimizing steam networks. Process industries such as Food and Beverages (F&B) generally use substantial amounts of steam for their varied applications and often incorporate complex steam systems to meet their requirements which are both dynamic and highly critical. This makes the estimation of actual steam requirement a very arduous process [1]. The brewing industry is one of the fastest growing and also one of the most energy intensive sectors in the world. A typical brewing plant in India consumes more than 200 tons of steam each operational day. The Wort kettle within a Brewhouse consumes about 30% of the overall steam production in the plant. Thus, the steam saving potential for a Brewhouse can be quite substantial through improved evaluation methods for steam distribution networks. Energy and exergy analysis of the brewing industries has helped many energy auditors in understanding the various inefficiencies within the steam distribution system itself [2]. EINSTEIN is a thermal auditing tool that has been used to some extent in breweries [3]. The most common metric used in the present scenario for evaluating the steam distribution system is the SSC or the specific steam consumption. It is the energy consumption analysis with regards to the production data. It has so far been the basis of benchmarking in the brewing industry [4]. But it does not indicate the efficiency of the steam distribution network itself. This further inspired many other steam enthusiasts to come up with different strategies to evaluate steam consumption based on critical parameters such as enthalpy, fuel, work load [5]. Another method to evaluate steam consumption was to do a complete process modeling of the brewing facility. This allows to have a holistic view of the energy balance of the Brewhouse which would further help in analyzing the energy load of the facility as a whole [6]. Though all these methods brief about the actual steam requirement, the lack of coherence with the realistic scenario deduces to a lower practicality when it comes to saving steam. It becomes hard to trace the losses when systems exhibit high degree of complexity and flexibility. Not to forget the seasonal variation that occurs over time. This paper assesses steam energy requirement for a Wort kettle which forms the soul of the Brewhouse. The steam energy factor (SEF) is a proven metric for evaluation of steam consumption for various F&B industries and steam-intensive equipment. The following paper is an approach to determine the steam energy factor using a model based on losses and critical parameters assisted by machine learning. The list of all the critical parameters that were used to derive the SEF have been listed in Table 1.

2 Steam Consumption Steam is primarily used for two processes in the Brewhouse: i. Preheating

Determination of Steam Energy Factor for Wort Kettle …

3

Table 1 Process parameters Inlet wort volume

HL

Heat load of the water evaporated

KCal

Outlet wort volume

HL

Radiation Loss

KCal

Sweet wort percentage

%

Radiation loss of evaporated water KCal

Sparge volume percentage

%

Latent heat of steam at stipulated pressure

KCal/Kg

Amount of water evaporated

Kg

Total load

KCal

Pressure range

BarG

Steam consumption

Kg/Batch

Wort Temperature after mash filter

°C

Actual steam consumption (Flow meter)

Kg/Batch

Wort Temperature after whirlpool °C PHE

SSC (Steam required per Kg of product)

Kg/Kg

Heat recovery pump raises temp to

°C

Latent heat of Vaporization at pressure

KCal/Kg

Boiling Temperature

°C

Saturation Temperature at pressure

°C

Boiling time

mins

Energy in

KCal

Degree Plato inlet of WK

°P

Energy out

KCal

Degree Plato outlet of WK/

°P

Lasses

KCal

Difference in inlet DP and outlet DP

°P

Total Condensate Loss

KCal

Overall heat transfer coefficient

W/m2 K

Flash loss

KCal

Ambient temperature

°C

Condensate loss

KCal

Insulation efficiency

Unit

HRS gain

KCal

Area of Wort kettle

m2

Loss when HRS is off

KCal

Specific gravity of wort at inlet flow meter

Kg/m3

Evaporation loss

KCal

Specific gravity of wort at outlet flow meter

Kg/m3

Brew energy

KCal

Contraction factor

Unit

UAL

KCal

Specific heat of wort

KCal/KgK

Indirect efficiency

Unit

Heat load

KCal

Direct efficiency

Unit

ii. Evaporation The amount of water evaporated and the preheating time is purely an aspect of the recipe and will vary from one industry to another. Even though steam requirement could be fairly estimated only by monitoring these two parameters, it was found that there were various other factors which interact with the system that also affect the overall steam consumption. Hence, it became evident to track all the factors to analyze the impact each of these factors have on steam consumption. There are many methods that have been proposed in the past to estimate steam consumption such as enthalpy-based

4

S. Kulkarni and A. Bernard

steam consumption [4] and equivalent steam requirement through fuel consumption [1]. Both the approaches though very commendable, lack in capturing the steam requirement inherited by the dynamic nature of process itself. The process of increasing the Degree Plato of the cold wort entering the Wort kettle to the required set point as per recipe is done by evaporating the moisture content within the product. There are multiple factors that affect this process. The list of all the factors taken into account for developing steam energy factors have been listed below.

3 Process Flow Figure 1 represents the schematic of the flow of sweet wort after the mash filter and before the whirlpool cooler for different batch sizes. The inlet wort, before entering the Wort kettle, is preheated using a heat recovery system. The estimation for maximum possible efficiency has been calculated. The value of Degree Plato (°P) is set according to the recipe. All the accountable losses have been calculated while the other are taken as unavailable energy losses (UAL). Most of the parameters required for determining the steam energy factor are already monitored by the brewer. And likewise, the brewer also has control over all of these parameters. SEF helps the brewer understand the interaction of all these parameters with the equipment and the process variation and thus giving him a better insight regarding the system performance and the impact each of these parameters have on the overall steam consumption.

Fig. 1 Schematic of Wort kettle section

Determination of Steam Energy Factor for Wort Kettle …

5

4 Development of SEF The methodology used for the determination of steam energy factor is a crucial step in its development and its usefulness. It shall not only brief the brewer about his excess steam consumption but also help him negate the various losses that are identified using SEF for the process. The development of this metric was addressed by two methods out of which one was used for a very renowned brewing industry in Asia.

4.1 Method 1 In the first method, the steam energy factor is calculated directly by dividing the actual steam consumption with the theoretical steam consumption. The theoretical steam consumption is estimated by developing thermodynamic relations of the process variables with the steam load demand. In simple terms, this model theoretically evaluates the steam demand for the process and then compares it with the actual steam consumed for the same process measured through steam flow meters. SEF(model 1) =

Actual steam consumption Theoretical steam consumption

(1)

4.2 Method 2 Method 2, though it might be familiar to method 1, it is a fairly different approach taken to determine the steam energy factor. The steam energy factor in this case accounts for all the losses that occur during a process. In this approach, all the process parameters are monitored continuously for a certain period. This is followed by identifying the parameters which have a significant impact on steam consumption. This further helps in estimating all the losses that occur due to process variation. And all of this is captured by SEF. SEF(model 2) =

1 100 − Losses(%)

(2)

The losses that were included for estimating the SEF for Wort kettle have been listed in Fig. 2. The following relations were used to determine the losses: V.L. H.R.L. C.L. F.L.

f (E.R., Sweet wort %, sparge volume %, inlet DP) f (HRS temperature, scaling, choking) f (heat carried out by the hot condensate) f (Condensate flashing into vapors)

6

S. Kulkarni and A. Bernard

Fig. 2 Energy distribution in Wort kettle

B.E. S.E.

f (Inlet temperature of cold wort) f (Steam pressure variation)

This model, though not as effective as the former one in terms of the accuracy in estimating the losses, still manages to brief all the possible reductions that could be incorporated to reduce the overall steam consumption by identifying them. Machine learning algorithms were used for determining the specific gravity of wort at inlet and outlet. Twenty batches were used for training the model.

5 Optimization with SEF The ideal value of SEF is 1, and hence, the best value of SEF is when there is minimal loss. Most of the losses occurred because of the variations in the actual process. Initial study depicted the variations in consecutive batches during the same shift. This was reflected by a rise in the overall steam consumption. Figure 2 is an empirical energy distribution diagram flow a Wort kettle. There are multiple factors that give rise to a high evaporation ratio. This could be a result of a low mashing efficiency, a higher percentage of Sparge volume, the quality of malt and other ingredients, etc. The heat recovery system (HRS) also plays an important role in the reduction of steam consumption. The steam pressure, trap leakages, ambient conditions, poor insulation of steam lines are some of the other factors which are categorized under site conditions also affect the overall steam consumption.

Determination of Steam Energy Factor for Wort Kettle …

7

6 Results and Discussion The initial study was done to validate the difference between specific steam consumption and steam energy factor. Specific steam consumption or SSC is total steam consumed per unit quantity of product. SSC is a true reflection of the overall steam consumption, but it fails to capture the variations occurring in the process which account for the marginal excess steam consumption (Fig. 3). The readings were taken for 400 HL batches. For batch 5, the SSC is not a true indication of the excess steam consumption. It is indicated by a low SSC value of 18.45 against a high SEF value of 1.821. This helps the brewer identify the quality of batch 5 as it accounts for a higher steam energy loss. The following are the results obtained through SEF for an international beer production organization.

6.1 Before The data recorded for the brewing process before the implementation of the SEF indicated a high steam consumption when compared to the actual process demand. Wort kettle running at sweet wort composition of less than 34% having an SEF of 1.76 accounted for 27% evaporation loss, 8.2% condensate loss, 2.4% flash loss, 1.3% heat recovery loss and 6.4% miscellaneous loss (Figs. 4, 5, 6, 7, 8, 9 and 10).

6.2 After Through SEF, an efficient control strategy was applied. The data was collected again after 7 months, and the results are as follows in Figs. 11, 12, 13, 14, 15, 16 and 17. With the reduction in all these factors, the SEF was brought down. It was seen that steam consumption per batch reduced by more than 600 kg. For the brewing

Fig. 3 SEF versus SSC

8 Fig. 4 Initial SEF

Fig. 5 Evaporation ratio

Fig. 6 Inlet degree plato

S. Kulkarni and A. Bernard

Determination of Steam Energy Factor for Wort Kettle … Fig. 7 Sweet wort percentage

Fig. 8 Sparge volume percentage

Fig. 9 Temperature of heat recovery system

9

10 Fig. 10 Overall steam consumption

Fig. 11 Final SEF

Fig. 12 Final evaporation ratio

S. Kulkarni and A. Bernard

Determination of Steam Energy Factor for Wort Kettle … Fig. 13 Inlet degree Plato

Fig. 14 Sweet wort percentage

Fig. 15 Sparge volume percentage

11

12

S. Kulkarni and A. Bernard

Fig. 16 Temperature of heat recovery system

Fig. 17 Overall steam consumption per batch

industry that produces as much as 8 batches per day, the total savings would come to nearly 1.65 megatons per year. It is still possible to raise the HRS temperature to more than 90 °C. As much as 500 kg per batch of steam consumption could be further reduced through an optimized control strategy.

7 Conclusion An optimized tool for steam evaluation helps in understanding the various losses that occur within a process. Identification of these losses helps in determining the right control strategy for reduced steam consumption. SEF as a metric identifies these losses and also helps in benchmarking the best possible performance of the steam

Determination of Steam Energy Factor for Wort Kettle …

13

equipment. The steam savings generated through SEF for one brewing plant is close to 5TPD. There are about 55 brewing plants in India alone. The potential for overall energy savings is enormous. SEF as an improved methodology for evaluating steam consumption can be extended for other steam intensive equipments and processes. Our country relies on steam as a major source of mobilized heat energy. Tools for evaluating the steam distribution systems will bridge the gap between the theoretical steam demand and the actual steam consumed and they get better with more parameters and improved models.

References 1. Zhu, X.X.: Energy and Process Optimization for the Process Industries. Wiley, Hoboken, NJ (2014) 2. Fadare, D.A., Nkpubre, D.O., Oni, A.O., Falana, A., Waheed, M.A., Bamiro, O.A.: Energy and exergy analyses of malt drink production in Nigeria. Energy 35, 5336–5346 (2010). https://doi. org/10.1016/j.energy.2010.07.026 3. Schweiger, H. et al.: Guide for EINSTEIN Thermal Energy Audits, Barcelona, Spain (2012) 4. Energy Saver Tool, Campden BRI, Surrey, UK. Available from http://www.campdenbri.co.uk/ services/brewing-energy-saver.php 5. Fuller, D.A.: Alternative scale measures and the behavior of average costs in steam electric generation. Energy Econ. 13(1), 61–68 (1991). https://doi.org/10.1016/0140-9883(91)90057-7 6. Muster-Slawitscha, B., Hubmanna, M., Murkovic, M., Brunner, C.: Process modelling and technology evaluation in brewing (2014). https://doi.org/10.1016/j.cep.2014.03.010

CMG-Based Simulation Study of Water Flooding of Petroleum Reservoir Pratiksha D. Khurpade , Somnath Nandi , Pradeep B. Jadhav , and Lalit K. Kshirsagar

List of Symbols K z S Rs Bo μ MSTB P

Permeability (mD) Grid thickness (ft.) Phase saturation [Dimensionless (–)] Solution gas oil ratio (ft3 /bbl.) The formation factor (bbl./ft3 ) Viscosity (centipoise) Thousand stock tank barrels (MSTB) Pressure (psi)

Subscripts w o g c r x, y, z

Water Oil Gas Capillary Relative Directions

P. D. Khurpade · P. B. Jadhav · L. K. Kshirsagar Department of Petroleum and Petrochemical Engineering, Maharashtra Institute of Technology, Paud Road, Pune 411038, India e-mail: [email protected] S. Nandi (B) Department of Technology, Savitribai Phule Pune University, Ganeshkhind, Pune 411007, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_2

15

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P. D. Khurpade et al.

1 Introduction The increasing global demand for oil and gas and falling prices of crude oil, it is necessary to increase the productivity of hydrocarbons as efficiently and economically as possible. As most of the existing oil fields are approaching a mature stage, in turn, needs the secondary recovery methods to increase production from these fields [1]. Waterflooding is the oldest and widely used secondary recovery method in these reservoirs because of easy water availability, ease of injection and it is less expensive. Basically, waterflooding operation involves the injection of water into the reservoir to displace the oil through porous medium [2]. Successful waterflooding applications recover more oil than the primary recovery stage. However, percentage of water cut from many of the petroleum reservoirs become significantly high, and hence sometimes the process may become uneconomical. Common reasons which include channeling of water which by-passed most of the oil, low permeability which limits the injection rate of water into the reservoir and higher cost of infrastructure for deeper or offshore located reservoirs [3]. Various methods to improve the performance of waterflooding operation to get the maximum productivity of oil and minimum water production are reported in the literature. One of the methods for extenuating this problem is by employing a different combination of horizontal and/or vertical wells in waterflooding projects [4]. The concept of horizontal injector/producer well for waterflooding project was first introduced by Taber and Seright 1992 in East Texas [5]. They have reported the potential benefits of combination of horizontal injection and production wells for waterflooding. Their results showed that horizontal wells for injection and production in opposite direction have higher injectivity and sweep efficiency as compared to five-spot conventional patterns [5]. The main advantage of horizontal well is the larger reservoir contact area of a well than a vertical well which depends on the well length. Hence the productivity of horizontal well increases with an increase in well length by enhancing the reservoir contact area. However, the major drawback of horizontal wells is the well cost which is 1.4–3 times more than that of a vertical well. Published literature of horizontal well showed that multi-well rather than single well is preferred for economic benefits [6]. Broman et al. 1990 have shown that reduction in the cost of drilling and completion of 16 horizontal wells in Purdhoe Bay, Alaska in the initial phase and then remained constant over the last two years [7]. Water coning and gas coning are severe problems in petroleum reservoir as it affects oil productivity. The main reason for coning is pressure drawdown near the wellbore. The coning problems can be reduced by minimizing pressure drawdown which is achieved by drilling long horizontal well. It also enhances the production rates due to increases in contact area [6]. Popa et al. 1998 have proposed that the horizontal injector and producer well in toe to heel combination enhance better sweep efficiency. They have also pointed out that vertical injection well along with horizontal producer well have better flow distribution than horizontal injector [8].

CMG-Based Simulation Study of Water Flooding …

17

Reservoir simulation utilizes mathematical model of the reservoir and simulates numerically in order to draw important decisions like selection of best recovery method for reservoir, maximization of the economic recovery of hydrocarbons, sensitivity of model parameters, etc. Numerical simulation tool aids to predict the production performance like hydrocarbon recovery at low expenses and in short period of time [9]. In this study, CMG-IMEX (Computer Modeling Group) commercial reservoir simulator has been used to study the effect of various combinations of well pattern horizontal or vertical, its location, and length on the productivity of oil of waterflooding process.

2 Modeling and Simulation The main objective of this study is to analyze the performance of a waterflooding process which is influenced by the flooding pattern and well length. CMG-IMEX (Computer Modeling Group) commercial reservoir simulator has been used to build reservoir model in three dimensions [10]. IMEX numerical simulator that uses IMES (finite difference implicit pressure-explicit saturation) is the CMG’s threephase black oil model. IMEX is used to model primary and secondary oil recovery processes in conventional reservoirs. The problem under study and the data used in the simulation model were obtained from published literature [3, 5]. The porosity of the reservoir in their study is 0.2. Reservoir data are presented in Table 1. Relative permeability and capillary pressure data are reported in Table 2, and the fluid property data are given in Table 3. Our reservoir model is represented by 9 × 9 × 6 Cartesian grid system. The grid block dimensions in horizontal direction (x and y direction) are shown in Fig. 1 and reservoir thickness in vertical direction (z-direction) are reported in Table 1. One injection and one production well are provided in the reservoir model. Nine different flood patterns were considered in order to achieve maximum oil recovery: base case and first four cases are with parallel horizontal injection and production well. The injection well was located at the bottom layer of the reservoir where water saturation is highest and production layer was located at the top layer Table 1 Reservoir data taken from Nghiem et al. [3] Layer

Cent. of layer (ft.)

Δz (ft.)

K x and K y (mD)

K z (mD)

Poil (psi)

S o (–)

1 (top)

3600

20

300

30

3600

0.711

2

3620

20

300

30

3608

0.652

3

3640

20

300

30

3616

0.527

4

3660

20

300

30

3623

0.351

5

3685

30

300

30

3633

0.131

6 (bottom)

3725

50

300

30

3650

0.000

18

P. D. Khurpade et al.

Table 2 Relative permeability and capillary pressure data taken from Nghiem et al. [3] Sw

K rw

K row

Pcow

Sg

K rg

K rog

Pcog

0.22

0.00

1.0000

6.30

0.00

0.000

1.00

0.0

0.30

0.07

0.4000

3.60

0.04

0.000

0.60

0.2

0.40

0.15

0.1250

2.70

0.10

0.022

0.33

0.5

0.50

0.24

0.0649

2.25

0.20

0.100

0.10

1.0

0.60

0.33

0.0048

1.80

0.30

0.240

0.02

1.5

0.80

0.65

0.0000

0.90

0.40

0.340

0.00

2.0

0.90

0.83

0.0000

0.45

0.50

0.420

0.00

2.5

1.00

1.00

0.0000

0.00

0.60

0.500

0.00

3.0

0.70

0.812

0.00

3.5

0.78

1.000

0.00

3.9

Table 3 Fluid property data taken from Nghiem et al. 1991 [3] P (psi)

Rs (ft3 /bbl.)

Bo (bbl./ft3 )

Bg (bbl./ft3 )

μo (cp)

μg (cp)

400

165

1.0120

0.0059

1.17

0.0130

800

335

1.0255

0.00295

1.14

0.0135

1200

500

1.0380

0.00196

1.11

0.0140

1600

665

1.0510

0.00147

1.08

0.0145

2000

828

1.0630

0.00118

1.06

0.0150

2400

985

1.0750

0.00098

1.03

0.0155

2800

1130

1.0870

0.00084

1.00

0.0160

3200

1270

1.0985

0.00074

0.98

0.0165

3600

1390

1.110

0.00065

0.95

0.0170

4000

1500

1.120

0.00059

0.94

0.0175

4400

1600

1.130

0.00054

0.92

0.0180

4800

1676

1.140

0.00049

0.91

0.0185

5200

1750

1.148

0.00045

0.90

0.0190

5600

1810

1.155

0.00042

0.89

0.0195

Description

Values

Oil density and gas lb/ft.3 Water density

lb/ft.3

45, 0.0702 62.14

Oil compressibility for under-saturated oil

1 × 10−5 1/psi

Water compressibility

3 × 10−6 1/psi

Rock compressibility

4 × 10−6 1/psi

Water viscosity cp

0.96

CMG-Based Simulation Study of Water Flooding …

19

Fig. 1 Grid block dimension considered for the simulation study adapted from Nghiem et al. [3]

of the reservoir where saturation of oil is highest. The length of an injection well has been considered in decreasing order from 2700 to 600 ft. for these cases, keeping the length of production well constant, i.e., 900 ft. The length of production well was from 600 to 1800 ft. in stepwise manner with 900 ft. long injection well in cases 5, 6, and 7. The last two simulation cases were performed with vertical injection well close to the reservoir boundary and horizontal production well at the top layer of the reservoir and vice versa. The details of all the cases are as follows: • Base Case: Horizontal Injection well drilled in bottom layer (6) and completed with length 2700 ft.: (I, 5, 6), I = 1, 2, …, 9. Horizontal production well drilled in top layer (1) and completed with length 900 ft.: (I, 5, 1), I = 6, 7, 8. • Cases 1, 2, 3, and 4: Horizontal Injection well drilled in bottom layer (6) and completed with the lengths of (i) 1800 ft.: (I, 5, 6), I = 1, 2, 3, 4, 5, 6. (ii) 1200 ft.: (I, 5, 6), I = 1, 2, 3, 4. (iii) 900 ft.: (I, 5, 6), I = 1, 2, 3. (iv) 600 ft.: (I, 5, 6), I = 1, 2. Horizontal production well: same as case 1. • Case 5, 6, and 7: Horizontal Injection well drilled in bottom layer (6) and completed with the lengths 900 ft.: (I, 5, 6), I = 1, 2, 3. Horizontal production well drilled in top layer (1) and completed with the lengths (v) 600 ft.: (I, 5, 1), I = 7, 8. (vi) 1200 ft.: (I, 5, 1), I = 5, 6, 7, 8. (vii) 1800 ft.: (I, 5,1), I = 3, 4, …, 8.

20

P. D. Khurpade et al.

• Case 8 and 9: Vertical production well drilled from layer 1 to 6 and completed with the length 160 ft. (8, 5, K), K = 1–6, and horizontal injection well with length of 900 ft. drilled in bottom layer: (I, 5, 6), I = 1, 2, 3. Vertical Injection well drilled from layer 1–6 and completed with the length 160 ft. (1, 5, K), K = 1–6, and horizontal production well with length of 900 ft. drilled in top layer (1) as in case 1. For all the cases, injection well was operated with bottom-hole pressure of 3700 psi and production well was operated with constant surface liquid rate of 3000 bbl./day and bottom-hole pressure of 1500 psi.

3 Results and Discussion 3.1 Validation of Simulation Results of Base Case The predicted results of the base case are compared with published results of Nghiem et al. [3] for the same reservoir data by using different simulation tools and by different participants such as ECL Petroleum Technologies and Chevron Oil Company where they had used black oil simulator with special extension of local grid refinement and wellbore hydraulics. Fig. 2a represents the oil rate (bbl./day) and cumulative oil production (bbl.) and Fig. 2b shows the water–oil ratio after 1500 days of simulation time for base case. Table 4 provides comparison results of our simulation runs along with ECL and Chevron Company. As depicted in Table 4, fresh simulation performed

Fig. 2 Simulation results of base case. a Oil rate (bbl/day) and cumulative oil production (bbl) as a function of time. b Water–oil ratio variation with time

Table 4 Comparison of simulation runs for base case with Nghiem et al. [3] Base case

New simulation result

ECL result [3]

Chevron result [3]

Cum. oil production MSTB

749.38

753.6

741

CMG-Based Simulation Study of Water Flooding …

21

using CMG in this study is in good agreement with ECL results with an accuracy level of 99.44% and with Chevron results with accuracy level of 98.87%.

3.2 Simulation Studies for Enhancement of Oil Production Nine different cases were considered for examining the effect of well length and configuration patterns to analyze the waterflood performance in order to enhance the oil production. The trends in cumulative oil production, percentage water cut, and water–oil ratio were analyzed in detail. The best pattern to be considered which would give the highest cumulative oil production with an appreciable low amount of water. The predicted cumulative oil, percentage water cut, and water–oil ratio at the end of 1500 days for all the cases are reported in Table 5. It is to be noted that the enhancement of cumulative oil production and detraction in water–oil ratio is observed for cases 1–3 if the injection well length was decreased from 2100 to 900 ft. but further decrease in injection well length to 600 ft. (Case 4), cumulative oil production decreases. Case 3 indicates the maximum oil production with minimum water–oil ratio. This is due to displacement of more oil through injection of water into wider zones reflecting into higher production of water. The enhancement of cumulative oil production has been observed if the production well length increased from 600 to 1800 ft. but at the expense of increase in water–oil ratio (Cases 5, 6, and 7). This implies that availability of wider zones of the reservoir for production of oil but at the same time as the production well crossed the injection well, more zones are inline and close to injection well which allows faster movement of waterfront towards production well resulted in higher water–oil ratio. Figure 3a, b shows a detailed plot of effect of length of injection and production well on cumulative oil production and water–oil ratio. Only the representative cases were mentioned in Table 5. Two more scenarios (case 8, 9) were simulated in which use of one vertical production with one horizontal injection well, and vice versa were examined for the enhancement of oil production. It is observed that in case 8 with vertical production well and horizontal injection well has very low cumulative oil production due to exposure of production well to less oil-producing zones of the reservoir. Finally, case 9 ensures highest cumulative oil production amongst all the cases with vertical injection well and horizontal production well. This is because horizontal producer well has exposed to more zones of reservoir than vertical well results in better recovery with corresponding low water cut. As a representative case, detailed simulation results of case 9 with vertical injection well (which is the best one) is provided in Fig. 4. It represents the oil rate (bbl./day) and cumulative oil production (bbl.) (panel 4a) and the water–oil ratio after 1500 days of simulation time for case 9 (panel 4b). It is to be noted that water to oil ratio for base case was 10.872 which has been reduced drastically to 6.573 (kindly refer to Figs. 2b and 4b). As depicted in the figure, the increased oil production rate was due to horizontal production well communicating with more zones of

22

P. D. Khurpade et al.

Table 5 Simulation results of all nine flooding patterns Production well

Injection well

Base Case

Case 1

Case 2

Case 3

Case 4

Orientation:

H

H

H

H

H

Length (ft.)

900

900

900

900

900

Layer

1 (T)

1(T)

1(T)

1(T)

1(T)

STL (bbl/day)

3000

3000

3000

3000

3000

Min. BHP (psi)

1500

1500

1500

1500

1500

Orientation

H

H

H

H

H

Length

2700

1800

1200

900

600

Layer

6 (B)

6 (B)

6 (B)

6 (B)

6 (B)

Min. BHP (psi)

3700

3700

3700

3700

3700

749.38

812.75

882.86

901.44

892.09

Cum. oil prod. MSTB Water cut %

91.58

90.27

88.99

88.35

88.24

Water–oil ratio

10.872

9.272

8.079

7.581

7.500

Production well

Injection well

Case 5

Case 6

Case 7

Case 8

Case 9

Orientation:

H

H

H

V

H

Length (ft.)

600

1200

1800

160

900

Layer

1(T)

1(T)

1(T)

1–6

1(T)

STL (bbl/day)

3000

3000

3000

3000

3000

Min. BHP (psi)

1500

1500

1500

1500

1500

Orientation

H

H

H

H

V

Length

900

900

900

900

160

Layer

6 (B)

6 (B)

6 (B)

6 (B)

1–6

3700

3700

3700

3700

3700

Cum. oil prod. MSTB

Min. BHP (psi)

871.83

925.52

944.53

562.07

965.49

Water cut %

87.80

88.99

89.89

88.55

86.80

Water–oil ratio

7.194

8.079

8.891

7.731

6.573

T top, B bottom, H horizontal, V vertical, Min. minimum, STL surface liquid rate, BHP bottom-hole pressure

reservoir and of good vertical to lateral anisotropy, tendency of injected water using vertical well to flow near the bottom of the reservoir by gravity drainage and this bottom pressure provides support for the movement of oil upwards towards horizontal production well which has higher oil saturation. As a result, water coning tendency has also been reduced because of more exposure of production well to oil column due to its placement horizontally in higher oil saturation top layer. In order to increase the performance of waterflooding process and maximum recovery of oil with minimum water cut, good understanding of reservoir geology is essential to place the horizontal or vertical wells properly inside the reservoir.

CMG-Based Simulation Study of Water Flooding …

23

Fig. 3 Effect of well length on Cumulative oil production (MSTB) and water–oil ratio. a Injection well. b Production well

Fig. 4 Simulation results of Case 9. a Oil rate (bbl./day) and cumulative oil (bbl.) versus time. b Water–oil ratio versus time

4 Conclusion The production of oil by secondary recovery method namely water flooding is studied. Different well length and flooding patterns were analyzed to understand productivity. CMG-IMEX (Computer Modeling Group) commercial reservoir simulator was used for the numerical simulation study. Based on simulation results performed, it can be concluded that the well length and horizontal and/or vertical flooding pattern affects the productivity of oil from petroleum reservoir. Short length horizontal injection well and short horizontal production well (represented by case 3) indicated better performance than longer horizontal injection well (Cases 1 and 2). The producer well should be drilled in reservoir layer where oil saturation is highest. Also, vertical injection well and horizontal production well has highest productivity of oil in terms of cumulative production and reduced water cut as compared to both horizontal injector and producer flooding pattern. With this flooding configuration (represented by case 9), cumulative oil production increased by 28.83%, and water cut is decreased by 39.54% of the base case. Hence, the study clearly indicated that oil recovery can substantially be increased by utilizing different flooding patterns and appropriate

24

P. D. Khurpade et al.

length of injector and producer well and their location so as to enhance the sweep efficiency and to delay the water breakthrough.

References 1. Sarma, P., Aziz, K., Durlofsky, L.J.: Implementation of adjoint solution for optimal control of smart wells. Paper SPE 92864 presented at the 2005 SPE Reservoir Simulation Symposium held in Houston, Texas, 31 Jan–2 Feb 2005 2. Nwaozo, J.: Dynamic optimization of a water flood reservoir. M.Sc. thesis, University of Oklahoma Graduate College, Norman, Oklahoma, USA (2006) 3. Nghiem, L., Collins D., Sharma, R.: Seventh SPE comparative solution project: modelling of horizontal wells in reservoir simulation. SPE 21221, presented at 11th SPE Symposium on Reservoir Simulation held in Anaheim, California, 17–20 Feb 1991 4. Algharaib, M., Gharib, R.B.C.: A comparative analysis of waterflooding projects using horizontal wells. SPE 93743, presented at 2005 Middle East Oil Show held in Bahrain, 12–15 Mar 2005 5. Taber, J., Seright, R.S.: Horizontal injection and production wells for EOR or waterflooding. SPE 23952, presented at 1992 SPE Permian Basin Oil and Gas Recovery Conference held in Midland, Texas, March 18–20, (1992) 6. Joshi, S.D.: Horizontal Wells Technology. Penwell Publishing Company, Tulsa, Oklahoma (1991) 7. Broman, W.H., Stagg, T.O.: Horizontal wells performance evaluation at Prudhoe Bay. SPE 90-124, presented at SPE Annual Technical Meeting held in Calgary, Alberta, 10–13 June 1990 8. Popa, C.G., Chipea, M.: Improved water flooding efficiency by horizontal wells. SPE 50400, presented at the SPE International Conference on Horizontal Well Technology, Calgary, Alberta, Canada, 1–4 Nov 1998 9. Aziz, K., Settari, A.: Petroleum Reservoir Simulation. Applied Science Publisher Ltd., London (1979) 10. Gielisse, R.A.M.: Dynamic local grid refinement. M.Sc. thesis, Delft University of Technology, Netherlands (2016)

Exergy-Based Comparison of Two Gas Turbine Plants with Naphtha and Naphtha-RFG Mixture as Fuels Sankalp Arpit, Sagar Saren, Prasanta Kumar Das, and Sukanta Kumar Dash

Nomenclature cp Ex h m˙ P R s T W W˙

Specific heat at constant pressure (kJ/kg K) Exergy rate (kW) Specific enthalpy (kJ/kg) Mass flow rate (kg/s) Pressure (kPa) Gas constant (J/gK) Specific entropy (kJ/kg K) Temperature (K) Work (kJ) Power (kW)

Greek Letters η ξ

Efficiency Ratio of chemical exergy and lower heating value of fuel

S. Arpit (B) School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India e-mail: [email protected] S. Saren · P. K. Das · S. K. Dash Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_3

25

26

S. Arpit et al.

Subscripts a e g i o

Air Exit Exhaust gas Inlet Dead state

Abbreviations AC CC GT LHV

Air compressor Combustion chamber Gas turbine Lower heating value

1 Introduction Improvement in human lifestyle has caused a huge gap in energy demand and supply. In order to narrow demand and supply gap, various energy resources are being accessed which are either renewable or non-renewable. From the past, most of the energy demand is met by non-renewable sources of energy like coal and natural gas, processed in power plants. Hence, thermodynamic analysis of power plant is important to understand the underlying principle” More from less” principle. In recent decades, the exergy [1, 2] analysis has been adopted as more useful method in the design, optimization, and improvement of energy systems such as gas turbine power plants. In the present paper, two Gas Turbine Plants (GT1 and GT2) plant of 34.5 MW (Figs. 1 and 2) each has been taken up as a case study for thermodynamic analysis. GT1 is being charged by naphtha, whereas GT2 is charged by Residual fuel gas and Naphtha mixture. Thermodynamic analysis based on energy and exergy is performed using actual operating data. Technical specification is provided in Table 1. In GT1 and GT2, fresh air (109.2 kg/s) form outside environment (1) enters the compressor and compressed non-isentropically to higher temperature and pressure (2). The highpressure and high-temperature air is used to burn fuel (5) in case of GT1 (5 and 6), in case of GT2 inside the combustion chamber. The resulting high temperature and high pressure (3) combustion product enters the turbine, gets expanded nonisentropically (4) while producing power. A part of this turbine power is used to drive the compressor.

Exergy-Based Comparison of Two Gas Turbine Plants …

27

Fig. 1 Open cycle gas turbine power plant (GT1) with Naphtha (5) as fuel (case A)

Fig. 2 Open cycle diagram of gas turbine power plant (GT1) with Naphtha (5) and Residual fuel gas (6) as fuel (case B) Table 1 Raw data of GT1 and GT2 Parameters

GT2 (Naphtha and RFG)

GT1 (Naphtha)

Mass flow rate

110.2 kg/s

110.2 kg/s

CPD

8.9 bar

8.9 bar

CPT

366 °C

366 °C

Naphtha

0.8 kg/s, 19.6 bar, 34 °C

2.57 kg/s, 15.2 bar, 34 °C

Residual fuel gas

1.47 kg/s, 15.2 bar, 92 °C

–

Rated work

34.5 MW

34.5 MW

Actual work

29.98 MW

24.45 MW

28

S. Arpit et al.

2 Modelling of Proposed System Assumptions were taken in order to model the gas turbine unit: • Steady-state operating condition and ideal gas behaviour of air and combustion gases constituents. • Kinetic and potential energy of fluid streams is neglected. The dead-state conditions at 101.325 kPa and 303 K. • Molar air composition is 77.48% N2 , 20.59% O2 , 0.03% CO2 . • The naphtha has following composition: C (0.8392), H2 (0.1583), S (0.001). Lower heating value is 44,079 kJ/kg. • The residual fuel gas (Residual fuel gas) has following composition: H2 (0.3674), CO (0.0005), H2 S (0.0001), CH4 (0.4986), C2 H4 (0.0144), C2 H6 (0.0096), C3 H8 (0.0173), C3 H6 (0.0073). Lower heating value is 51,660 kJ/kg.

2.1 Energy Analysis The principle of mass conservation, energy conservation and exergy balance equation is applied for thermodynamic modelling of each and every component with possible heat interaction and work transfer. (a) Compressor: The energy balance for air compressor subsystem is given by

W˙ AC = m˙ a c pa (T2 − T1 ) 

T2s T1



 =

ηAC =

P2 P1

(1)

 γ −1 y

T2s − T1 T2 − T1

(2) (3)

Inlet and outlet of the compressor air temperature are indicated by T1 and T2 . ηAC is the isentropic efficiency of air compressor and γ is specific heat ratio. (b) Combustion chamber The energy balance for the combustion chamber subsystem for GT1 and GT2 is given by Eqs. (2) and (5). m˙ a h 2 + m˙ 5 LHV5 = m˙ g h 3

(4)

m˙ a h 2 + m˙ 5 LHV5 + m˙ 6 LHV6 = m˙ g h 3

(5)

Exergy-Based Comparison of Two Gas Turbine Plants …

29

(c) Gas turbine The energy balance equation for gas turbine is given by: W˙ GT = m˙ g (h 3 − h 4 ) 

T3 T4



 =

ηGT =

P3 P4

(6)

 γ −1 y

T3 − T4 T3 − T4s

(7) (8)

Mass flow rate of flue gas is denoted by m˙ g m˙ g = m˙ f + m˙ a

(9)

The net power can be expressed as W˙ net = W˙ GT + W˙ AC

(10)

T3 and T4 indicate temperature of gas at inlet and outlet of gas turbine. ηGT is isentropic efficiency of gas turbine; c pg is the specific heat of gas. Variation of specific heat of flue gas c pg considering the composition of the combustion products with temperature for GT1 and GT2 is given below c pg = 0.9840 + 0.0001262T + 0.000000146T 2 (For GT1) c pg = 1.031 + 0.0000858T + 0.000000195T 2 (For GT2) Realizing energy analysis of each and every component, first law analysis can be carried out by equations written below. W˙ net,GT1 m˙ 5 LHV5

(11)

W˙ net,GT2 m˙ 5 LHV5 + m˙ 6 LHV6

(12)

η I,GT = η I,GT =

2.2 Exergy Analysis Maximum useful work obtained from a system is represented by exergy and is a widely accepted tool for thermodynamic analysis. Physical exergy designates

30

S. Arpit et al.

maximum work potential of system while chemical exergy is related to change of chemical composition of a system from its equilibrium conditions. E˙ x,heat +



m˙ i ex,i =



m˙ e ex,e + E˙ x,w + I˙dest

(13)

e

i

  T0 × Q˙ i E˙ x,heat = 1 − Ti

(14)

E˙ x,W = W˙

(15)

In Eq. (13), E˙ x,heat represent the exergy flow due to heat transfer, i and e represent inlet and exit condition of energy systems. Further, E˙ x,W represents exergy flow due to work. In order to calculate physical exergy of water/steam phases, equation written below is used. ex = ex,physical + ex,chemical

(16)

ex,physical = (h − h 0 ) − T0 (s − s0 )

(17)

In Eq. (15) h 0 and s0 are enthalpy and entropy values of system at dead-state conditions. In the thermodynamic analysis, chemical exergy of fuel and combustion products have an important role. The chemical exergy of Naphtha is determined by Eq. (18) ζ =

ex,fuel LHV

(18)

ζ represents ratio of chemical exergy to lower heating value of the fuel. In order to calculate chemical exergy of gaseous fuel and combustion products below equation can be used.  n  n   ex,chemical = xi ex,chemical,i + RT0 xi ln(xi ) (19) i=1

i=1

In order to determine the chemical exergy of combustion gases, it is key to know the molar composition of it after combustion process. The molar fraction of combustion gases (Tables 2 and 3) produced in GT1 and GT2 is found by the chemical equation. The following equations can be used to calculate the exergy efficiency of the gas turbine power plant. η I i,GT =

W˙ net,GT1 E˙ xNaphtha

(20)

Exergy-Based Comparison of Two Gas Turbine Plants … Table 2 Flue gas composition in GT1 and GT2

Component CO2

Table 3 Thermodynamic property comparison between two fuels

Molar fraction (GT1)

31 Molar fraction (GT2)

ex,chemical

0.78

4.58

N2

83.72

83.72

25.71

O2

16.4

15.09

124.06

SO2

0.0051

0.0016

H2 O

3.6

7.7

442.73

313.4 9.5

Thermodynamic property

GT1

GT2

Isentropic efficiency AC (%)

79

79

Isentropic efficiency GT (%)

89.4

91

Turbine inlet temperature (K)

1089

1133

First law efficiency (%)

21.58

20.18

η I i,GT =

W˙ net,GT2 ˙ E xNaphtha + E˙ xRFG

(21)

3 Results and Discussions This section presents energy and exergy analysis of GT plant working on naphtha and naphtha and residual fuel gas with the same configuration.

3.1 Energy Analysis of GT2 and GT1 This section presents the comparison between GT2 and GT1 based on energy and exergy analysis. Table 3 presents the comparison of thermodynamic property between GT2 and GT1. Further, energy flow of both gas turbine (GT2 and GT1) is presented in form of Sankey diagram (Figs. 3 and 4), which depicts enthalpy flows across the various components. The flows are represented as arrows, and the width represents amount of energy. From the diagram, it can be inferred that 36.9 MW is provided by the turbine, and turbine produces 181.3 MW from which 29.98 MW is output as electrical energy and the remaining 114.97 MW is produced as waste heat in GT2.

32

S. Arpit et al.

Fig. 3 Sankey diagram of GT1

Fig. 4 Sankey diagram of GT2

3.2 Exergy Analysis of GT2 and GT1 This section presents exergy analysis of both gas turbine (GT2 and GT1). Table 4 presents the value of exergy destruction and exergy efficiency of GT2 and GT1. It can be seen that in case of GT2 exergy destruction is quite high because of high temperature of Residual Fuel gas as compared to Naphtha. Furthermore, exergy efficiency (Fig. 7) of GT2 (20.70%) is higher as compared to GT1 (20.17%) (Figs. 5 and 6).

Exergy-Based Comparison of Two Gas Turbine Plants …

33

Fig. 5 Grassman diagram of GT1

Fig. 6 Grassmann diagram of GT2 Table 4 Exergy destruction of GT1 and GT2 Equipment

Exergy destruction (GT1)

Exergy destruction (GT2)

AC

4300

5700

CC

58,700

78,200

GT

6320

2100

34

S. Arpit et al. 30

Fig. 7 Energy and Exergy efficiency of GT1 and GT2 Efficiency (%)

25 20

26 21.5

20.7

20.17

15 10 5 0

GT1 Energy Efficiency

GT2 Exergy Efficiency

4 Conclusion Energy and exergy analysis of two GT power plant configuration, case A Naphtha based, case B Naphtha and Residual Fuel gas have been performed. Some of the main conclusions that can be drawn from this study are mentioned below. (a) The combustion chamber is a high source of exergy destruction in gas turbines followed by air compressors and gas turbines. (b) In case of GT2, exergy destruction in combustion chamber is high as the temperature difference between naphtha and residual fuel gas high causing mixing. Second law analysis plays a crucial role in the evaluation of thermodynamic system. Some recommendations are as follows: • Due to additional fuel Naphtha–Residual fuel gas mixture, both energy and exergy efficiency of GT2 has increased as compared to GT1, but due to high exergy destruction in case of GT2 there is a negligible increase in exergy efficiency. • Some sort of preheating arrangement can be made in GT2 so as to reduce exergy destruction due to mixing losses between Naphtha and Residual fuel gas.

References 1. Rosen, M.A., Le, M.N., Dincer, I., Casas-Ledón, Y., Spaudo, F., Arteaga-Pérez, L.E., et al.: Exergoenvironmental analysis of a waste-based Integrated Combined Cycle (WICC) for heat and power production. Energy 32, 249–253 (2005) 2. Ersayin, E., Ozgener, L.: Performance analysis of combined cycle power plants: a case study. Renew. Sustain. Energy Rev. 43, 832–842 (2015)

Decentralized Solid Waste Management for Educational-Cum-Residential Campus: A Pilot Study Deepak Singh Baghel

and Yogesh Bafna

1 Introduction Appropriate handling of the anthropogenic waste is a great challenge faced by world to achieve goal of sustainable development [1]. All the garbage coming out of animal and human activities that possess no use is termed as solid waste. 1,41,064 metric tonnes per day of municipal solid waste (MSW) is generated in India [2] (see Fig. 1). Major contributing factors for this include urban population which is 31% of total population and rate of urbanization which is 2.4% during 2011–2015 [3]. About 80– 90% of total MSW is collected out of which only 22–27% is processed and treated [4, 5]. This huge waste generated requires a suitable locations and high treatment cost due to which municipalities implemented integrated waste management policy. This includes methods such as waste minimization, processing by reuse and recycling, treatment by composting, incineration, biomethanation, pyrolysis, gasification, and final disposal in landfill [6]. These methods are applicable based on the quality and quantity of waste produced [7]. To facilitate collection, segregation, storage, transportation, processing, and disposal of MSW, solid waste management (SWM) rules are circulated by MOEF [8]. The MSW possesses huge variations in terms of volume and quality at different locations and has correlation with economic status of people. Various Indian cities have limitations in the current practices used in municipal solid waste management (MSWM) which includes deficit manpower, machinery and finances, and lack of implementation [9]. Further, collection efficiency of waste is another problem due to thousands of waste which remain unhandled per day [10]. Transportation of MSW is another major issue which increases overall cost and causes environmental pollution. Large number of researches for optimizing total overall cost of MSWM including transportation has been carried out [11, 12]. Further, D. S. Baghel (B) · Y. Bafna MPSTME, NMIMS, Shirpur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_4

35

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D. S. Baghel and Y. Bafna

Metals Glass and rags Paper and plastics Ash and Fine earth Food and vegetable waste Fig. 1 Composition of municipal solid waste [2]

MSWM at centralized level includes various risks in terms of planning and finance [13]. Also, site selection of these MSW facilities requires different considerations of geology, water supply resources, land use, sensitive sites, air quality, groundwater quality, etc. [14]. Decentralization of MSWM is solution to the problem [15]. This will increase collection efficiency of waste. MSW generated India consists mainly of biodegradable matter with high moisture content [2]. Hence, after segregation, processing of waste using composting and biomethanation can be adopted. However, after removing reusable and recyclable materials from remaining waste, disposal in engineered landfill is recommended [5]. Numerous innovations and methodologies on composting, biomethanation and sanitary landfill have been developed to provide efficient results. Few of these include specifying characteristics to represent degree of maturity of product using box composting [16], spectrometric analysis of organic matter transformation [17] during composting, pilot scale study of composting for waste minimization, resource recovery and increased crop yield [18], effect of leachate recirculation on acidification in biogas plant [19], behavior of metals and non-metals in a landfill leachate [20] and environmental impacts and operating cost of landfill [21], and use of synthetic waste for anaerobic digestion of restaurant waste to produce methane by varying input lipid [22]. Current work presents decentralized solid waste management using two systems viz composting and biomethanation for a closed campus.

2 Materials and Methods The study is performed in an educational-cum-residential campus located in Shirpur, Maharashtra. The campus consists of mess area and canteen for serving food to campus students and staff, hostels for students, and quarters for staff, laundry, and academic buildings. The campus has well-functioning waste collection facility. Food, paper, plastics, and polythenes are collected in separate dustbins, and wastes like rags, leaves, wood, etc., are collected separately. These wastes are further sent to local municipal corporation for disposal. Presently, the campus does not have its own any solid waste management facility. With the increasing population in campus,

Decentralized Solid Waste Management for Educational …

37

MSW

Seggregated collection

Quantification of waste

Analysis of waste

Sample collection and analysis

Monitoring

Design of biomethantion pilot plant

Design of compost

Fig. 2 Flowchart of overall methodology

the quantity of waste will rise. Hence, adequate management of waste by available techniques will help in improving campus scenario. The flowchart of overall methodology is depicted in Fig. 2.

2.1 Composting Composting is decomposition of organic matter to produce compost. It can be done either aerobically or anaerobically. Aerobic composting Two types of methods namely windrow composting and box composting are adopted. Windrow composting requires as set of long narrow piles arranged on a compost pad to receive waste one by one daily for 35 days (composting period). The composting pits are designed and constructed by brick masonry having a concrete base to prevent leachate percolation to soil. The depth of these aerobic composts is kept low to keep air circulation and facilitate turning up regularly. Rectangular shaped windrow and box composts are designed to receive daily biodegradable waste. The composts in both methods are filled in layers consisting chopped vegetables followed by cow dung and food waste (see Fig. 3a). Green and dry leaves are added to overcome high moisture content and C/N ratio of incoming waste (Table 1). A sample of sewage (activator) from aeration tank of sewage treatment plant in campus is added as seed to facilitate the process. Because of dry season, additional sprinkling of water and leachate recirculation is also required. To maintain aerobic environment, the composts are turned up regularly in 5 days and to analyze quality of compost, samples are taken on 14th, 21st, 28th, and 35th day. Anaerobic composting Box-type anaerobic compost is designed and constructed with capacity to receive one-day organic waste. Materials used are same as aerobic pit. Unlike aerobic method, the depth of these aerobic composts is kept higher. The compost is filled in layers similar to aerobic one, but the number of layers is increased because of greater depth. Also, a thick soil cover followed by plastic cover is applied at top to keep anaerobic environment. A leachate collection system at the bottom and methane escape vent at the top is provided.

38

D. S. Baghel and Y. Bafna

Fig. 3 Compost filling and sample

Table 1 Characteristics of biodegradable waste before composting S. No.

Parameter

Value

Required range (CPHEEO Manual on MSW, 2016)

1.

Moisture content (%)

63.4

55–60

2.

C/N ratio

32:1

25:1–30:1

2.2 Biomethanation The waste from kitchen consisting of chopped vegetables and food is also fed into the pilot waste-to-energy plant to get nutrient rich slurry and methane under anaerobic condition. The characteristics of waste are given in Table 2. The food waste is fed as slurry by mixing it water having temperature 45 °C and cow dung [23–25]. A sample of sewage and compost is added to maintain optimum C/N ratio in digester. The pH of sample is monitored to be between 6.5 and 7.5 [26, 27]. The slurry within the tank is also frequently stirred to prevent layer formation and continuously mix waste. The plant is operated for 30 days. Table 2 Characteristics of biodegradable waste before biomethanation

S. No.

Parameter

Value %

1.

Moisture content

68.4

2.

Total solids (TS)

31.6

3.

Total volatile solids (TVS)

79.7

Decentralized Solid Waste Management for Educational …

39

After which biomethane produced treated further to remove water vapor and hydrogen sulfide. A container having water at 20 °C is kept to remove water vapor by condensation and hydrogen sulfide which is soluble in water up to certain extent [28]. Biogas contains 50–85% CH4 (methane), 20–35% CO2 , and H2 , N2 , and H2 S form the rest [29]. Density of methane is 1.15 kg/m3 [30]. The treated methane gas is collected in Mylar balloon. By weighing the treated biomethane (H2 O removal and H2 S removal), the quantity of biogas per kg of total solids is estimated. Pilot waste-to-energy plant The circular cylinder diameter 1200 m and height 760 mm is taken as digester. It has an inlet of diameter 80 mm fitted with oneway cap to feed waste into digester. Five steel plate wings are fixed to plastic pipe through screws. A pipe above the base 240 mm is inserted to hold and manually rotating steel plate fans. The length of wings is 190 mm which helps to mix the inner contains. The material is filled up to height of 350 mm, and the above space is kept empty for gas collection. The upper part is covered with a dome-shaped lid for gas collection. An outlet gas pipe of diameter 190 mm and length 1000 mm is provided which is connected to another cylindrical container having water to remove H2 O and H2 S (see Fig. 4). The digester is provided with a slurry outlet of diameter 190 mm at bottom. An outlet for treated gas is provided at the top of water cylinder fitted with Mylar balloon to collect gas. Table 3 shows details of feedstock into the digester. The plant runs for 30 days with operating parameters as presented in Table 4.

Inlet

Clean Methane

Raw Methane

Digester

H S and H2O 2

removal

Slurry outlet Fig. 4 Pilot plant schematic

Table 3 Details of feedstock into digester

Material

Quantity

Water

200 L is poured

Food waste (from mess)

50 kg is poured

Cow dung

12.5 kg is poured

Total material filling

72.5 kga

aA

small quantity of sewage and compost is also mixed

40

D. S. Baghel and Y. Bafna

Table 4 Operating parameters of digester S. No.

Parameter

Value %

1.

Temperature

35–37 °C

2.

pH

6.5–7.5

3.

(C/N) ratio in anaerobic digesters

20:30

Table 5 Solid waste quantification and characteristics S. No.

Quantity(kg/day)

Source

Type of waste

Properties

1.

430

Mess and canteen

Food and leaves

Biodegradable

2.

280

Stationery, offices, canteen

Paper, wood, cardboard, glass, plastic

Recyclable/reusable

3.

100

Pavements, construction site, water boilers, WTP

Dust, silt, sand, ash

Land fillable

4.

190

Laundry and canteen Textile and wrappers

Recyclable

3 Data Collection The details of data collected for a total of 1000 kg/day waste produced is mentioned in Table 5.

4 Results and Discussion Details of composts designed by each method are presented in Table 6. Total area required for windrow facility of 35 days was 166 m2 . The total quantity of compost produced by aerobic method is 77 kg for box compost and 2350 kg for windrow compost. The collected aerobic box compost samples on 14th, 21st, 28th, and 35th day are dried in oven and analyzed to get parameters as presented in Table 7. Analysis of compost showed that • Compost is having high moisture which can be due to high moisture in feedstock sample, and more dry leaves needs to be added in it. • Also, higher moisture content resulted in brownish color of compost. • Nitrogen content is quite high which makes suitable for plants and crops. • All other parameters are within standards which makes compost suitable for application. The quantity of biomethane after weighing was 4.96 kg. The biomethane production was estimated to 0.162 m3 /kg of TS which is close to that of Bhattacharyya

Decentralized Solid Waste Management for Educational …

41

Table 6 Specifications of composts Density

Aerobic method

Anaerobic method

450 kg/m3 Windrow composting • Shape rectangular • Spacing = 1.5 m • Dimension = 1.27 m × 1.0 m × 0.75 m • Leachate tank = 1 m × 1 m × 0.5 m • Time = 35 days • Feedstock + cow dung + leaves

• Dimension = 1.0 m × 0.41 m × 2.30 m • Time = 120 days • Feedstock + cow dung + soil + leaves

Box composting • Dimension = 1.27 m × 1.0 m × 0.75 m • Time = 30 days • Feedstock + cow dung + leaves

Table 7 Compost quality S. No.

Parameter

Value

Organic compost (Fertilizer Control Order 2009)

Phosphate-rich organic manure (Fertilizer Control Order 2013)

1.

Total nitrogen (as N), percent by weight

1.2

0.8

0.4

2.

Total phosphate (as P205 ) 0.7 percent by weight

0.4

10.4

3.

Total Potassium (as K20 ), 1.0 percent by weight

0.4

4.

Moisture content

31.7%

15.0–25.0

25.0

5.

C/N ratio

18:1.

Less than 20:1

Less than 20:1

6.

Color

Brownish

Dark brown to black

7.

Odor

Absent

Absence of foul odor

8.

pH

7.2

6.5–7.5

(1:5 solution) maximum 6.7

et al. [19], Dhar et al. [31], and Kumar et al. [32]. Assuming 70% CH4 in biogas, the total quantity of methane produced is 0.12 m3 /kg of TS and 0.21 m3 /kg of VS which is near to Babaee and Shayegan [33], Cho et al. [34], and Dhar et al. [31]. The result shows that cleaning of biomethane by water was effective and provided better quality methane.

42

D. S. Baghel and Y. Bafna

5 Conclusion Current scenario of population growth and increasing waste has overloaded the waste management system in any city. The municipal corporations are struggling with handling the collection and disposal of such a huge amount of waste. The cost of collection and transportation itself contributes to huge percentage of overall cost for MSW. The present investigation for decentralized management of solid waste based on different techniques and their modifications by researchers presents feasible results. The results further depict that 2350 kg of compost can be generated by aerobic windrow composting which can suffice campus’s in-house nursery compost requirement and save manure cost. However, keen monitoring of compost is required because of higher moisture content and nitrogen present in it. The excess leachate produced can be sent to in-house sewage treatment plant for stabilization. The pilot waste-to-energy plant developed can be upgraded to full scale and result in production of 16 m3 of biomethane for total 430 kg daily food waste. The treated methane can be either stored or used directly for partial replacement of LPG for cooking purpose. Slurry produced in digester can be dried and being rich in nutrients can be used as manure. Campus has already attained status of zero liquid by recycling its wastewater after treatment. By adopting the MSWM methodology presented, waste-free campus status can be achieved. Engineered landfill not recommended because of location of campus and residential population. Moreover, the quantity of land fillable of waste is also less. However, it can be used for town of Shirpur because of low water table and availability of suitable sites for setting up landfill. These results can be utilized for solid waste management of the developing town of Shirpur under Swachh Bharat Mission. Further, this approach can be applied to similar localities in more efficient and automated manner to produce fuel and energy. Acknowledgements We are thankful to management of SVKM’s NMIMS for funding the project and SVKM’s NMIMS MPSTME, Shirpur campus for facilities rendered for the research work.

References 1. Anyaoku, C.C., Baroutian, S.: Decentralized anaerobic digestion systems for increased utilization of biogas from municipal solid waste. Renew. Sustain. Energy Rev. 90, 982–991 (2018) 2. Asefi, H., Lim, S.: A bi-objective optimization approach to a municipal solid waste management system. In: 15th International Conference on Environmental Science and Technology, Rhodes, Greece (2017) 3. Babaee, A., Shayegan, J.: Effect of organic loading rates (OLR) on production of methane from anaerobic digestion of vegetables waste. In: World Renewable Energy Congress, Sweden, pp. 411–417 (2011) 4. Belevi, H., Baccini, P.: Long-term behavior of municipal solid waste landfills. Waste Manage. Res. 7, 43–56 (1989) 5. Bhattacharyya, J.K., Kumar, S., Devotta, S.: Studies on acidification in two-phase biomethanation process of municipal solid waste. Waste Manage. 28, 164–169 (2008)

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6. Castaldi, P., Alberti, G., Merella, R., Melis, P.: Study of the organic matter during municipal solid waste composting aimed at identifying suitable parameters for the evaluation of compost maturity. Waste Manage. Res. 25, 209–213 (2005) 7. Census: Ministry of Home Affairs, India (2011) 8. Central Pollution Control Board: Waste Generation and Composition. Ministry Environment Forest and Climate Change, India (2017) 9. Central Pollution Control Board: The National Action Plan for Municipal Solid Waste Management. Ministry Environment Forest and Climate Change, India (2017) 10. Central Pollution Control Board: Selection Criteria for Waste Processing Technologies. Ministry Environment Forest and Climate Change, India (2016) 11. Central Pollution Control Board: Solid Waste Management (SWM) rules. Ministry of Environment Forest and Climate Change, India (2016) 12. Chefetz, B., Hatcher, P.G., Hadar, Y., Chen, Y.: Chemical and Biological Characterization of Organic Matter during Composting of Municipal Solid Waste. J. Environ. Qual. 25, 776 (1996) 13. Chen, Y., Cheng, J.J., Creamer, K.S.: Inhibition of anaerobic digestion process: a review. Bioresour. Technol. 99, 4044–4064 (2008) 14. Cho, J.K., Park, S.C., Chang, H.N.: Biochemical methane potential and solid state anaerobic digestion of Korean food wastes. Bioresour. Technol. 52, 245–253 (1995) 15. CPHEEO: Manual on Solid Waste Management. Ministry of Housing and Urban Affairs, India (2016) 16. Deublein, D., Steinhauser, A.: Biogas from Waste and Renewable Resources (2008) 17. Dhar, H., Kumar, P., Kumar, S., Mukherjee, S., Vaidya, A.N.: Effect of organic loading rate during anaerobic digestion of municipal solid waste. Bioresour. Technol. 2–7 (2015) 18. Ghiani, G., Laganà, D., Manni, E., Musmanno, R., Vigo, D.: Computers & operations research operations research in solid waste management: a survey of strategic and tactical issues. Comput. Oper. Res. 44, 22–32 (2014) 19. Johannessen, L.M., Boyer, G., Mikkel, L.: Observations of Solid Waste Landfills in Developing Countries: Africa, Asia, and Latin America. World Bank Rep. 47 (1999) 20. Jørgensen, P.J.: Biogas-green energy (2009) 21. Kumar, S., Bhattacharyya, J.K., Vaidya, A.N., Chakrabarti, T., Devotta, S., Akolkar, A.B.: Assessment of the status of municipal solid waste management in metro cities, state capitals, class I cities, and class II towns in India: an insight. Waste Manage. 29, 883–895 (2009) 22. Kumar, S., Mukherjee, S., Devotta, S.: Anaerobic digestion of vegetable market waste in India. World Rev. Sci. Technol. Sustain. Dev. 7, 217–224 (2010) 23. Lee, J.I., Mather, A.E.: Solubility of Hydrogen Sulfide in Water, Berichte der Bunsengesellschaft für physikalische Chemie (1977) 24. Li, J., Kumar Jha, A., He, J., Ban, Q., Chang, S., Wang, P.: Assessment of the effects of dry anaerobic co-digestion of cow dung with waste water sludge on biogas yield and biodegradability. Int. J. Phys. Sci. 6, 3723–3732 (2011) 25. Mata-Alvarez, J., Mac, S., Llabr, P.: Anaerobic digestion of organic solid wastes. An overview of research achievements and perspectives. Bioresour. Technol. 74, 3–16 (2000) 26. Mbuligwe, S.E., Kassenga, G.R., Kaseva, M.E., Chaggu, E.J.: Potential and constraints of composting domestic solid waste in developing countries: findings from a pilot study in Dar es Salaam, Tanzania. Resour. Conserv. Recycl. 36, 45–59 (2002) 27. Neves, L., Gonçalo, E., Oliveira, R., Alves, M.M.: Influence of composition on the biomethanation potential of restaurant waste at mesophilic temperatures. Waste Manage. 28, 965–972 (2008) 28. Oliveira, L.S.B.L., Oliveira, D.S.B.L., Bezerra, B.S., Silva Pereira, B., Battistelle, R.A.G.: Environmental analysis of organic waste treatment focusing on composting scenarios. J. Clean. Prod. 155, 229–237 (2017) 29. Pastorek, Z., Kára, J., Jeviˇc, P.: Biomasa - obnovitelný zdroj energie. FCC Public, Prague, p. 288 (2004) 30. Rajeshwari, K., Pant, D., Lata, K., Kishore, V.: Studies on biomethanation of vegetable market waste. In: Biogas Forum (1998)

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31. Sharholy, M., Ahmad, K., Mahmood, G., Trivedi, R.: Municipal solid waste management in Indian cities—a review. Waste Manage. 28, 459–467 (2008) 32. Sumathi, V.R., Natesan, U., Sarkar, C.: GIS-based approach for optimized siting of municipal solid waste landfill. Waste Manage. 28, 2146–2160 (2008) 33. Tan, S.T., Lee, C.T., Hashim, H., Ho, W.S., Lim, J.S.: Optimal process network for municipal solid waste management in Iskandar Malaysia. J. Clean. Prod. 71, 48–58 (2014) 34. Wei, Y., Li, J., Shi, D., Liu, G., Zhao, Y., Shimaoka, T.: Environmental challenges impeding the composting of biodegradable municipal solid waste: a critical review. Resour. Conserv. Recycl. 122, 51–65 (2017)

Does the Criteria of Instability Thresholds During Density Wave Oscillations Need to Be Redefined? Subhanker Paul, Suparna Paul, Maria Fernandino, and Carlos Alberto Dorao

1 Introduction Two-phase flow instabilities and in particular the density wave oscillations are commonly observed instabilities [1, 2] in flow boiling and condensation systems such as nuclear reactors and steam generators. These instabilities are found to be one among the major impediments in increasing the efficiency of aforementioned devices. Besides, these instabilities hinder the performance of solar thermal power production and are also seen in other systems ranging from turbine blades, rocket engines, chemical processes for hydrogen and metal production, cooling of avionics systems, hybrid vehicle power electronics, air conditioning, and space cooling technologies. The associated multifaceted ill-effects on the system performance (namely mechanical vibrations, thermal fatigue, and the heat-transfer deterioration) have asked the researchers to do research on the DWOs over a few decades. In particular, over last 80 years, several researchers have attempted to unveil the accurate mechanism of this instability, knowing which, it can be effectively controlled. Based on the intensive numerical and experimental evidence, so far three mechanisms have been postulated by the researchers as follows: 1. The DWOs can be attributed as the delayed feedback of the transient distribution of the pressure drop along the pipe [2, 3]. This feedback is caused by the difference in the densities between the subcooled liquid entering the channel and the two-phase mixture exiting. A pressure drop perturbation in the flow leads to a flow rate perturbation, which causes an enthalpy perturbation propagating throughout the pipe. This modifies the lengths of the single-phase and the two-phase regions [2] which alter the densities of the fluid in these regions. When a certain amount S. Paul (B) · S. Paul · M. Fernandino · C. A. Dorao Department of Energy and Process Engineering, Norwegian University of Science and Technology, NTNU, Trondheim, Norway e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_5

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of feedback is induced by the perturbations, a series of self-sustained oscillating high- and low-density fluid flow appears which is broadly known as DWOs. In general, the period of oscillations is double to the channel residence time. The above-said mechanism suggests that the dominant factor to trigger the instability is the density variation of the fluid along the length of the channel which was first proposed by Stenning [3] and later widely accepted. 2. In 1994, Rizwan-Uddin [4] based on his numerical investigations criticized the above mechanism. In this study, it was found that the alternate higher- and lowerdensity waves cannot be treated as the fundamental mechanism. Instead, the variation in the mixture velocity is dominant over the mixture density. It was concluded that the oscillations can persist even with very weak density waves. One strong argument made in this study is: the exit pressure drop changes strongly with the exit velocity instead of the exit density. In addition, it was also found that the period of oscillations was closer to four times the channel residence time. It should be noted that, although the above-mentioned mechanisms are widely accepted, they do not provide much details of the applicability of such mechanisms on controlling the amplitude and frequency of the DWOs. 3. In view of this knowledge gap, in the recent studies of O’Neill [5], a counter mechanism to the above-said mechanisms of DWO is presented. Based on a number of experiments and numerical investigations, it was found that, instead of the conventionally accepted feedback effects of pressure drop, flow rate and flow enthalpy change, the body force acting on the system plays a dominant role to define the characteristics of the DWOs. It is mentioned that the body force, acting on the liquid and the vapor phase separately, creates an accumulation of the liquid in the inlet of the channel. The accumulation of the liquid at the channel inlet forms a high-density front (HDF) which travels along the channel. This HDF during its travel along the channel re-wets the liquid film and thus re-establish annular co-current flow. These mechanisms being contradictory to each other suggest that further research is needed to attain an agreement on the accurate mechanism that controls the amplitude and frequency of the DWOs. The hunt for the mechanism along-with the control of the multifaceted ill-effects of the DWOs has motivated several researchers to perform multiple numerical [4, 6–8] and experimental studies [9–13]. In particular, the experimental studies on DWOs were focused on identifying the linear instability thresholds. It should be noted that the linear stability threshold is only valid for small perturbation in the system. However, with considerably large perturbation, similar systems can show multiple strange behaviors as evident from the numerical investigations [14–21]. Since in real operating situations, one does not have much control on the amount of perturbation, the linear stability threshold does not provide the complete stability characteristics of the system. In particular, the studies by Paul and Singh [22] suggest that at some operating conditions (namely subcritical Hopf bifurcation region), the system can exhibit growing oscillations after the exposure to a large perturbation in the linearly stable region. The above-mentioned numerical investiga-

Does the Criteria of Instability Thresholds During Density Wave …

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tions raise a conflict to the conventional definition of stable and unstable operating conditions in real-world applications. Hence, in this study, an attempt is made to experimentally investigate the instability thresholds by using the concept of limit cycle oscillations across the linear stability boundaries.

2 Experimental Setup and Procedure The experimental facility is a closed loop (Fig. 1) consisting the segments namely a main tank, a pump, a conditioner, a heated test section, a visualization glass, an adiabatic test section, and a condenser. A magnetically coupled gear pump is used to drive the working fluid (R134a) through the loop. The main tank is used to control the working pressure of the system at the saturation conditions. The inlet temperature of the fluid is adjusted with the help of the pre-heater that is a shell and tube type heat exchanger with glycol in the shell side. At the inlet of the heated section, a Coriolis flow meter is installed to measure the flow rate of the fluid entering the heated section. Ten thermocouples and seven pressure taps are distributed along the length of the test section. All the variables are logged using a National Instruments NI RIO data acquisition system and were acquired at a frequency of 10 Hz. The test section is a stainless steel tube of length 2035 mm with 5 mm I.D and 8 mm O.D. A manually operated valve (K i = 10.65) before the test section and an orifice plate at the exit (K e = 2.63) are installed to control the flow which are known to be the important tools to trigger and control the DWOs.

Fig. 1 Sketch of the test facility

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Table 1 Table of experimental conditions Pressure 650 Mass flux Channel orientation Channel length Channel diameter Inlet loss coefficient Exit loss coefficient

300 Horizontal 2035 5 10.65 2.63

(kPa) (kg/m2 s) – (mm) (mm) – –

2.1 Experimental Method For all the experiments, the inlet pressure of the fluid was kept constant at 650 kPa. Before recording the data for each point, extreme care was taken to assure that steadystate conditions were established. Steady-state conditions were declared when the variation in the time-averaged values of both the mass flux (300 kg/m2 s) and pressure varied less than 6% for about 200 s. In this experiment, for a given inlet subcooling, the power was increased in small steps till sustained flow oscillations are observed. It is necessary to allow enough time between successive increments in order to observe the true nature of the flow. The average amplitude of the flow variation at each applied power is recorder over a time interval of 200 s. The experimental conditions are shown in Table 1.

3 Results and Discussions In the first step of the experiments, the linear instability thresholds are found using the conventional technique. The typical approach to identify the instability thresholds is as follows: 1. Fixing all the operating parameters (namely operating pressure, inlet–exit loss coefficients, and flow rate), at a fixed inlet subcooling, the applied power is increased in small steps and the flow behavior is observed (Fig. 2). It is found very difficult to pinpoint the threshold of the instability at a certain power by visual observation of the flow behavior. This is resolved by plotting the average amplitude of the flow at different applied heat fluxes for a given inlet subcooling (say Nsub = 2.93) (Fig. 3). 2. In a typical power versus amplitude plot Fig. 3, two distinct variations in the oscillation amplitudes can be observed. These two distinct variations are fitted with two linear curves (shown by black dotted lines), and the point at which the two curves cross each other is noted as the instability threshold.

Does the Criteria of Instability Thresholds During Density Wave … i

sub

= 22.4K

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G=302[kg/m s] P =653[kPa] q"=38[kW/m ] T

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G=302[kg/m s] P i=646[kPa] q"=29[kW/m ] T sub = 22.4K

2

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Gi [kg/m 2 s]

Gi [kg/m 2 s]

G=302[kg/m2 s] P =646[kPa] q"=29[kW/m2 ] T 400

49

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G=298[kg/m s] P i=653[kPa] q"=35[kW/m ] T sub = 22.5K

320 300 280

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(b)

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Time [s]

(a) Fig. 2 a Oscillations viewed with same axes range. b Oscillations with zoomed view E D

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Fig. 3 Average amplitude of the flow oscillations with applied power

100 Linear stability threshold 50 A

B

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0 30

32

34

36

38

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Npch

Similar experiments are carried out to observe the flow behavior for six different inlet subcoolings at different powers. A total 38 operating conditions are considered which are shown in a non-dimensional parameter plane of Npch − Nsub (Fig. 4a). Following the same procedure as described before, for all inlet subcoolings, the instability thresholds are plotted in Npch − Nsub plane (Fig. 4b).

4 Experimental Nonlinear Stability Behavior Following the rich literature on the numerical study of nonlinear stability behavior of the DWOs, further investigations are done to identify various limit cycle oscillations and characterizing the bifurcation phenomena. To do so, the flow behavior at each applied power is studied for both low and high perturbation on a fixed inlet subcooling

S. Paul et al. 8

8

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Nsub

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2 25

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P=650[kPa], G=300[kg/m 2 s]

Stable

35

Unstable

40

45

Npch

Npch

(a)

(b)

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55

Fig. 4 a Operating points at which the flow behavior is observed. b Stability map in the Npch − Nsub plane showing the linear instability thresholds

Linear stability threshold Unstable limit cycle

2.9 A

B

C

32.0 35.5 38.8

Stable side

D E 40.1 40.3

Unstable side

Fig. 5 Subcritical Hopf

(Nsub = 2.93). One such behavior is explained in Figs. 5, 6, and 7. In this study, low perturbation is considered by applying no external perturbation to the flow, whereas high perturbation is applied by increasing and decreasing the pump speed suddenly, thus mimicking a sudden change in the flow rate. It is observed from Figs. 6 and 7 that the flow returns to the stable state after exposing to large perturbations at points A and B. The flow at point C is stable with no external perturbation applied. However, when a large perturbation is applied at

20

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P i=651[kPa] q"=31.8[kW/m 2 ] T s ub = 10.0K 600 400 200 0

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G i [kg/m 2 s]

G i [kg/m 2 s]

Does the Criteria of Instability Thresholds During Density Wave …

1000

51

P i=654[kPa] q"=28.7[kW/m 2 ] T s u b = 10.2K

500 0 0

10 20 30 40 50 60 P i=653[kPa] q"=31.8[kW/m 2 ] T s u b = 9.9K

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500 0

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] T s ub = 10.0K

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G i [kg/m 2 s]

G i [kg/m 2 s]

G i [kg/m 2 s]

Fig. 6 Case of subcritical Hopf bifurcation: flow behavior with low (left side) and high perturbations (right side) P i=654[kPa] q"=28.7[kW/m 2 ] T s u b = 10.2K 500 0

0

0.15

0.2

P i=653[kPa] q"=36.6[kW/m

0.25 2

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G i [kg/m 2 s] G i [kg/m 2 s] G i [kg/m 2 s]

500

0.2

0.25

500 0

2

P i=652[kPa] q"=35[kW/m ] T s u b = 10.0K

0.15

P i=653[kPa] q"=31.8[kW/m 2 ] T s u b = 9.9K

0.15 0.2 0.25 P i=653[kPa] q"=35[kW/m 2 ] T s u b = 10.0K

500 0

0.15

0.2

0.25

] T s u b = 10.0K

500 0

0.15

0.2

0.25

P i=655[kPa] q"=38.1[kW/m 2 ] T s u b = 10.1K 500 0

0.15

0.2

0.25

Fig. 7 Evolution of the flow variables on the phase plane of pressure drop versus mass flux (ΔP − Gi )

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the point C, the flow does not return to stable state, instead it approaches a large amplitude stable limit cycle. The flow at points D and E is unstable even without any perturbation. This phenomenon can be attributed by the existence of an unstable limit cycle (marked by red dashed line in Fig. 5) at the point C which repels the trajectories toward the large amplitude stable limit cycle (marked by the green solid lines in Fig. 5). Hence, instead of damped oscillations, a constant amplitude oscillation is observed. These characteristics of the flow at this inlet subcooling (Nsub = 2.93) provide sufficient evidence of the occurrence of the subcritical Hopf bifurcation. It is also observed that these characteristics of the flow appear at very narrow region (Fig. 5) between the stable and the unstable side of the stability map and is called as meta-stable region. In addition, the above-mentioned evidence suggests that the operating points A and B are globally stable where the system is stable for any amount of perturbation.

5 Proposed Method to Detect Nonlinear Stability Boundary It should be noted that the linear instability threshold is detected by using the conventional technique by plotting the average amplitude of oscillations with different powers as shown in Fig. 3. The instability threshold thus obtained is only valid for small perturbation in the system. However, with significantly large perturbation, the system can be unstable even before this threshold is shown by the point D in Fig. 6. Since in the real-world applications, one does not have much control on the amount of perturbation, the instability threshold predicted by the conventional technique does not hold good to predict the overall system behavior, and hence the point D should be treated as the onset of instability of the system. Due to the existence of the meta-stable region between the globally stable (point A, and B) and the instability threshold (point C in Fig. 3), the authors postulate that it is improper to find the instability threshold using the approach mentioned in Sect. 3 and hence the authors proposed another approach to predict the instability threshold which can provide global instability limits of the system. The proposed approach to find the nonlinear instability threshold is as follows: • It is worth noting that the proposed method to find the nonlinear stability threshold is similar to the linear stability threshold presented in the previous section with a few additional steps. Allowing sufficient time after increasing the power in small steps to observe the flow behavior, a large perturbation should be applied to the system. Again, allowing sufficient time after applying the perturbation, the average amplitude of the flow variation should be recorded. • Allowing sufficient time after increasing the power, a large perturbation should be applied to the system. Again, allowing sufficient time after applying the perturbation, the average amplitude of the flow variation should be recorded.

Does the Criteria of Instability Thresholds During Density Wave …

3.5

C 100

Nsub

Average Amplitude

4

E D

150

53

Global stability limit

Nonlinear stability limit

3

E A

50 2.5

Globaly stable

A 32

C B Metastable region

D Unstable

B

0 30

Linear stability limit

34

36

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(a)

38

40

42

2 30

32

34

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40

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(b)

Fig. 8 a Average amplitudes with large perturbation showing global stability limit. b Visualization of the meta-stable region

• The amplitude of the flow vs applied power after exposure to a large perturbation should be plotted similar to Fig. 3. The point of intersection between the two distinct variations in the amplitudes should then be treated as the instability threshold as shown in Fig. 8a, b .

6 Conclusions In this work in addition to the usual growing and damped oscillatory behavior of the DWOs, two types of limit cycle oscillations are shown, namely stable limit cycle and unstable limit cycle. The subcritical Hopf bifurcation is observed to appear across the stability threshold. In addition, due to the appearance of the subcritical Hopf bifurcation, a narrow region of meta-stable characteristics is found between the stable and the unstable region. Inside the meta-stable region, dual nature of the system (both stable and unstable) is observed which is primarily determined by the amount of perturbation. Thus, due to the existence of a meta-stable region, a method is proposed to identify the nonlinear stability thresholds. Acknowledgements Funding for this work from the Research Council of Norway under the FRINATEK project 275652 is gratefully acknowledged. The authors also gratefully acknowledge the European Unions Horizon 2020 research and innovation programme to receive funding from the Marie Skodowska-Curie Actions Individual Fellowship grant (Dr. Subhanker Paul) for the project HisTORIC (No 789476).

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References 1. Boure, J., Bergles, A., Tong, L.: Review of two-phase flow instability. Nucl. Eng. Des. 25(2), 165–192 (1973) 2. Kakac, S., Bon, B.: A review of two-phase flow dynamic instabilities in tube boiling systems. Int. J. Heat Mass Transf. 51(02), 399–433 (2008) 3. Stenning, A.H.: Instabilities in the flow of a boiling liquid. J. Basic Eng. 86(2), 213–217 (1964) 4. Rizwan-Uddin: On density-wave oscillations in two-phase flows. Int. J. Multiph. Flow 20(4), 721–737 (1994) 5. O’Neill, L.E., Mudawar, I.: Mechanistic model to predict frequency and amplitude of density wave oscillations in vertical upflow boiling. Int. J. Heat Mass Transf. 123, 143–171 (2018) 6. Rizwan-Uddin, Dorning, J., : Some nonlinear dynamics of a heated channel. Nucl. Eng. Des. 93(1), 1–14 (1986) 7. Liang, N., Shuangquan, S., Tian, C., Yan, Y.: Two-phase flow instabilities in horizontal straight tube evaporator. Appl. Therm. Eng. 31(2), 181–187 (2011) 8. Narayanan, S., Srinivas, B., Pushpavanam, S., Bhallamudi, S.M.: Non-linear dynamics of a two phase flow system in an evaporator: the effects of (i) a time varying pressure drop (ii) an axially varying heat flux. Nucl. Eng. Des. 178(3), 279–294 (1997) 9. Liu, H.T., Kakac, S.: An experimental investigation of thermally induced flow instabilities in a convective boiling upflow system. Wärme - und Stoffübertragung 26(6), 365–376 (1991) 10. Wang, Q., Chen, X., Kakaç, S., Ding, Y.: An experimental investigation of density-wave-type oscillations in a convective boiling upflow system. Int. J. Heat Fluid Flow 15(3), 241–246 (1994) 11. Chiapero, E.M., Doder, D., Fernandino, M., Dorao, C.: Experimental parametric study of the pressure drop characteristic curve in a horizontal boiling channel. Exp. Therm. Fluid Sci. 52, 318–327 (2014) 12. Dorao, C.A.: Effect of inlet pressure and temperature on density wave oscillations in a horizontal channel. Chem. Eng. Sci. 134, 767–773 (2015) 13. Fukuda, K., Kobori, T.: Classification of two-phase flow instability by density wave oscillation model. J. Nucl. Sci. Technol. 16(2), 95–108 (1979) 14. Dokhane, A., Rizwan-Uddin, Chawla, R.: BWR stability and bifurcation analysis using reduced order models and system codes: identification of a subcritical Hopf bifurcation using RAMONA. Ann. Nucl. Energy 34(10), 792–802 (2007) 15. Paul, D., Singh, S., Mishra, S.: Interaction of density wave oscillations and flow maldistribution for two-phase flow boiling parallel channels. Int. J. Therm. Sci. 145, 106026 (2019) 16. Paul, D., Singh, S., Mishra, S.: Impact of system pressure on the characteristics of stability boundary for a single-channel flow boiling system. Nonlinear Dyn. 96, 175–184 (2019) 17. Rahman, M.E., Singh, S.: Flow excursions and pressure drop oscillations in boiling two-phase channel. Int. J. Heat Mass Transf. 138, 647–658 (2019) 18. Rahman, M.E., Singh, S.: Non-linear stability analysis of pressure drop oscillations in a heated channel. Chem. Eng. Sci. 192, 176–186 (2018) 19. Singh, M.P., Singh, S.: Non-linear stability analysis of supercritical carbon dioxide flow in inclined heated channel. Progr. Nucl. Energy 117, 103048 (2019) 20. Singh, M.P., Paul, S., Singh, S.: Development of a novel nodalized reduced order model for stability analysis of supercritical fluid in a heated channel. Int. J. Therm. Sci. 137, 650–664 (2019) 21. Singh, M.P., Emadur, M.E., Singh, S.: Nodalized reduced ordered model for stability analysis of supercritical fluid in heated channel. In: ASME 2018 Power Conference collocated with the ASME 2018 12th International Conference on Energy Sustainability and the ASME 2018 Nuclear Forum, vol. 137, Paper No. POWER2018-7366 (2018) 22. Paul, S., Singh, S.: Linear stability analysis of flow instabilities with a nodalized reduced order model in heated channel. Int. J. Therm. Sci. 98, 312–331 (2015)

Solar Energy for Meeting Service Hot Water Demand in Hotels: Potential and Economic Feasibility in India Niranjan Rao Deevela and Tara C. Kandpal

1 Introduction Hotels are key element in travel and tourism industry. For example, in India, there has been a continued increase in the number of hotels. With a substantial demand for service hot water in these hotels, energy consumption and consequent greenhouse emissions have gradually increased substantially for the same. Harnessing solar energy for meeting the service hot water demand in hotels can contribute significantly toward reduction of fossil fuel consumption and consequent environmental emissions. Large-scale deployment of solar water heating systems (SWHS) in hotels in India would essentially depend upon (i) availability of unshaded space (usually on rooftop) for installation of SWHS, (ii) solar resource assessment, and (iii) financial feasibility of the incremental investment on the SWHS, which would directly depend upon the fraction of total annual useful thermal energy demand for water heating contributed by the SWHS. This study is an attempt to assess the suitability of SWHS in hotels in India from the above-mentioned three perspectives. The solar fraction for SWHS at 27 locations in different climatic zones of the country has been estimated. Also, roof area available with 18 hotels in India for installing solar water heating systems has been estimated. Finally, the levelized (unit) cost of useful thermal energy has been estimated for the SWHS at 27 locations along with the corresponding values for several conventional water heating systems used by hotels.

N. R. Deevela (B) · T. C. Kandpal Center for Energy Studies, Indian Institute of Technology Delhi, New Delhi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_6

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2 Service Hot Water in Star Category Hotels One of the major energy-consuming areas in hotels is service hot water generation. Currently, service hot water is generated through many ways like conventional boilers to the latest alternative options such as heat pumps, waste heat recovery-based systems, and solar water heaters [1]. Majority of the hotels are using conventional boilers to generate service hot water. These boilers are energy-intensive and generate significant carbon emissions. Hotels with piped natural gas (NG) connection are using gas boilers. As per revised guidelines by Ministry of Tourism, hotels are classified under (i) heritage (grand, classic, and heritage); (ii) star category hotels (five-star deluxe and five-star to one-star); (iii) bread and breakfast establishments; (iv) guest house; and (v) apartment hotels. By the end of January 2019, around 1117 approved classified hotels with capacity of 91,140 rooms and 343 unclassified hotels with capacity of 22,604 rooms are available in India [2]. Classified hotels are fully dominated by star category hotels [i.e., 1059 hotels (94.8%) with 89,266 rooms]. Out of star category hotels, 3-star hotels are 374 with 13,160 rooms (14.74%), 4-star hotels are 318 with 16,183 rooms (18.12%), 5-star hotels are 343 with 59,170 rooms (66.28%), and remaining 24 hotels are categorized into 2-star and 1-star hotels with 753 rooms [2]. An attempt was made to collect data available in public domain as well as through a questionnaire-based survey with selected hoteliers (3-, 4-, and 5-star category). From each hotel, data related to the number of rooms, occupancy, energy consumption, average service hot water requirement per person, mode of service hot water generation, temperature of hot water, buildup area, and rooftop area were collected. As mentioned earlier, it was found that hotels are using conventional boilers as well as heat pumps and solar water heaters for service hot water generation. The estimated service hot water requirement of four-star and above category hotels is in the range of 150–220 L/room/day, and for three-star hotels, it is in the range of 100–150 L/room/day. It is worth mentioning that the service hot water consumption among hotels may vary significantly due to climatic conditions, hotel type, and habits of end users. Typical service hot water generating approaches followed in star category hotels are listed in Table 1. Table 1 Some of the service hot water generating approaches followed in star category hotels in India Energy source

Approach(es) based on

Electricity

Heat pumps (air to water, water to water)

Fossil

fuelsa

Renewable energy

Hot water generators/boilers Solar water heaters based on FPC and ETC Biomass boilers using rice husk, fuelwood, wood chips, biomass pellets as feedstocks

Waste heat a Coal,

Waste heat recovery units (often based on HVAC system)

diesel, furnace oil, and natural gas

Solar Energy for Meeting Service Hot Water Demand in Hotels …

57

Table 2 Comparison of some service hot water generating approaches in star category hotels in India Attribute

Water heating approach based on Electric geysers

Fossil fuel boilers

Solar water heaters

Waste heat recovery water heaters

Heat pump water heaters

Biomass boilera

Storage of water

Optional

Essential

Essential

Optional

Essential

Essential

Capital cost

Low

Medium

High

Medium

Very high

Medium

O&M cost

High

High

Low

Medium

Low

High

Useful life (years)

10–15

10

20

10–15

10–15

10

Commercialization status in India

Very good

Very good

Very good

Moderate

Moderate

Very Good

Efficiency (%)/CoP 90–95

70–85

40–60

60–70

3.0–4.5a

60–75

Greenhouse gas emissions

Medium to Very low high

Very low

Medium

Very low

a COP

High

of the heat pump

Most of the hotels in 3–5-star category hotels are using boilers based on fossil fuels such as diesel, furnace oil, and natural gas. Very few hotels are using biomass-based gasifiers (most of them located in the states of Kerala and Karnataka). Some hotels are using SWHS and electric heat pumps as a supplement to the main boiler. Hotels with SWHS use boilers as backup during peak load and cloudy/rainy days. Service hot water is usually supplied for end use between 45 and 50 °C and stored between 50 and 60 °C. Hotels with conventional boilers only supply service hot water at 45 °C. Based on the information compiled from the result of the questionnaire-based survey, a generic comparison of various service hot water generating approaches being used by star category hotels is presented in Table 2.

3 Methodology In view of the substantial climate variations across the country, 27 locations across five climatic zones were selected. While the annual average ambient temperature at these locations varies from 15 to 29 °C, the annual average daily value of Global Horizontal Irradiance (GHI) varies between 4.86 and 5.82 kWh/m2 . In order to assess the availability of adequate roof area for installation of SWHS, the utilizable roof area for 18 hotels at 6 locations (Delhi, Jaipur, Udaipur, Chennai, Bangalore, and Hyderabad) was estimated with the help of Google EarthTM version 7.3.2. Also, the required solar collector area was estimated using RETScreen4® software for the service hot water demand estimated on the basis of the findings of the questionnaire-based survey.

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N. R. Deevela and T. C. Kandpal

The levelized (unit) cost of useful thermal energy delivered by different service hot water generating approaches in hotels has been estimated with due consideration of the capital cost, cost of operation and maintenance, cost of finance, expected useful life of the system as well as the expected annual useful thermal energy delivered. Finally, the values of several measures used for assessing the financial attractiveness of incremental investments in SWHS (discounted payback period, net present values, and internal rate of return) were estimated using standard formulae of engineering economics. The formulae used for estimation of the levelized (unit) cost of useful thermal energy and other measures of financial performance are presented in Appendix 1. The values of various input parameters used in the estimation of potential and financial feasibility of FPC-based and ETC-based SWHS in hotels in India are listed in Table 6 in Appendix 2.

4 Results and Discussion Using the methodology outlined in Sect. 3, calculations have been made to estimate (i) the fraction of service hot water demand that can be met through solar energy; (ii) available roof area with some hotels; (iii) the levelized (unit) cost of useful thermal energy delivered by some of the conventional hot water generating approaches; and (iv) the values of discounted payback period, net present values, and internal rate of return for an incremental investment on SWHS. The collectors used in the SWHS are assumed to be tilted at an angle equal to the latitude of the location and facing south. Based on the results obtained from RETScreen4® software, the collector area required for the locations considered in the study is varying in the range from 258 to 407 m2 for systems based on FPC and from 237 to 373 m2 for systems based on ETC with average solar fraction of 0.55 and 0.65, respectively, for 30,000 lpd system. The values obtained for 27 locations are summarized in Table 4 in Appendix 2. With the help of Google EarthTM version 7.3.2., 18 hotels’ roof area was calculated and it is varying from 578 to 6985 m2 . However, complete available roof area cannot be used for installation of solar water heating systems due to several factors such as shading (approximately accounts in the range of 0.16–0.3 of the roof area) due to the neighboring structures, walls, trees [3–5], and installation of utilities like generators, cooling towers, air conditioning systems, and water tanks. Sample hotel building screenshots are presented in Fig. 1. In this study, as a conservative estimate, a value of 0.3 [6–8] is assumed as utilizable roof area for installation of SWHS in hotels. The details of utilizable roof area for installation of SWHS on sample hotels are presented in Table 5 in Appendix 2. From the analysis, it is observed that, in all of the hotels, utilizable roof area is sufficient to install SWHS. Estimated values of the levelized (unit) cost of useful thermal energy delivered by some of the service hot water generating approaches being followed in hotels in India at selected locations are presented in Table 3.

Solar Energy for Meeting Service Hot Water Demand in Hotels …

59

Fig. 1 Sample pictures obtained from Google EarthTM for roof area measurement

It is worth mentioning that the estimates presented in Table 3 do not consider the effect of any variation in the local price of the fuel and variation in the size/capacity of the system used for meeting service hot water demand in the hotels. It may also be noted from Table 3 that, as expected, LUCte delivered by different hot water generating approaches for meeting the service hot water demand in star category hotels in India varies considerably with the energy source-technology combination. The levelized (unit) cost of useful thermal energy delivered LUCte is much higher for diesel, LPG, furnace oil, natural gas, and other petroleum fuels as compared to the other potential options presented in Table 3. Since the estimated value of LUCte is relatively much lower for heat pump-based system, there is an increasing trend in the country toward their use. Wherever feasible, waste heat recovery-based water heating is likely to be the least cost option. The levelized (unit) cost of useful thermal energy delivered by SWHS based on FPC and ETC is found to vary between 0.85–1.05 Rs./MJ and 0.68–0.80 Rs./MJ, respectively. Measures of financial viability such as discounted payback period, net present value, and internal rate of return for an investment in SWHS at selected locations are presented in Figs. 2, 3, and 4 assuming the saving of diesel, natural gas (NG), LPG, and electricity with the installation of FPC-based and ETC-based

60

N. R. Deevela and T. C. Kandpal

Table 3 Values of input parameters used in the analysis or even in calculations Parameter

Values

Hot water requirement

4-star and above

150 L/day

3-star Service hot water outlet temperature

Parameter

Values

Capital cost of hot water generator (Rs.)

Hot water generator (1 lakh Kcal/h)

1,395,000

125 L/day

Solar water heater

200/L

60 °C

Waste heat recovery (1 lakh Kcal/h)

600,000

heat pump (6 TR/Hr)

320,000

Hot water generator (1 Lakh Kcal/h)

3–15 based on fuel

Solar water heater

4 3

Gross calorific Diesel 11,840 value of each fuel (Kcal/kg or Natural gas 12,000 kWh) Furnace oil 10,050

Cost of fuel (Rs./Kg or kWh)

Maintenance cost of hot water generator (% of capital cost)

Coal

4500

Waste heat recovery (1 lakh Kcal/h)

Biomass

3200

Heat pump (6 TR/h)

LPG

12,500

electricity

860

Diesel

67.1

Efficiency of hot water Generator (%)

Diesel

85

Natural gas

85

Furnace oil

80

Natural gas 48.75

Coal

70

Furnace oil 42

Biomass

70

Coal

4.1

LPG

85

Biomass

6

Electricity

92

LPG

60

Solar energy

44

Electricity

8

Thermal energy

70

Electricity for heat pump

460

Hot water distribution system 10% losses Annual capacity utilization factor

80%

systems. Financial viability of SWHS at two locations in cold climatic zone is also presented in Table 7 in Appendix 2. From the results of financial analysis of SWHS at different locations in India as presented in Figs. 2, 3, and 4, it appears that investment in such systems is financially attractive. For example, for the locations considered in the study, the payback periods of SWHS are found to be very small compared to their expected useful life. At all 27

Solar Energy for Meeting Service Hot Water Demand in Hotels …

61

Fig. 2 Discounted payback period of 30,000 LPD capacity FPC and ETC SWHS under substituting different fuels

Fig. 3 Net present worth of 30,000 LPD capacity FPC and ETC SWHS substituting different fuels

Fig. 4 Internal rate of return (%) of 30,000 LPD capacity FPC and ETC SWHS under substituting different fuels

locations considered in the study, ETC-based SWHS are financially more attractive than FPC-based systems.

62

N. R. Deevela and T. C. Kandpal

Appendix 1: Mathematical Expressions Used for Financial Feasibility Estimation  LUCte =

Co

d(1+d)n (1+d)n −1



+ Com

Aued

(1)

where Co the capital cost of hot water generator (includes auxiliary and installation cost), d the discount rate in fraction, n the useful life of the hot water generator, and Com the annual cost of operation and maintenance of the hot water generator. In case the hot water generation approach consumes fuel, the annual cost of operation would also include the cost of purchasing the fuel and the same can be estimated from the annual useful thermal energy delivered, the calorific value of the fuel used, the efficiency of fuel utilization in the hot water generator, the unit price of fuel used, and Aued represents the annual amount of useful energy delivered. Tdp =

ln(B − Com ) − ln[(B − Com ) − dCo ] ln(1 + d)

(2)

where B is annual monetary benefit accrued as a result of fuel savings achieved with use of solar water heating systems. It is assumed that B is constant throughout the life of the solar water heating systems.   E aq Fup B= (Fcv )(ηb ) 

(3)

where E aq the annual quantity of useful energy delivered, Fup the unit price of the fuel replaced, Fcv the calorific value of the fuel replaced, and ηb the efficiency of existing hot water generator.  NPV = (B − Com )

 (1 + d)n − 1 − Co d(1 + d)n

(4)

Internal rate of return (IRR) is defined as the discount rate at which the net present value (NPV) of the investment is zero. The value of IRR can be estimated from the following equation: 

 (1 + IRR)n − 1 − Co = 0 (B − Com ) IRR(1 + IRR)n

(5)

Solar Energy for Meeting Service Hot Water Demand in Hotels …

Appendix 2: Results See Tables 4, 5, 6, and 7.

63

Vijayawada

Visakhapatnam

Goa

Kolkata

Mumbai

Pune

Guwahati

Agra

10

11

12

13

14

15

16

17

Lucknow

Thiruvananthapuram 0.55

9

Composite

Cochin

8

0.55

0.56

0.56

0.56

0.54

0.53

0.56

0.56

0.53

0.53

0.52

Coimbatore

7

0.55

0.52

0.55

0.56

0.57

0.59

Chennai

18

Evacuated tubular collector

311

322

329

288

278

299

271

278

283

281

285

285

265

294

269

283

299

267

530

541

542

541

475

482

496

487

466

491

500

474

465

473

488

497

531

526

0.65

0.66

0.66

0.65

0.63

0.64

0.66

0.66

0.63

0.65

0.62

0.61

0.65

0.63

0.65

0.66

0.66

0.68

283

294

301

262

253

274

248

253

258

258

260

260

242

269

246

258

271

244

624

639

643

628

561

576

582

573

554

581

587

562

550

565

573

585

621

611

(continued)

Solar fraction Solar collector Annual useful Solar fraction Solar collector Annual useful area (m2 ) thermal energy area (m2 ) thermal energy delivered (GJ) delivered (GJ)

Flat plate collector

Solar water heating systems using

6

Warm and humid Bhubaneswar

Vadodara

4

5

Surat

3

Jodhpur

Udaipur

Hot and dry

1

Location

2

Climatic zone

S. No.

Table 4 Performance of 30,000 LPD capacity SWHS at 27 locations across five climatic zones in India

64 N. R. Deevela and T. C. Kandpal

Jaipur

Hyderabad

Khajuraho

New Delhi

Bangalore

Temperate

Cold

21

22

23

24

25

26

Shimla

Mussoorie

Ludhiana

27

Moradabad

Location

20

Climatic zone

19

S. No.

Table 4 (continued) Evacuated tubular collector

0.58

0.59

0.52

0.57

0.54

0.51

0.59

0.59

0.57

407

386

283

294

304

283

258

322

313

828

780

505

544

517

468

550

603

575

0.65

0.66

0.61

0.67

0.64

0.61

0.67

0.69

0.67

373

352

260

269

278

258

237

294

285

931

879

594

635

612

555

634

700

668

Solar fraction Solar collector Annual useful Solar fraction Solar collector Annual useful area (m2 ) thermal energy area (m2 ) thermal energy delivered (GJ) delivered (GJ)

Flat plate collector

Solar water heating systems using

Solar Energy for Meeting Service Hot Water Demand in Hotels … 65

66

N. R. Deevela and T. C. Kandpal

Table 5 Solar collector area required and utilizable roof area available with few hotels for installation of solar water heating system Start category

Number of rooms

Location

Hot water demand (lpd)

Solar collector area required (m2 ) FPC

3

87

4

141

4

72

4

58

5

Utilizable roof area(m2 )

ETC

Hyderabad

10,875

103

93

383

Jaipur

21,150

182

167

173

10,800

93

85

431

8700

85

78

211

119

17,850

175

160

376

5

523

78,450

770

704

2096

5

261

39,150

384

351

982

5

216

32,400

318

291

779

5

403

60,450

593

542

924

5

250

37,500

368

336

924

5

87

Jaipur

13,050

112

103

861

5

211

31,650

272

250

1245

5

141

Udaipur

21,150

211

191

1519

5

171

Chennai

25,650

227

207

446

4

85

12,750

113

103

398

3

42

5250

46

42

239

3

104

13,000

123

117

362

5

115

17,250

163

155

256

New Delhi

Bangalore

Table 6 Estimated value of levelized (unit) cost of useful thermal energy for different energy resource technologies for a 30,000 lpd system Energy source

Technology

Levelized (unit) cost of useful thermal energy (Rs./MJ) Delhi

Diesel

Srinagar

Bangalore

Udaipur

1.741

1.792

1.744

1.772

1.767

1.473

1.516

1.475

1.498

1.494

Furnace oil

1.448

1.517

1.452

1.489

1.483

Natural gas

1.265

1.308

1.268

1.291

1.287

Coal

0.475

0.514

0.479

0.493

0.488

LPG

Boiler (hot water generator)

Chennai

Electricity

Heat pump

0.606

0.608

0.609

0.595

0.592

Electricity and waste heat

Hybrid heat pump

0.298

0.415

0.305

0.367

0.357

Waste heat at 120°C

Waste heat recovery

0.062

0.063

0.064

0.057

0.055

5.77

4.30

Discounted payback period (years)

Net present worth (million Rs.)

Mussoorie

1.92

9.27

3.26

6.89

10.35

2.99

Electricity (FPC)

33

5.80

3.71

33

5.47

3.71

5.47

3.71

ETC LPG (FPC)

41

11.03

2.96

40

10.35

2.99

Diesel (ETC)

NG (FPC)

20

3.51

6.81

20

3.26

6.89

Diesel (FPC)

16

2.08

9.14

16

1.92

9.27

FPC

23

Internal rate of return (%)

Parameter

4.61

Net present worth (million Rs.)

Location

5.71

23

Internal rate of return (%)

Discounted payback period (years)

4.30

Net present worth (million Rs.)

Shimla

5.77

Discounted payback period (years)

Mussoorie

ETC Electricity (FPC)

Diesel (ETC)

LPG (FPC)

Diesel (FPC)

NG (FPC)

FPC

Parameter

Location

Table 7 Values of financial parameters of two different locations in cold climatic zone

3.26

5.46

NG (ETC)

19

1.68

7.04

24

3.26

5.46

NG (ETC)

4.69

4.31

LPG (ETC)

24

2.67

5.42

29

4.69

4.31

LPG (ETC)

12.24

2.05

(continued)

Electricity (ETC)

47

7.88

2.48

56

12.24

2.05

Electricity (ETC)

Solar Energy for Meeting Service Hot Water Demand in Hotels … 67

Shimla

Location

5.71

4.61

23

Net present worth (million Rs.)

Internal rate of return (%)

23

16

2.08

9.14

16

20

3.51

6.81

20

41

11.03

2.96

40

33

5.80

3.71

33

ETC Electricity (FPC)

Diesel (ETC)

LPG (FPC)

Diesel (FPC)

NG (FPC)

FPC

Discounted payback period (years)

Internal rate of return (%)

Parameter

Table 7 (continued)

19

1.68

7.04

24

NG (ETC)

24

2.67

5.42

29

LPG (ETC)

47

7.88

2.48

56

Electricity (ETC)

68 N. R. Deevela and T. C. Kandpal

Solar Energy for Meeting Service Hot Water Demand in Hotels …

69

References 1. Wang, W., Guo, P., Zhang, H., Yang, W., Mei, S.: Comprehensive review on the development of SAHP for domestic hot water. Renew. Sustain. Energy Rev. 72, 871–881 (2017) 2. GOI, Government of India: Ministry of Tourism, Classification, approval and occupancy of hotels, New Delhi (2019). https://hotelcloud.nic.in/MOT/AllindiaRpt.aspx. Last accessed 2 Feb 2019 3. Khanna, R.K., Rathore, R.S., Sharma, C.: Solar still an appropriate technology for potable water need of remote villages of desert state of India -Rajasthan. Desalination 220, 645–653 (2008) 4. Nguyen, H.T., Pearce, J.M.: Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale. Sol. Energy 86, 1245–1260 (2012) 5. Izquierdo, S., Rodrigues, M., Fueyo, N.: A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations. Sol. Energy 82, 929–939 (2008) 6. Pillai, I.R., Rangan Banerjee, R.: Methodology for estimation of potential for solar water heating in a target area. Sol. Energy 81, 162–172 (2007) 7. Singh, R., Banerjee, R.: Estimation of Roof-top Photovoltaic Potential Using Satellite Imagery and GIS. IEEE (2013). 978-1-4799-3299-3/13 8. Singh, R., Banerjee, R.: Estimation of rooftop solar photovoltaic potential of a city. Sol. Energy 115, 589–602 (2015)

Techno-economic Feasibility of Condenser Cooling Options for Solar Thermal Power Plants in India Tarun Kumar Aseri , Chandan Sharma , and Tara C. Kandpal

1 Introduction Increasing climate change concerns and uncertainty about the price and availability of fossil fuels have generated significant interest in renewable energy-based electricity generation options [1]. Solar power generation is being promoted across the globe as an environmentally sustainable renewable energy option [2]. High annual direct normal irradiance (DNI), large land area (usually wastelands) and sufficient water availability are the primary conditions that needs to be evaluated before deployment of a solar thermal power plant [3, 4]. The first two conditions are more likely to prevail in arid regions. However, the arid regions may not always have adequate water availability due to little or negligible rainfall [5, 6]. At such locations, a solar thermal power plant with wet cooling technology may not be feasible as the same requires substantial amount of water (3.5–4.0 m3 /h per MW) for condenser cooling purpose in the power block [7]. Hence, it is imperative to explore and consider alternative condenser cooling options that are water conservative. One of the possible approaches to reduce the water requirement in solar thermal plants is to use the dry cooling technology [8], sometimes also referred to as “air-cooling system” or “air-cooled condenser” (ACC). Another water conservative condenser cooling option that partially combines the desirable features and characteristics of both wet and dry cooling is hybrid cooling technology [9]. However, the use of dry cooling or hybrid cooling technology in place of the wet cooling technology has its own implications on techno-economic performance and other relevant aspects of solar thermal power plants. In the present study, an attempt has been made T. K. Aseri (B) · T. C. Kandpal Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi, India e-mail: [email protected] C. Sharma Mechanical Engineering Department, Engineering College, Ajmer, Rajasthan, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_7

71

72

T. K. Aseri et al.

Fig. 1 Schematic of a solar thermal power plant with two-tank (indirect) thermal energy storage

to assess techno-economics of a 50 MW nominal capacity parabolic trough collector (PTC) based plant with different condenser cooling technologies at four potential locations in India.

2 Solar Thermal Power Plant A schematic of a solar thermal power plant with two-tank (indirect) thermal energy storage is shown in Fig. 1. A solar thermal power plant can be divided into three systems, namely solar energy collection system, thermal energy transfer system and power generation system [10]. Solar energy collection system comprises of an array of solar collectors that are continuously tracked. The heat collected by the solar energy collection system can be either transferred to a power generation system or can be stored to generate electricity beyond sunshine hours or during periods of intermittent sunlight. Steam obtained from the thermal energy transfer system is expanded in a turbine (usually in a Rankine cycle) in power generation system. The turbine exhaust steam is condensed and converted into the water using a condenser with the help of cooling technology. In the condenser, the heat is transferred to the available cooling medium.

3 Techno-economic Feasibility Analysis of Different Condenser Cooling Technologies A techno-economic feasibility assessment of different available cooling technologies for a 50 MW PTC based solar thermal power plant (without thermal energy storage) at four potential locations in India has been undertaken. The analysis has

Techno-economic Feasibility of Condenser Cooling Options …

73

been undertaken using System Advisor Model (SAM), a software developed by a National Renewable Energy Laboratory, USA [11].

3.1 Technical and Economic Parameters The details of locations selected for the analysis, their ambient conditions and corresponding annual DNI availability are presented in Table 1. The National Solar Radiation Database (NSRDB) has been used for weather and solar irradiance data [12]. For reference, the technical data of an operational 50 MW wet-cooled PTC based solar thermal power plant (Megha solar plant) located at Anantapur, Andhra Pradesh, India (Table 2) has been considered [13]. The design parameters for wet-cooled, dry-cooled and hybrid-cooled power block are presented in Table 3. To reduce the annual water requirements by 50% in a solar thermal power plant, a technology with 50% hybridization of wet cooling and dry cooling technologies in parallel mode has also been considered. The dry cooling is relatively less effective than the wet cooling leading to a significant reduction in thermal to electric conversion efficiency. Furthermore, the dry-cooled plant delivers less net electricity due to its higher parasite requirements. To compensate this, more collector area needs to be installed for the plant with the same nominal capacity (Table 2). Table 1 Ambient conditions and annual DNI availability of the locations selected for the analysis [12] Location, State

Latitude (°N)

Longitude (°E)

Annual average dry-bulb temperature (°C)

Annual DNI (kWh/m2 )

Kutch, Gujarat

22.58

69.66

28.29

1909

Jaisalmer, Rajasthan

26.91

70.95

28.65

1883

Nashik, Maharashtra

20.00

73.79

25.55

1908

Mandsaur, Madhya Pradesh

24.09

75.25

26.50

1843

Table 2 Design parameters used in the analysis of a 50 MW PTC based solar thermal power plant Parameter

Unit

Value

Nominal capacity

MW

50

Parabolic trough collector

–

AlbiasaTrough AT150

Heat collection element

–

Siemens UVAC 2010

Heat transfer fluid

–

Therminol VP-1

Design value of DNI

W/m2

700

Solar collector area (for wet- and hybrid-cooled plants)

m2

366,240

Solar collector area (for dry-cooled plant)

m2

392,400

74

T. K. Aseri et al.

Table 3 Design parameters of the power blocks used in the analysis Parameter

Unit

Condenser cooling technology Wet/Hybrid

Dry 32.1

Rated power block efficiency

%

34.2

Ambient temperature at design condition

–

Annual average of the location

Boiler operating pressure

bar

100

100

Turbine inlet temperature

°C

373

373

Initial temperature difference at design condition

°C

–

18

Cooling tower range

°C

10

–

Cooling tower approach

°C

5

–

Condenser terminal temperature difference

°C

3

–

In the condenser cooling system, several parameters affect the performance of cooling tower considerably as they decide the operating pressure and temperature of condenser. The same is expected to affect the efficiency of power cycle. These parameters include approach temperature, temperature range, initial temperature difference and condenser terminal temperature difference [14]. The approach temperature represents the temperature difference between the circulating water at the condenser inlet (or cooling tower outlet) and the ambient temperature of the surrounding. In case of wet cooling technology, the wet-bulb temperature is the surrounding temperature and dry-bulb temperature will act as a surrounding temperature in case of dry cooling technology. The temperature range of any cooling tower is temperature gain by the circulating cooling water across the condenser. The terminal temperature difference is the temperature difference between the steam inlet temperature and outlet temperature of circulating water at the condenser. The sum of all three temperatures is known as initial temperature difference. The initial temperature difference is widely used in dry-cooled plant. A dry-cooled solar thermal plant is reportedly 4–10% [15, 16] costlier as compared to a wet-cooled plant owing to its lower power block efficiency and higher parasitic requirements. Since capital cost of a 50 MW dry-cooled PTC based plant was not available, it is assumed (on the conservative side) that dry-cooled solar thermal power plant shall be 10% costlier (INR 18.66 crore per MW) than wet-cooled solar thermal power plant (i.e., INR 16.96 crore per MW) [13, 17]. Further, in hybrid cooling, the size of the dry section is relatively smaller in comparison to plant with only dry cooling technology. Accordingly, the capital cost of the hybrid-cooled plant has been assumed as INR 17.8 crore per MW. The cost of water is not considered in the study. The annual electricity output and water requirements have been obtained from SAM. To analyse the cumulative effect of annual electricity output and capital cost of the plant with various condenser cooling technologies, the financial metric, levelized cost of electricity (LCOE) can be estimated using following expression:

Techno-economic Feasibility of Condenser Cooling Options …

LCOE =

 Capital cost ×

d(1+d)n (1+d)n −1



75

+ Annual O&M cost

Net annual electricity output

where d represents the discount rate and n the useful life of the plant. In the present study, a discount rate of 10%, a useful life of 25 years and annual operation and maintenance (O&M) cost of 2% of the capital cost have been assumed.

3.2 Results Figure 2 presents the variation in annual electricity output of PTC based plants for the three cooling technologies at the selected locations. Due to decrease in power block efficiency and increase in parasitic consumption, the plants with dry and hybrid cooling technologies generate approximately 5% and 3% less annual electricity, respectively, in comparison to the plants with the wet cooling technology. This can be attributed to the fact that efficiency of any power cycle depends on magnitude of the temperature gradient between source and sink. In a wet-cooled plant, the sink temperature is wet-bulb temperature of ambient air whereas dry-bulb temperature shall be the sink temperature in dry-cooled plant. The dry-bulb temperature is significantly higher than the wet-bulb temperature resulting in reduced temperature gradient between source and sink, and hence, a significant reduction in power cycle efficiency is observed. The monthly variation in electricity output and annual parasitic requirements for a plant with wet, dry and hybrid cooling technologies for the location of Kutch, Gujarat, is shown in Figs. 3 and 4, respectively. As expected, the monthly electricity output follows the trend of available direct normal irradiation at

Annual electricity output (GWh)

104 Wet

102

Hybrid

Dry

100 98 96 94 92 90 88 86 84 Kutch

Jaisalmer

Nashik

Mandsaur

Fig. 2 Annual electricity output of a 50 MW PTC based plant with three different condenser cooling options at the selected locations

76

T. K. Aseri et al.

Net Electricity output (GWh)

275 10.0 225

8.0 Wet Dry Hybrid DNI

6.0 4.0 2.0

175 125

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Direct Normal Irradiance (W/m²)

12.0

75

Fig. 3 Estimated values of monthly net electricity output from a PTC based solar thermal power plant with different condenser cooling technologies at Kutch, Gujarat 60%

Parasitic load (%)

Wet

Dry

Hybrid

50% 40% 30% 20% 10% 0% Boiler feed pump

Cooling system

Solar field

HTF pump

Fixed load of the plant

Fig. 4 Annual parasitic consumption in different activities for a 50 MW PTC based solar thermal power plant at Kutch, Gujarat

the location (Fig. 3). The parasitic consumption in components other than cooling technology (such as boiler feed pump, solar system and HTF pump) does not have significant effect of cooling technology used in plant. The total parasitic load per year for wet-cooled, dry-cooled and hybrid-cooled plant is estimated to be in the range of 7–8%, 12–13% and 9–10%, respectively, of total electricity delivered at four potential locations. From the result obtained, it is also observed that annual power requirements to operate condenser cooling technologies (wet/dry/hybrid) are in the range of 40–60% of the total parasitic load. A comparison of monthly water requirements for the three condenser cooling technologies along with corresponding saving (compared to wet cooling) in water requirements is presented in Table 4. It is observed that a dry-cooled plant requires 97% less water annually as compared to a wet-cooled plant. Considering the water requirements for mirror washing for dry-cooled (16,151 m3 per year for dry and hybrid plants), the overall water saving for dry-cooled and hybrid-cooled plants were observed to be 94% and 48%, respectively.

Techno-economic Feasibility of Condenser Cooling Options …

77

Table 4 Monthly water consumption and water saving for a 50 MW PTC based plant in Jaisalmer, Rajasthan Month

Monthly amount of water requirement by the power block (m3 )

Monthly saving in water requirement (%)

Wet-cooled

Wet → dry

Wet → Hybrid

677

13,427

97.5

50.9

33,490

874

16,663

97.4

50.2

46,678

1224

23,422

97.4

49.8

April

52,770

1410

26,706

97.3

49.4

May

56,924

1523

28,895

97.3

49.2

June

54,575

1462

27,675

97.3

49.3

January

27,395

February March

Dry-cooled

Hybrid-cooled

July

52,089

1380

26,246

97.4

49.6

August

39,554

1031

19,827

97.4

49.9

September

42,583

1135

21,476

97.3

49.6

October

42,555

1108

21,355

97.4

49.8

November

34,184

840

16,918

97.5

50.5

December

28,719

677

14,025

97.6

51.2

511,516

13,341

256,635

97.2

49.8

Annual water requirements

The effect of the choice of condenser cooling technology on the LCOE is shown in Fig. 5. Among the three condenser cooling options, LCOE of a dry-cooled plant is observed to be highest due to comparatively lower electricity output and higher capital cost. The LCOE of the dry-cooled plant is around 16% higher than that of a wet-cooled plant. Further, LCOE of the hybrid-cooled plant is approximately 8% higher than that of a wet-cooled plant but 7% lower than that of a dry-cooled

LCOE (INR/kWh)

14.0

Wet

Hybrid

Dry

12.0

10.0

8.0

6.0 Kutch

Jaisalmer

Nashik

Fig. 5 Effect of the choice of condenser cooling technology on LCOE

Mandsaur

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T. K. Aseri et al.

plant. For example, at Kutch, the LCOE for a wet-cooled plant is estimated as INR 10.8 per kWh, whereas the same for a dry-cooled plant is INR 12.5 per kWh and for a hybrid-cooled plant is INR 11.6 per kWh.

4 Concluding Remarks An attempt has been made to study the techno-economics feasibility of three condenser cooling options for parabolic trough collector based solar thermal power plants in India. It was observed that cost of electricity delivery with dry cooling option is expected to increase by 15.7% (from INR 10.8 per kWh for wet-cooled plant to INR 12.5 per kWh for dry-cooled plant at Kutch). However, the same also resulted in 94% saving in water requirement. The results obtained for hybrid-cooled plant shows relatively less penalty (in terms of performance and hence LCOE) than that with dry-cooled plant. It is also worth mentioning that there is reasonably high penalty (in terms of LCOE) of using dry cooling in a solar thermal power plant (as against a wet-cooled plant). However, as most of the locations suitable for solar thermal power generation are likely to be in arid areas, dry cooling would have to be adopted. This is likely to adversely affect the competitiveness of solar thermal power plants as against PV plants. Only with desirable success in cost reduction and integration of thermal storage, there could be some improvement in the competitiveness of solar thermal power generation.

References 1. Edenhofer, O., Madruga, R.P., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., et al.: Renewable energy sources and climate change mitigation: special report of the intergovernmental panel on climate change (2015). https://doi.org/10.1017/cbo9781139151153 2. U.S. Department of Energy (DOE): Concentrating Solar Power Commercial Application Study : Reducing Water Consumption of Concentrating Solar Power Electricity Generation. Report to Congress, Washington DC (2009) 3. Sundaray, S., Kandpal, T.C.: Preliminary feasibility evaluation of solar thermal power generation in India. Int. J. Sustain. Energy 33, 461–469 (2014). https://doi.org/10.1080/14786451. 2013.770395 4. Sharma, C., Sharma, A.K., Mullick, S.C., Kandpal, T.C.: Assessment of solar thermal power generation potential in India. Renew. Sustain. Energy Rev. 42, 902–912 (2015). https://doi.org/ 10.1016/j.rser.2014.10.059 5. Xu, X., Vignarooban, K., Xu, B., Hsu, K., Kannan, A.M.: Prospects and problems of concentrating solar power technologies for power generation in the desert regions. Renew. Sustain. Energy Rev. 53, 1106–1131 (2016). https://doi.org/10.1016/j.rser.2015.09.015 6. Aseri, T.K., Sharma, C., Kandpal, T.C.: Assessment of water availability for wet cooling at potential locations for solar thermal power generation in India. Int. J. Ambient Energy 1–16 (2018). https://doi.org/10.1080/01430750.2018.1507926

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7. CEA: Report on Minimisation of Water Requirement in Coal Based Thermal Power Stations. Central Electricity Authority, New Delhi, India, pp. 1–52 (2012). Homepage, http://www.cea. nic.in/reports/others/thermal/tetd/min_ofwater_coal_power.pdf. Last accessed 4 Mar 2018 8. Wagner, M.J., Kutscher, C.: The impact of hybrid wet/dry cooling on concentrating solar power plant performance. In: Proceedings of 4th International Conference on Energy Sustainability. ES2010, Arizona, USA, pp. 1–8 (2010) 9. Hu, H., Li, Z., Jiang, Y., Du, X.: Thermodynamic characteristics of thermal power plant with hybrid (dry/wet) cooling system. Energy 147, 729–741 (2018). https://doi.org/10.1016/j.ene rgy.2018.01.074 10. Praveen, R.P., Baseer, M.A., Awan, A.B., Zubair, M.: Performance analysis and optimization of a parabolic trough solar power plant in the Middle East region. Energies 11(4), 741 (2018). https://doi.org/10.3390/en11040741 11. SAM-2018. System Advisor Model. Version, 2018.11.11. National Renewable Energy Laboratory, Alliance for Sustainable Energy, LLC for Department of Energy, USA (2018). Homepage https://sam.nrel.gov/download. Last accessed 25 Dec 2018 12. NREL-NSRDB. The National Solar Radiation Database (NSRDB). National Renewable Energy Laboratory, USA (2018). Homepage https://nsrdb.nrel.gov/. Last accessed 21 Nov 2018 13. SolarPACES: Concentrating Solar Power Projects. National Renewable Energy Laboratory (2018). Homepage https://solarpaces.nrel.gov/. Last accessed 12 July 2018 14. EPRI: Comparison of Alternate Cooling Technologies for U.S. Power Plants: Economic, Environmental, and Other Tradeoffs. Electric Power Research Institute, California, USA (2002) 15. Poullikkas, A., Hadjipaschalis, I., Kourtis, G.: A comparative overview of wet and dry cooling systems for Rankine cycle based CSP plants. Trends Heat Mass Transf. 13, 27–50 (2013) 16. Turchi, C.: Parabolic Trough Reference Plant for Cost Modeling with the Solar Advisor Model (SAM), TP550-47605. National Renewable Energy Laboratory, pp. 1–112 (2010). www.nrel. gov/docs/fy10osti/47605.pdf 17. UNFCCC-CDM: Project Design Document Form (CDM PDD): Solar Thermal Power Plant by Godawari Green Energy Limited, Project 7379, pp. 1–8 (2012). https://cdm.unfccc.int/Pro jects/DB/KBS_Cert1348206450.84/view. Last accessed 20 Apr 2018

Optical Modeling of Parabolic Trough Solar Collector Anish Malan and K. Ravi Kumar

1 Introduction Parabolic tough solar collector (PTSC) is one of the most proven commercially available concentrated solar collectors to harness the energy from sun [1]. It is a line focus collector consists of a reflector, receiver and tracing system. Reflector concentrates the solar radiation on the absorber tube placed at the focal axis of the collector. The levelised cost of electricity (LCoE) with commercially available PTSC is ~0.20 e/kWh [2]. The LCoE is not attractive as compared to other conventional and renewable energy power generation systems and there is need to mitigate the capital cost to make it more economical. IRENA, 2012 addresses the avenues to decrease the cost of the PTSC [3]. It is reported that the cost of solar field which is nearly 52% of total capital cost of the plant can be reduced by diminishing the cost of different segments of the PTSC field like support structure, foundation, reflectors and receivers. It is expected that the LCoE may be reduced to 0.13 e/kWh by 2020 [4]. The performance of the PTSC is evaluated mainly based on three aspects, i.e., optical, thermal and structural analysis. Out of three aspects, optical analysis is the most important among them because the result of the optical analysis is used as an input for the thermal analysis of the PTSC. From engineering point of view, the measurement of flux in real-time condition is very complex, so mainly the analytical method is used for the optical analysis of the PTSC [5]. The researches have considered various approaches to study the flux distribution on the absorber surface of the PTSC such as analytical formulation [6, 7], flux mapping [8], photogrammetry [9, 10] and ray tracing techniques like MCRT [11–18], finite volume method [19, 20], inverse MCRT [18], and reverse ray tracing [21]. The optical software has also been used by the researchers such as Zemax [22] and SolTrace [23, 24], etc. Most of the A. Malan · K. R. Kumar (B) Indian Institute of Technology Delhi, New Delhi 110016, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_8

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studies for the flux distribution are performed by considering sun as a uniform source but in real-time condition, the intensity of the incoming solar radiation is greater at center and falls toward the limbs. This effect is also known as limb darkening effect. Jose provided the analytical formulation for the flux distribution in the incoming solar cone including limb darkening effect [25]. The optical analysis of the PTSC including limb darkening effect provides more realistic results. To reduce the computation time, the complete PTSC problem is converted into two-dimensional model. It helps to provide the results with same accuracy in short time period. In this study, the focus is to provide the flux distribution including limb darkening effect. For this study, a model is developed for the flux distribution on the absorber surface of a PTSC using MATLAB [26].

2 Methodology The schematic of ideal PTSC highlighting the important parameters like aperture, rim angle, focal length, receiver and the incoming and reflected solar radiation with sun subtended angle of 32 is shown in Fig. 1. Receiver consists of the absorber tube and glass cover placed at the focal axis of the PTSC. Glass cover is provided to reduce the convective and radiative losses from the absorber surface. The methodology used in this work is based on certain assumption such as (a) the parabola is perfect and continuous, (b) effect of the glass cover is not considered, (c) analysis is carried out at the middle section of the PTSC, and (d) the tracking system is perfect and continuous. The algorithm used for the flux distribution analysis is shown in Fig. 2. The program is divided into two parts based on the rays reached the absorber surface:

32ʹ

32ʹ

Receiver

Rim angle

Aperture

Focal length

Fig. 1 A schematic of the parabolic trough solar collector

Optical Modeling of Parabolic Trough Solar Collector Fig. 2 Flow chart of the algorithm used for the flux distribution analysis

83

Start Definition of geometry Sun source model Initialization photon distribution

Y

Shadowed by absorber?

N Reach concentrator surface Reflected from concentrator Conical scattering of ray into child rays Energy distribution into child rays Child rays hit on absorber surface

Rays absorbed on absorber Identification of Rays hitting position Count photon distribution

End

i. Rays that directly reach the absorber surface ii. Rays that first reflected form the concentrator and then reach the absorber surface. The rays are differentiated based on the size of the absorber surface such as if the ray originated point is within the size of the absorber, then it directly absorbed on the absorber surface and if not, then it will be absorbed after reflected from concentrator.

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Ro Perpendicular plane Fig. 3 Schematics of the intersection of the reflected ray on a perpendicular plane

The limb darkening effect is considered in the incoming radiation from the sun using the below formulation [24]:  Ro + 1.5641 Ro2 − r 2 Io I = 2.5641Ro Io =

Energy of the incoming ray 2.5027 × Ro2

(1) (2)

where I is the intensity, I o is the intensity at the center of the image, Ro is image radius of the solar radiation on the perpendicular plane and r is arbitrary radius of the reflected radiation image. The ray is divided in the number of child rays forming the complete ray with each ray at the edge of the arbitrary radius. The schematics of the intersection of reflected solar cone are shown in Fig. 3. The energy distribution for various radial diameters is calculated by selecting the circular ring of interest. Energy in outmost circular element (r = Ro ): Energy =

  1 Io × π × R02 − r12 2.5641

(3)

The energy in the preceding circular element:

Energy =

 Ro + 1.5641 R02 − r 2 2.5641R0

  Io × π × r12 − r22

(4)

where r 1 , r 2 , … is the internal radii of the circular image. The above formulation can only be used if all reflected ray falls on one perpendicular plane but in case of the circular receiver, the perpendicular plane for each child ray is different. Therefore, for each child ray, a different perpendicular plane has to be considered as shown in Fig. 4 (for representation, only two planes are considered).

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Fig. 4 Schematics of projection of child rays on perpendicular planes

For the representation point of view, only ten child rays have been considered but in actual model, 200 numbers of child rays are used. If plane 1 is considered the point of interest for this particular plane is C 1 , energy is calculated for the elemental ring between C 1 and C 2 and provided to the region between C 1 and C 2  . Similarly, for plane 2, R o is image radius of the solar radiation and point of interest is C 2  , energy is calculated for the elemental ring between C 2  and C 3  and so on. Once the energy distribution for one parent ray is known, then it is converted to the flux/LCR, and at the end of the simulation, it is summed to get the distribution of LCR on the absorber surface.

3 Results and Discussion The results are illustrated in terms of local concentration ratio (LCR). It is defined as ratio of the solar flux at a point on the absorber to the solar flux incident on the aperture of the PTSC. The developed model is compared with the results provided in the Jeter model [6] for 4.5 mrad sun shape, 90° rim angle, 5.77 m aperture and 100% intercept factor as shown in Fig. 5. The Jeter [6] presented results by considering sun as a uniform source. Hence, for validation point of view, results are compared for the same. There is significant difference in LCR if sun is considered including

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A. Malan and K. R. Kumar 160

Jeter [6]

140

Present model

120 LCR

100 0⁰

80 60

270⁰

90⁰

40

180⁰

20 0 0

40

80 120 160 200 240 280 Circumference of the absorber (deg)

320

360

Fig. 5 Comparison of the LCR of present model with Jeter model [6]

limb darkening effect. Hence, sun including limb darkening results should be used for the optical analysis of the PTSC model. All the subsequent results are obtained considering sun with limb darkening effect. The effect of change of aperture width on the LCR for same geometrical concentration ratio (GCR) is shown in Fig. 6. GCR is defined as the ratio aperture area to the absorber area (π * absorber diameter * length of the collector). The results are obtained for 26.23 GCR (which is in the case of the commercially available euro trough collector), 4.5 mrad sun shape, 80° rim angle and 100% intercept factor. There is no effect in the LCR for same GCR on the lower half of the absorber but for small aperture, little variation in there in upper half of the absorber due to few rays falls directly on the absorber. The effect of rim angle () on the LCR for 5.77 m aperture, 100% intercept factor and 4.5 mrad sun shape is shown in Fig. 7. The rim angle is varied from 70° to 120° in step size of 10°. The LCR is more concentrated in lower half of the receiver for Aperture = 2 m

60

Aperture = 5 m

LCR

50

Aperture = 5.77 m

40

Aperture = 7.5 m

30

Aperture = 10 m

20 10 0 0

40

80

120 160 200 240 280 Circumference of the absorber (deg)

Fig. 6 Effect of variation of aperture width on the LCR for same GCR

320

360

Optical Modeling of Parabolic Trough Solar Collector 180

Ψ = 70°

160

Ψ = 80°

140

Ψ = 90°

120 LCR

87

Ψ = 100°

100

Ψ = 110°

80

Ψ = 120°

60 40 20 0 0

40

80 120 160 200 240 280 Circumference of the absorber (deg)

320

360

Fig. 7 Effect of change of rim angle on the LCR

lower rim angle and unformitivity improves toward the larger rim angle. But with increase of the rim angle, the surface area of the concentrator also increases that results in more material cost and structural load. Errors in the manufacturing of the PTSC play significant effect in the flux distribution as shown in Fig. 8. The error can be in form of slope error, tracking errors, misalignment error, etc. The effect of the error is considered in widening of the reflected solar radiation cone itself by adding it into the half of the sun subtended angle, i.e., 4.5 mrad. The results are obtained for the increasing the error from 0 to 8 mrad in step size of 2 mrad. The LCR become more uniform with increase in errors, but these errors are not intentionally introduced in the manufacturing. These errors are unavoidable due to the lack of the robustness in the manufacturing. The results are obtained for 5.77 m aperture, 80° rim angle and 100% intercept factor. The error influences the absorber size of the PTSC; to have a better intercept factor, the receiver size has to be made in accordance with the size of the reflected 160 Error = 0 mrad Error = 2 mrad Error = 4 mrad Error = 6 mrad Error = 8 mrad

140 120 LCR

100 80 60 40 20 0 0

40

80 120 160 200 240 280 Circumference of the absorber (deg)

Fig. 8 Effect of error in the manufacturing on the LCR

320

360

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solar radiation. However, with lager diameter of the absorber, the thermal heat losses also increase which decreases the overall performance of the PTSC.

4 Conclusions The work focuses on the flux distribution of the PTSC by considering limb darkening effect in the solar radiation. The two-dimensional model is developed to decrease the computation time of the CPU without compromising with the accuracy in the results. If the GCR is same, there is no effect in the LCR irrespective of the aperture size. The consideration of the limb darkening effect in the sun shape provides more realistic results. There is significant difference in the LCR if sun is considered as a uniform source and including limb darkening effect. The LCR distribution become more uniform with increase of the rim angle but also the receiver size and the surface area of the concentrator increases which results in increase in heat losses from the receiver. The optimum rim angle is 90° if the trade-off is made between surface area and the receiver diameter. Slope error plays a significant effect on the LCR; hence, these should be controlled if one has to go for large aperture PTSC. It is desirable to upgrade the manufacturing standards for the manufacturing of the large aperture PTSC to improve the GCR of the collector. If the current practices have been used for the manufacturing, the size of the receiver will also increase with increase of the aperture size and so the heat losses from the receiver which decreases the performance of the PTSC.

References 1. Bellos, E., Tzivanidis, C.: Alternative designs of parabolic trough solar collectors. Prog. Energy Combust. Sci. 71, 81–117 (2019) 2. Timilsina, G.R., Kurdgelashvili, L., Narbel, P.A.: Solar energy: markets, economics and policies. Renew. Sustain. Energy Rev. 16(1), 449–465 (2012) 3. International Energy Agency (IEA): Renewable energy essentials: concentrating solar thermal power at https://www.iea.org/publications/freepublications/publication/CSP_Essent ials.pdf (2009) 4. International Renewable Energy Agency (IRENA): Renewable energy technologies: Cost analysis series. Concentrating Solar Power at https://www.irena.org/publications/2012/Jun/Renewa ble-Energy-Cost-Analysis–Concentrating-Solar-Power (2012) 5. Song, J., Tong, K., Li, L., Luo, G., Yang, L., Zhao, J.: A tool for fast flux distribution calculation of parabolic trough solar concentrators. Sol. Energy 173, 291–303 (2018) 6. Jeter, S.M.: Calculation of the concentrated flux density distribution in parabolic trough collectors by a semifinite formulation. Sol. Energy 37(5), 335–345 (1986) 7. Khanna, S., Kedare, S.B., Singh, S.: Analytical expression for circumferential and axial distribution of absorbed flux on a bent absorber tube of solar parabolic trough concentrator. Sol. Energy 92, 26–40 (2013) 8. Lüpfert, E., Pottler, K., Ulmer, S., Riffelmann, K.J., Neumann, A., Schiricke, B.: Parabolic trough optical performance analysis techniques. J. Sol. Energy Eng. 129(2), 147–152 (2007)

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9. Schiricke, B., Pitz-Paal, R., Lüpfert, E., Pottler, K., Pfänder, M., Riffelmann, K.J., Neumann, A.: Experimental verification of optical modeling of parabolic trough collectors by flux measurement. J. Sol. Energy Eng. 131(1), 011004 (2009) 10. Ulmer, S., Heinz, B., Pottler, K., Lüpfert, E.: Slope error measurements of parabolic troughs using the reflected image of the absorber tube. J. Sol. Energy Eng. 131(1), 011014 (2009) 11. Roesle, M., Coskun, V., Steinfeld, A.: Numerical analysis of heat loss from a parabolic trough absorber tube with active vacuum system. J. Sol. Energy Eng. 133(3), 031015 (2011) 12. Hachicha, A.A., Rodríguez, I., Capdevila, R., Oliva, A.: Heat transfer analysis and numerical simulation of a parabolic trough solar collector. Appl. Energy 111, 581–592 (2013) 13. Wang, Y., Liu, Q., Lei, J., Jin, H.: A three-dimensional simulation of a parabolic trough solar collector system using molten salt as heat transfer fluid. Appl. Therm. Eng. 70(1), 462–476 (2014) 14. Cheng, Z.D., He, Y.L., Du, B.C., Wang, K., Liang, Q.: Geometric optimization on optical performance of parabolic trough solar collector systems using particle swarm optimization algorithm. Appl. Energy 148, 282–293 (2015) 15. Zhao, D., Xu, E., Yu, Q., Lei, D.: The simulation model of flux density distribution on an absorber tube. Energy Procedia 69, 250–258 (2015) 16. Houcine, A., Maatallah, T., El Alimi, S., Nasrallah, S.B.: Optical modelling and investigation of sun tracking parabolic trough solar collector basing on Ray Tracing 3Dimensions-4Rays. Sustain. Cities Soc. 35, 786–798 (2017) 17. Liang, H., Fan, M., You, S., Zheng, W., Zhang, H., Ye, T., Zheng, X.: A Monte Carlo method and finite volume method coupled optical simulation method for parabolic trough solar collectors. Appl. Energy 201, 60–68 (2017) 18. Zou, B., Dong, J., Yao, Y., Jiang, Y.: A detailed study on the optical performance of parabolic trough solar collectors with Monte Carlo Ray Tracing method based on theoretical analysis. Sol. Energy 147, 189–201 (2017) 19. He, Y.L., Xiao, J., Cheng, Z.D., Tao, Y.B.: A MCRT and FVM coupled simulation method for energy conversion process in parabolic trough solar collector. Renew. Energy 36(3), 976–985 (2011) 20. Cheng, Z.D., He, Y.L., Cui, F.Q., Xu, R.J., Tao, Y.B.: Numerical simulation of a parabolic trough solar collector with nonuniform solar flux conditions by coupling FVM and MCRT method. Sol. Energy 86(6), 1770–1784 (2012) 21. Leutz, R., Annen, H.P.: Reverse ray-tracing model for the performance evaluation of stationary solar concentrators. Sol. Energy 81(6), 761–767 (2007) 22. Islam, M., Karim, M.A., Saha, S.C., Miller, S., Yarlagadda, P.K.: Development of empirical equations for irradiance profile of a standard parabolic trough collector using Monte Carlo ray tracing technique. Adv. Mater. Res. 860, 180–190 (2014) 23. Wang, Y., Liu, Q., Lei, J., Jin, H.: Performance analysis of a parabolic trough solar collector with non-uniform solar flux conditions. Int. J. Heat Mass Transf. 82, 236–249 (2015) 24. Mwesigye, A., Huan, Z., Bello-Ochende, T., Meyer, J.P.: Influence of optical errors on the thermal and thermodynamic performance of a solar parabolic trough receiver. Sol. Energy 135, 703–718 (2016) 25. Jose, P.D.: The flux through the focal spot of a solar furnace. Sol. Energy 1, 19–22 (1957) 26. MATLAB and Statistics Toolbox Release: The MathWorks Inc. Natick, MA, USA (2017)

Cooling Energy-Saving Potential of Naturally Ventilated Interior Design in Low-Income Tenement Unit Ahana Sarkar

and Ronita Bardhan

1 Introduction Indoor design, albeit a subject of individual predilection and societal regime, tends to possess spinoff repercussions on household-level environmental quality and energy demand [1]. Particularly, in low-income settlements where space-restraints tie with social milieu leading to the inferior indoor environment, this becomes exigent. With unprecedented urbanization levels, people are transiting indoors gradually and spending 90% time inside. Hence, energy-efficient interior designs turn integrally crucial. The adverse climate in tropical regions has led to inefficient indoor temperature and airflow performance within living areas. This forces the occupants to rely on electro-mechanical ventilation for reaching acceptable thermal comfort. This phenomenon advertently increases the cooling energy demand up to 6.7% of the total world energy consumption. A significant amount of researches have analyzed the energy requirement and consumption pattern [2], available energy sources, energy transition and overall energy situation of tropical India, to investigate the potential of renewable energy implementation [3]. Identical findings have been reported for other developing nations [4] like China [5] and tropical regions like Mexico with similar climatic influences. While accounting the background of sustainable space cooling technologies, researchers have addressed natural ventilation to be an effectual alternative in delivering acceptable thermal comfort conditions while reducing energy consumption by 2.35% [6, 7].

A. Sarkar (B) · R. Bardhan Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 700046, India e-mail: [email protected] R. Bardhan Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UK © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_9

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Natural ventilation, as a passive building cooling strategy, has been less explored in India [8]. The energy-saving capabilities of natural ventilation in Indian lowincome buildings have been studied a few times [9], to the best of authors’ knowledge. However, specific occupant behaviour, detailed interior designs and numerical analyses on micro-level cooling energy-saving potential were not considered in the afore-mentioned study [9]. Mumbai’s residential sector records to 3386 cooling degree days. It contributes to two-third of the electrical load, with air-conditioning (30%) which is going to surge up to 73% by 2030. Considering a coincidence factor 0.7, the highest demand contribution of air-conditioned rooms has augmented from 5 Gigawatt (GW) in 2010 to 46 GW in 2020 and 143 GW in 2030 [10]. Additionally, owing to progressive slab rates of electricity charges, the tariff varies from INR 3.36/kWh for 0–100 units to INR 9.50/kWh for over 1000 units [11]. Among end-use components, occupant behaviour and indoor built environment design affect the energy demand maximum. Thus, with the excessive usage of energy-intensive appliances, the electricity charges tend to sprout to higher slabs, generating economic burden, especially for the low-income population. Mumbai, the financial capital of India, by attracting huge in-migration has transformed into the largest slum agglomeration. Currently, national affordable housing programs like ‘Housing for All 2022’ and slum improvement strategies provided by Mumbai City Development Plan 2005–2025 deliver affordable multi-rise rehabilitation units with habitable space of 24.6 m2 to the slum population [12, 13]. However, these completely ignore the livability parameters thus providing the occupants lesser degree of freedom in interior layout. These space-restrained compact multi-rise low-income tenement units suffer from poor indoor airflow and hightemperature zones owing to poor cross-ventilation. Consequently, these units turn energy-intensive because of lack of energy-concerning awareness and extensive low-cost energy-intensive cooling equipment usage despite economic, social and cultural-regime constraints. The novelty of this study lies in the utilization of cross-sectional methodology to evaluate the natural ventilation potential under warmer conditions using lowincome tenement of Mumbai as a case example. Objectively, this study attempted to identify the nat-vent effective iterated interior design solution and compare its energy-saving potential with respect to the existing case. This study also elucidated on the impact of optimized furniture location in delivering comfortable experiential indoor air velocity. This design is expected to save cooling energy consumption by reducing active cooling techniques usage. Owing to lack of literature regarding the nexus of low-income space-constrained tenement unit design, occupant behaviour and micro-level energy consumption pattern, this is the first study of its kind regarding the investigation of micro-level cooling energy reducing the possibility of nat-vent effective low-income tenement units design of Mumbai.

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2 Data and Methods 2.1 Case Study The slum rehabilitated multi-rise colony of Lallubhai compound was selected as the study area. Each tenement unit (21.42 m2 ) within this compound, stacked alongside a common double-loaded corridor, consisted of multipurpose living space with attached toilet and unsegregated kitchen. The major interior parameters included a window (air-inlet), a high-level air-outlet (0.3 m × 0.3 m) and a single-bed (an item of furniture). To identify the comfort-efficiency of the unit’s airflow performance, a ‘monitoring point’ was designated at 1.2 m above (human height during sitting) at the mid-bed position. The rationale behind the selection of ‘monitoring point’ can be attributed to the most observed occupant behaviour, where inhabitants spend most of the time near the bed due to high living space restriction. The aim would be to offer effective thermal comfort levels over this monitoring point, connoted further as ‘active zone’. From the housing survey, the unsegregated kitchen was noted to be detrimental to adverse indoor environmental conditions. Hence, partition wall and bed location were introduced as interior design parameters while generating iterated hypothetical cases.

2.2 Mixed-Mode Method A transverse stepwise methodology coupled with a housing survey and in-situ environmental sensor deployment (Testo 480® vane-metre, temperature sensors) was adopted here. The aim was to assess the nat-vent efficient indoor design with the highest cooling energy reduction possibility. The methodology started with the formulation of environmentally sustainable optimal design layouts (see Fig. 1).

Fig. 1 Floor plan of the iterated scenarios (change in bed location and partition wall design)

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These layouts were generated by utilizing ‘random sampling—computational fluid dynamics (CFD) simulations in nat-vent conditions—multi-objective optimization’-based approach adapted from [1]. The ten scenarios differed in partition wall design, its distance from the window, orientation, and height and bed location. CFD simulations Next, the indoor air velocity performance of the layouts was tested with hourly CFD simulations utilizing commercially available CFD tool of ANSYS Fluent. Both nat-vent and mech-vent enabled conditions using the ceiling fan and air-conditioning (AC) were simulated. For most of the indoor pollution simulators, the ambient air is assumed to be infinitely large volume. Thus, exhaust fan vented to the outdoors especially to the double-loaded closed corridors would degrade the environment of the neighbours. The vented air would also get reverted inside due to ceilings and parapets trapping the air. Hence, the exhaust fan was not considered for this particular space-constrained case study. The input boundary conditions for CFD models were retrieved from the vane-metre-based recordings. The models used fine tetrahedral mesh along with refinement for window (air-inlet), fan, and AC-inlet. The steady-state model settings included RANS k−ε turbulence model for nat-vent and AC and rotating frame model for ceiling fan. SIMPLE algorithm was utilized in CFD simulations for all cases for solving velocity–pressure coupling. Identification of nat-vent effective design scenario The afore-mentioned CFD simulated air velocity for natural ventilation, ceiling fan and AC represented airspeed values over the CFD monitoring point. Furthermore, owing to the stochastic nature of wind-driven natural ventilation, the holistic indoor airflow performance for a longer duration could not be captured in the nat-vent scenario. Hence, three major indicators were further utilized to identify the final design harnessing highest cooling energy reducing possibility due to nat-vent effectiveness. First, sensor-based hourly averaged outdoor wind speed at the window was reckoned for eight consecutive hours of a day in August 2018. The occupants’ reservations concerning privacy, the reveal of information and unwillingness regarding sensor installation for longer duration restricted the authors to install indoor sensors for eight hours only during daytime. Yet it beheld the impression of uncertainties linked with natural ventilation. The design scenarios were then simulated for these eight hours to retrieve the indoor air velocity values over the active zone in nat-vent condition. The nat-vent profiles for all scenarios were compared with persistent experiential air velocity values from the ceiling fan and AC. In this study, the hypothesis considered was—‘the design scenario which would deliver maximum hours of comfortable indoor air velocity, i.e. within the range of 0.2–1.08 m/s solely with wind-driven natural ventilation strategies would be selected primarily as nat-vent effective design. Another proxy measure of ‘total percentage of breathing zone (area of bed) with thermally acceptable indoor air velocity’ was accounted for additional screened selection in order to capture the holistic indoor airflow characteristics. The scenarios were also compared against average airspeed values over monitoring point due to natural ventilation, ceiling fan and AC in order to identify the most appropriate design scenario with maximum nat-vent enabled comfortable air velocity along with mech-vent strategies.

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The design solution which was observed to exhibit the maximum number of natural ventilation hours, which could deliver comfortable wind-driven indoor air velocity with constant set-point temperature and experience maximum percentage of area over the active zone (bed in this case) with comfortable air velocity in nat-vent conditions, would be finalized as the most nat-vent efficient indoor design solution. Estimating Cooling Energy Reducing Possibility A step-wise empirical analysis was adopted to calculate the cooling energy reducing possibility utilizing natural ventilation for the most nat-vent effective design layout. Nonetheless, to estimate the energy-saving potential, it is vital to compute the cooling demand with or without mech-vent modes while maintaining a fixed tolerable indoor temperature. The simulated airspeed values for all scenarios in nat-vent and mech-vent context were initially simulated keeping the temperature constant at 300 K (26.85 °C). This was designated as this value offers genuine comfort measure for indoor settings and is considered appropriate for warm-humid climate throughout the year. However, due to increased temperature, the thermal comfort levels tend to vary especially during warmer seasons of the tropical climate of Mumbai, which advertently forces the occupants to shift to mech-vent strategies from sole natural ventilation. In a response to this context, the monthly average temperature profile was retrieved from the Indian Meteorological Department (IMD) Mumbai to identify the total number of months the ‘best design scenario’ would deliver thermally comfortable indoor air velocity in nat-vent conditions. In order to estimate the energy-reducing potential, the AC and ceiling fan demand without natural ventilation was calculated. An alternative approach of estimating the energy saving is utilizing the thermal energy balance equation. Here, the air changes per hour that need to be delivered to the zone to cool it down can be estimated by aggregating the required cooling power and the difference between indoor and ambient temperature [7]. The estimation for the warmer months was carried out based on the energy balance model where heat taken from the room was assumed equal to the heat removed by the AC (refer to Eq. 1). (Q) = C p nt = τ W

(1)

where Q = amount of heat to be removed from the room, C p = heat coefficient of air at 1 atm pressure, n = moles of air to be cooled within the room, t = Difference in temperature, τ = Time taken by the AC to reduce the temperature by t, W = power of the air-conditioner in Kilowatt. On attainment of the desirable ambient condition, i.e. 300 K (26.85 °C), the ceiling fan was then assumed operated to uphold the thermally comfortable ventilation levels. However, nat-vent effective strategies have not been considered for warmer months

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in order to lessen the complexity concerning the uncertainties of natural ventilation. Thus, the total energy consumed by the unit for delivering thermally comfortable ventilation levels for warmer months was the aggregate of energy consumed by AC and ceiling fan (see Eq. 2).   E sav = E AC + E ceiling fan − E nat-vent

(2)

The total energy consumption owing to the air-conditioning and ceiling fan utilization for the summer months was calculated for best design scenario as well as the basecase scenario. This best indoor design layout would thus harness maximum cooling energy-reducing potential by increasing natural ventilation utility and minimizing mech-vent usage in winter months.

3 Results 3.1 Interior-Based Airflow Simulations The CFD simulated indoor airspeed values for natural ventilation scenarios (see Fig. 2a) over the active zone were observed between 0.10 m/s and 0.68 m/s. This indicates the significance of interior design and elucidates that by altering design elements like partition wall designs and bed location, comfortable indoor air velocity levels can be achieved in nat-vent spaces. Among ten scenarios, scenario 1 recorded maximum air velocity value of 0.69 m/s, when outdoor air velocity at air-inlet (here window) was measured 0.98 m/s. On contrary, scenarios 4, 5 and 6 recorded lowest air velocity values of 0.13 m/s, 0.16 m/s and 0.12 m/s, respectively. This can be attributable to the increased distance of bed from the window (air-inlet) for scenario 5 and 6, while, in scenario 4, the bed is located in the low-velocity zone thus creating a spatial gap between the bed position and airflow path. Despite same bed locations for scenario 1 and 4, the partition wall design also significantly modified the indoor airflow pattern. Thus, appropriate bed location selection with respect to optimized partition wall design is necessary to deliver comfortable indoor ventilation levels over the ‘active zone’. The room was considered an enclosed volume without any inlet and outlet while predicting for ceiling fan induced ventilation performance (see Fig. 2b). Here, the ceiling fan, accounted as a momentum source, was modelled with a diameter of 120 cm and a separation of 254 mm from the ceiling [14]. The velocity contours show that ceiling fan induced air velocity ranges between 0.0 and 2.2 m/s. Due to the steady behavioural character of airflow generating from the ceiling fan, maximum air velocity values were recorded right below the fan (1.08–1.48 m/s). It gradually reduced with the increased distance from the fan. However, scenario 1 and 8 experienced maximum airspeed of 0.55 m/s and 0.67 m/s, respectively, over the active zone which is attributed to the close proximity between bed and fan, while still air

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Fig. 2 Indoor air velocity profiles due to a natural ventilation, b ceiling fan and c air-conditioning

zones were observed over the active zone for scenarios 5 (0.18 m/s), 6 (0.17 m/s) and 7 (0.20 m/s) due to an increased gap between the ceiling fan and bed location. The constant reference air-conditioner (AC) inlet velocity and inlet supply temperature were assumed v = 2 m/s and T = 297 K (23.85 °C) for all the ten cases. Figure 2c shows nearly uniform air velocity distributions for all cases. Although a negligible difference in the highest and lowest mass-weighted average speed of 0.015 m/s was observed, the CFD predicted air velocity over the active zone was found to range between 0.02 m/s (scenario 6) and 0.84 m/s (scenario 9). The high air velocity zone of 0.8 m/s was found constant in all cases due to stable air throw from AC. Hence, optimized bed location with respect to the AC position can improve experiential air velocity over the active zone.

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3.2 Most Natural Ventilation Effective Design Scenario The sensor reckoned average outdoor air speeds at air-inlet were 0.47, 0.52, 0.52, 0.31, 0.23, 0.61, 0.21 and 0.98 m/sec. Figure 3a demonstrates that design scenario 4, 5 and 6 could not provide comfortable air velocity over active zone with sole nat-vent as well as ceiling fan and air-conditioning owing to poorly positioned bed. Out of eight hours, design scenarios 3, 7 and 9 delivered comfortable indoor airspeed for five hours. ‘Scenario 1’ was observed to be the most feasible design as the active zone had thermally comfortable nat-vent-driven indoor airflow for six hours. Figure 3b elucidates that effective percentage of comfortable ventilation for design scenarios 6 and 9 were recorded minimum for natural ventilation. While scenario 1 could deliver comfortable air velocity for over 96.95% of active zone in the natvent scenario and 89.1% from AC, scenarios 10 and 8 recorded highest (79.88 and 79.52% of the active zone) in terms of ceiling fan induced ventilation. This signifies that scenario 1 is the most nat-vent effective indoor design layout. Lastly, when the scenarios were compared against indoor air velocity performance levels for average nat-vent condition, ceiling fan and AC (see Fig. 3b), scenarios 1 and 9 performed best as these solutions delivered comfort air velocity from both natvent, AC and ceiling fan contexts. Hence, considering the afore-referred conclusions from the three above indicators, ‘scenario 1’ was decided to be the most feasible design layout with the highest nat-vent efficiency.

Fig. 3 Comparison of scenarios utilizing natural ventilation, ceiling fan and air-conditioning

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Table 1 Time required by air-conditioning to reduce to an ambient comfortable temperature Months

April

May

June

July

Sept.

Oct.

Nov.

Avg. Temp (°C)

28.1

29.7

28.9

27.2

27

28

27

Set Temp (°C)

26.85

τ (min)/day

1.34

3.03

2.19

0.37

0.1613

1.23

0.161

3.3 Cooling Energy-Reducing Potential Based on the weather data retrieved from IMD Mumbai, five months were found to be thermally convenient when the average temperature was recorded below 300 K (26.85 °C). This explicated that the ‘scenario 1’ would be able to deliver thermally comfortable air velocity over active zone without the assistance of any mech-vent techniques (ceiling fan or AC). While for seven warmer months, an aggregated utilization of afore-mentioned mech-vent strategies was required to reduce the room temperature. Table 1 shows the total time taken by the 1 kW AC to reduce the indoor temperature with 54.81 cu.m volume using Eq. 1. It can be observed from Fig. 4 that for the seven warmer months, the aggregated energy consumption for ‘scenario 1’ was accounted to 2.15 kWh for AC and 378 kWh for ceiling fan, amounting to 380.17 kWh. The estimation was subsequently lower than the base-case scenario with 744.6 kWh (AC: 105 kWh and ceiling fan: 639.6 kWh). This can be primarily attributable to the five months of natural ventilation efficiency and its potential to deliver thermally comfortable temperature for ‘scenario 1’ which saved cooling energy consumption during this period. Owing to the poor furniture location, the base-case scenario was forced to utilize mech-vent modes throughout the year to exhibit acceptable thermal comfort conditions. Furthermore, the occupants, unaware of energy concerns, tend to operate the AC for excess hours (0.5–1 h/day), advertently leading to an increase in unnecessary energy consumption. The energy saving due to natural ventilation is the difference of the cooling demand without natural ventilation and that with natural ventilation. Considering the flat rate

Fig. 4 Average monthly temperature variation in Mumbai (left); energy consumption pattern of ‘design scenario 1’ and ‘base-case scenario’

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of electricity charges, an annual saving of INR 2575.86 could be achieved through effective utilization of natural ventilation strategies. Thus, this analysis represents that with mere alteration in bed location, cooling energy demand can be significantly reduced by increasing indoor ventilation levels.

4 Conclusion A mixed-mode design and numerical analyses-based sequential methodological approach were adopted here to assess the interior design layout with maximum cooling energy-saving potential due to natural ventilation utilization. The results conclude that appropriate interior design with an optimized partition wall and furniture location as depicted in ‘scenario 1’ can deliver thermally comfortable indoor ventilation levels and can harness higher cooling energy-saving potential. Factors like windows operating schedule, construction materials, built area, room orientation, and occupants which are aerodynamically effective parameters have not been considered here. Moreover, the cost savings have been estimated based on a flat rate of INR 7/kWh which would portray a different image with progressive slab consideration. Nevertheless, the results demonstrate an exact potential of inexpensive interior retrofit design to analyze the probability of utilizing natural ventilation to have both environmental and economic benefits in tropical climates. This analysis can pave a pathway to the development of regulatory design guidelines for environmentally sustainable and energy-efficient low-income habitat rejuvenation.

References 1. Sarkar, A., Bardhan, R.: Optimizing interior layout for effective experiential indoor environmental quality in low- income tenement unit : a case of Mumbai, India. In: Building Simulation and Optimization Conference, Sept 2018, pp. 11–12 2. Pachauri, S., Spreng, D.: Direct and indirect energy requirements of households in India. Energy Policy 30, 511–523 (2002) 3. Kumar, A., Kumar, K., Kaushik, N., Sharma, S., Mishra, S.: Renewable energy in India: current status and future potentials. Renew. Sustain. Energy Rev. 14(8), 2434–2442 (2010) 4. Lee, C., Chang, C.: Energy consumption and GDP revisited: a panel analysis of developed and developing countries. Energy Econ. 29, 1206–1223 (2007) 5. Crompton, P., Wu, Y.: Energy consumption in China: past trends and future directions. Energy Econ. 27, 195–208 (2005) 6. Tong, Z., Chen, Y., Malkawi, A., Liu, Z., Freeman, R.B.: Energy saving potential of natural ventilation in China: the impact of ambient air pollution. Appl. Energy 179, 660–668 (2016) 7. Oropeza-perez, I., Alberg, P.: Energy saving potential of utilizing natural ventilation under warm conditions—a case study of Mexico. Appl. Energy 130, 20–32 (2014) 8. Indraganti, M.: Adaptive use of natural ventilation for thermal comfort in Indian apartments. Build. Environ. 45(6), 1490–1507 (2010) 9. Bardhan, R., Debnath, R., Malik, J., Sarkar, A.: Low-income housing layouts under socioarchitectural complexities: a parametric study for sustainable slum rehabilitation. Sustain. Cities Soc. 41(April), 126–138 (2018)

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10. Phadke, A.: Avoiding 100 new power plants by increasing efficiency of room air conditioners in India: opportunities and challenges, June 2014 11. The Maharashtra Electricity Regulatory Commission and Electricity Supply Code, (Annexure ‘ A’) Approved Tariff Schedule Maharashtra State Electricity Distribution Co. Ltd., no. 19, pp. 1–24 (2012) 12. Lueker, J., Bardhan, R., Sarkar, A., Norford, L. K.: Indoor air quality among Mumbai’s resettled populations: Comparing Dharavi slum to nearby rehabilitation sites. Build. Environ. 167, (2020) 13. Sarkar, A., Bardhan, R.: A simulation based framework to optimize the interior design parameters for effective Indoor Environmental Quality (IEQ) experience in affordable residential units: cases from Mumbai, India. In: IOP Conference Series: Earth and Environmental Science, vol. 294, p. 012060 (2019) 14. Babich, F., Cook, M., Loveday, D., Rawal, R., Shukla, Y.: Transient three-dimensional CFD modelling of ceiling fans. Build. Environ. 123, 37–49 (2017)

Development of an Improved Cookstove: An Experimental Study Himanshu, S. K. Tyagi, and Sanjeev Jain

1 Introduction The limited availability of fossil fuels and has forced researchers to search for alternative sources of energy for domestic cooking activities. Presently, about 2.8 billion people across the globe lack access to clean cooking energy [1]. Majority of them rely on the traditional stoves using solid biomass fuel to meet their daily cooking energy requirements. The incomplete burning of biomass in traditional stoves leads to lower thermal efficiency and higher emissions of pollutants [2]. The people especially young children and women who are exposed to emissions from traditional stoves suffer from adverse health effects [3]. Indoor air pollution from burning of solid fuel used for cooking is accountable for approximately 4.3 million deaths annually in addition to various respiratory and cardiovascular diseases [4]. Emission of black carbon from cooking stoves was found to be one of the essential causes of rapidly changing climate [5]. Improved cookstoves can significantly reduce the environmental and healthrelated issues caused due to traditional stoves. The forced draft improved cookstoves are found to be the most promising in reducing black carbon emissions released due to biomass burning [6]. The emission of particulate matter (PM 2.5) was found to be lowest for advanced forced draft cookstoves in comparison with traditional cookstoves [7]. An experimental study was carried out to compare the emission of PM from improved and traditional biomass cookstove, and the results indicated that the emissions of CO and PM were drastically decreased for improved gasifier stoves as compared to three stone stoves [8]. The recent advancements such as material of construction of cookstove, mode of air supply to the combustion chamber, design methodology and testing methods have been discussed thoroughly to improve the performance of traditional cookstoves [9]. Himanshu · S. K. Tyagi (B) · S. Jain Indian Institute of Technology Delhi, New Delhi 110016, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_10

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The effect of stove design and fuel type on efficiency and emissions of natural draft cookstoves was investigated and it was concluded that the semi-gasifier toplit up-draft stoves can significantly reduce the emission levels if they are operated with specified fuel under controlled operating conditions [10]. The performance of fifteen different cookstove models including both natural as well as forced draft configurations were analyzed experimentally [11]. The results indicated that the combustion in natural and forced draft top-lit up-draft models was cleaner due to proper mixing of air-fuel resulting in complete burning of volatiles [11]. The technical aspects such as design principles, parameters required to assess the cookstove performance, different testing protocols and methods available for performance evaluation of biomass cookstoves were reported on the basis of the existing literature [12]. The use of forced draft cookstoves in place of traditional cookstoves can result in huge saving of fuel and a significant reduction in emissions of methane, organic carbon, black carbon and other hydrocarbons can also be achieved [13]. The present work is focused on the development of an improved forced draft cookstove to reduce the emission of CO and PM resulting from solid biomass burning. An experimental investigation has been carried out to investigate the thermal performance and emission characteristics of the developed cookstove model.

2 Materials and Methods An improved cookstove following the principle of gasification has been developed on the basis of design parameters such as height, diameter of the cookstove and air flow rate requirement available in the literature [14]. The developed cookstove model is a forced draft and the combustion chamber was of cylindrical shape. Two axial fans were attached to the combustion chamber to supply both primary as well as secondary air. A variable speed arrangement was also provided to alter the amount of air being supplied according to requirement. The developed cookstove model consists of two coaxial cylinders with outer and inner radii of 90 mm and 70 mm, respectively. The primary air was supplied below the grate placed at the bottom portion of the combustion chamber as shown in Fig. 1 for gasification of fuel kept inside the chamber. The holes were provided at the top portion of combustion chamber, i.e., inner cylinder to fulfill the secondary air requirement. The preheated air was introduced into the combustion chamber via secondary air holes by passing the air through annulus provided between two concentric cylinders. The diameter of both primary and secondary air holes was kept to be 4 mm. Biomass pellets of 8 mm diameter were used as fuel in the present study. The thermal performance and emission characteristics of the developed improved cookstove have been calculated by using Bureau of Indian Standards under standard operating conditions [15]. The schematic diagram of the setup as shown in Fig. 2 was used to investigate the performance of the improved cookstove. The major components of the setup used in the present study are duct, hood, flue gas analyzer, PM

Development of an Improved Cookstove: An Experimental Study

Fig. 1 Schematic representation of developed improved cookstove

Fig. 2 Schematic representation of the experimental test facility

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sampler, desktop and exhaust blower. The hood as shown in Fig. 2 was used to collect the emissions followed by dilution of the same by mixing with ambient air. The diluted emissions were sampled through PM analyzer to know the amount of PM in the cookstove emissions. The calorific value of biomass pellets was determined by bomb calorimeter. The sampling probe of gas analyzer (Testo 350 XL) was inserted in the duct to measure the emissions of CO. The range and resolution of gas analyzer used in the present work for measurement of CO emissions were 0–10,000 ppm and 1 ppm, respectively. The flue gas was sampled through PM 2.5 cyclone using suitable pump arrangement. The particulate matter was accumulated on the filter paper kept in the filter holder of PM analyzer and the mass of the same was determined by calculating the difference between initial and final weights of the filter. The velocity of exhaust gas passing through the duct was measured to calculate total volume of exhaust gas. Microbalance having resolution of 1 µg was used to weigh filter papers. The temperature of water was noted by using PT 100 temperature sensor with least count of 0.1 °C. The digital weighing balance having resolution of 1 g was used to measure the water filled inside the vessel.

3 Results and Discussion A total of six number of experiments were performed to ensure the accuracy of the results. The heating value of biomass pellets used in this study was found to be 17.3 MJ/kg.

3.1 Thermal Efficiency The thermal efficiency for six set of experiments is shown in Fig. 3 and the average value of the thermal efficiency for the present model during all the experiments were found to 36.82% which was beyond the minimum requirement of 35% to be fulfilled by any forced draft cookstove as prescribed by MNRE. The thermal efficiency for all experiments lies in the range of 36.52–37.4%. The higher thermal efficiency was attributed to precisely managed air flow rates which resulted in complete combustion of volatiles. Also, the heat losses were minimized from outer surface of improved cookstove due to incorporation of air gap surrounding the combustion chamber. The uncertainty in measurement of thermal efficiency was found to be 0.16%.

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Thermal Efficiency (%)

40 38 36 34 32 30

1

2

3

4

5

6

Experiment Number Fig. 3 Thermal efficiency of improved cookstove model

3.2 CO Emission The emission factors for CO calculated in g/MJD and g/kg-fuel are shown in Table 1. It can be observed that the emission factor for CO for the present model was in range of 5.67–7.06 g/kg which is approximately ten times lower as compared to the emission factor of CO for traditional cookstove [16]. The CO emissions were lesser in the improved stove due to reduced heat losses as a result of air gap which acted as insulation, and hence leading to higher combustion chamber temperature. The air flow rates of primary as well as secondary air were also adjustable as less air is required during start and stop phases of cookstove. The uncertainty in calculation of CO emissions was found to be 2.02%. Table 1 Emission factors for CO emission

Experiment

CO (ppm)

CO (g/MJD )

CO (g/kg)

1

46

0.96

5.87

2

47

1.03

6.43

3

51

1.09

6.63

4

52

1.03

6.57

5

60

1.098

7.06

6

49

0.89

5.67

Average

50.83

1.02

6.37

108 Table 2 Emission factors for PM 2.5 emission

Himanshu et al. Experiment

PM 2.5 (mg/MJD )

PM 2.5 (mg/kg)

1

30.65

185.91

2

32.01

198.87

3

30.92

186.68

4

29.47

186.97

5

28.57

183.92

6

28.12

179.04

Average

29.96

186.90

3.3 PM 2.5 Emission The PM 2.5 emission factors calculated in mg/MJD and mg/kg-fuel are presented in Table 2. The range of PM 2.5 emission factor for the present model was found to be 179–198 mg/kg which was approximately twenty-five times lower than that of the traditional cookstove [16]. The PM level decreased significantly due to uniform distribution of preheated secondary air into combustion chamber at the top portion of the cookstove which completely burnt the particulates which otherwise could escaped into the environment. The uncertainty in determining PM 2.5 emission was 0.47%.

4 Conclusions The present study has been carried out to investigate the thermal performance and emissions of CO and PM 2.5 from an improved forced draft cookstove using pellets as fuel. The combustion chamber was insulated by providing an air gap to increase the thermal efficiency of the stove. The secondary air was also preheated as it came in contact with the wall of combustion chamber by allowing it to pass through an air gap before being supplied to the chamber. The thermal efficiency of the improved stove was found to be three times higher than that of the traditional stove. The emission factors for both CO and PM 2.5 were determined on the basis of mass per unit of energy delivered to the pot and mass per unit quantity of fuel. The average value of emission factors for CO and PM 2.5 calculated in terms of mass per unit quantity of fuel was 6.37 g/kg and 186.9 mg/kg, respectively. The emissions from the developed model were drastically reduced due to optimum air supply, preheating of secondary air and an air gap around the combustion chamber. The outer surface temperature of the cookstove was still very higher than the ambient which suggested that heat loss should be minimized for further improvement of the thermal efficiency.

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References 1. Energy Access Outlook 2017: International Energy Agency https://www.iea.org/public ations/freepublications/publication/WEO2017SpecialReport_EnergyAccessOutlook.pdf. Last accessed 5 Feb 2019 2. Jetter, J.J., Kariher, P.: Solid-fuel household cook stoves: Characterization of performance and emissions. Biomass Bioenergy 33(2), 294–305 (2009) 3. Berrueta, V.M., Edwards, R.D., Masera, O.R.: Energy performance of wood-burning cookstoves in Michoacan, Mexico. Renew. Energy 33(5), 859–870 (2008) 4. Subramanian, M.: Deadly dinners. Nature 509(7502), 548 (2014) 5. Venkataraman, C., Habib, G., Eiguren-Fernandez, A., Miguel, A.H., Friedlander, S.K.: Residential biofuels in South Asia: carbonaceous aerosol emissions and climate impacts. Science 307(5714), 1454–1456 (2005) 6. Kar, A., Rehman, I.H., Burney, J., Puppala, S.P., Suresh, R., Singh, L., Singh, V.K., Ahmed, T., Ramanathan, N., Ramanathan, V.: Real-time assessment of black carbon pollution in Indian households due to traditional and improved biomass cookstoves. Environ. Sci. Technol. 46(5), 2993–3000 (2012) 7. Jetter, J., Zhao, Y., Smith, K.R., Khan, B., Yelverton, T., DeCarlo, P., Hays, M.D.: Pollutant emissions and energy efficiency under controlled conditions for household biomass cookstoves and implications for metrics useful in setting international test standards. Environ. Sci. Technol. 46(19), 10827–10834 (2012) 8. Just, B., Rogak, S., Kandlikar, M.: Characterization of ultrafine particulate matter from traditional and improved biomass cookstoves. Environ. Sci. Technol. 47(7), 3506–3512 (2013) 9. Kumar, M., Kumar, S., Tyagi, S.K.: Design, development and technological advancement in the biomass cookstoves: a review. Renew. Sustain. Energy Rev. 26, 265–285 (2013) 10. Tryner, J., Willson, B.D., Marchese, A.J.: The effects of fuel type and stove design on emissions and efficiency of natural-draft semi-gasifier biomass cookstoves. Energy Sustain. Dev. 23, 99–109 (2014) 11. Still, D., Bentson, S., Li, H.: Results of laboratory testing of 15 cookstove designs in accordance with the ISO/IWA tiers of performance. EcoHealth 12(1), 12–24 (2015) 12. Sutar, K.B., Kohli, S., Ravi, M.R., Ray, A.: Biomass cookstoves: a review of technical aspects. Renew. Sustain. Energy Rev. 41, 1128–1166 (2015) 13. Sharma, M., Dasappa, S.: Emission reduction potentials of improved cookstoves and their issues in adoption: an Indian outlook. J. Environ. Manage. 204, 442–453 (2017) 14. Belonio, A.T.: Rice husk gas stove handbook. Appropriate Technology Center. Department of Agricultural Engineering and Environmental Management, College of Agriculture, Central Philippine University, Iloilo City, Philippines (2005) 15. Bureau of Indian Standards (BIS): Indian standard on Portable Solid Biomass Cookstove (Chulha First Revision). IS 13152 (Part 1) (2013) 16. Venkataraman, C., Sagar, A.D., Habib, G., Lam, N., Smith, K.R.: The Indian national initiative for advanced biomass cookstoves: the benefits of clean combustion. Energy Sustain. Dev. 14(2), 63–72 (2010)

Impact of Demand Response Implementation in India with Focus on Analysis of Consumer Baseline Load Jayesh Priolkar and E. S. Sreeraj

1 Introduction The power sector in India faces various issues like low plant load factor, network congestion, aging assets, the high value of transmission, and distribution loss. Shifting towards a decentralized generation can overcome power sector challenges. Demand response (DR) is the technique of managing load consumption patterns of the consumer in response to the needs of power utility with aim of lowering electricity costs, infrastructural deferral, and improving system reliability [1, 2]. DR programs where renewable energy sources (RES) are integrated can overcome challenges like dynamic intermittency, ramping nature, uncertainty, and volatility reliably and effectively [3]. DR if implemented on large-scale results in the reduction of capacity requirements as well as CO2 emission reduction [4]. DR is one of the important components for the successful implementation of the smart grid. A smart grid is an intelligent two-way power and information flow delivery system from source to the consumer which facilitates the integration of distributed generation sources, storage systems, demand-side management, and electric vehicles [5]. Consumer baseline load (CBL) estimation is one of the important aspects of realizing and tapping DR potential. Baseline gives reference consumption which is determined based on consumer’s consumption characteristics and past load data. Developing a standard baseline for a different class of consumers helps the utility to decide about incentives and compensation to consumers for their participation in DR programs. Our work presented in this paper is divided into two parts. The first part reviews various aspects of DR and its possible impacts on RES integration and deployment of smart grids in India. In the second part case study of CBL estimation is presented and analyzed. From the load data analysis for the last three years of one of the industrial feeder in Goa state, the baseline load curve is developed by averaging, adjustment, J. Priolkar (B) · E. S. Sreeraj National Institute of Technology Goa, Ponda, Goa 403401, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_11

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maximum value, and regression-based methods. Various performance metrics related to accuracy, bias, and variability are found and all the methods are compared for their effectiveness. The results obtained from CBL analysis help to know the load curtailment needed during event and DR potential available. Power utility of the state does not have a standard method for baseline estimation and effective data regarding the potential value of DR. CBL analysis presented in this work can be generalized and used for the other feeders of the utility for the determining potential value of DR.

2 Importance of Baseline on CBL Estimation Accurate estimation of CBL is crucial for success of the DR program. Estimation of CBL gives what will be expected consumption pattern of a consumer in the absence of a DR event. CBL is estimated based on historical data and forecasting methods. Measurement of DR typically involves comparing actual load during time of curtailment to estimated load that would otherwise have occurred without curtailment. The actual load is metered by smart meters at the consumer’s location, so the difference between CBL and actual-metered load gives actual realizable DR potential. If CBL estimation is less, consumers are less motivated to participate in the DR program because of receiving lower incentives from utility. Incase of overestimation of CBL utility is less motivated to operate the DR program, because of the overestimation of load reduction and paying off higher incentives to consumers. The literature on various aspects of CBL estimation is available in [6–9]. A statistical method is proposed for CBL estimation for non-residential buildings in California [6]. Various CBL methods are analyzed and compared based on accuracy and bias for residential consumers in [7]. For residential consumers, effect of CBL on the performance of the peak-time rebate program is investigated [8]. CBL estimation based on load pattern clustering for residential consumers is proposed; results obtained are compared with averaging and regression-based methods [9].

3 DR Impact Analysis on Renewable Energy Integration and Smart Grids 3.1 DR Impact on Renewable Energy Integration Implementing the DR program in a power system network can overcome ramping nature, uncertainty, volatility, and dynamic intermittency of RES [10–16]. To model the unit commitment problem for an isolated power system with a major share of wind energy resource, two approaches are used in [10]. In the first approach, utility control loads remotely as per system requirement, and in the second approach, consumers

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based on price signals shift the loads to minimize the costs. The stochastic unit commitment model is used to handle variations in wind power by deploying DR programs, three cases based on centralized load control, demand-side bidding, and wind power variation on deferrable loads is investigated in [11]. To determine the optimal size of wind, photovoltaic and battery-based power systems, real-time pricing and interruptible load DR programs are used. An optimal scheme for real-time pricing and interruptible load is formulated to minimize system cost and loss of energy probability [12]. From the perspective of the system operator, profit maximization problem is formulated so that he can decide to procure energy from RES or spot market. An optimal DR-based price scheme to sell power to end-users for a different time frame is also proposed in [13]. To effectively capture time-varying uncertainty of wind on the generation and consumer behavior, the effect of real-time pricing is analyzed through a nodal based DR model in [14]. Mixed-integer linear programming simulation software is used for developing scheduling model which incorporates DR and intermittent RES with random outages of generation units in [15]. DR management strategy for non-deferrable load with RES and storage using continuous-time optimization is proposed in [16]. DR implementation will provide necessary capacity addition when renewable sources like wind and solar are ramping up or down. DR deployment will also influence optimal sizing and siting of RES. It will help to improve reliability and minimize cost in terms of sizing and location of RES for state utility.

3.2 DR Impact on Smart Grid Deployment The use of DR provides operational flexibility for smart grids. The established infrastructure of smart grids and active participation of the consumers creates opportunities for DR deployment. DR provides advantages of peak load shifting resulting in energy savings, cost savings for power purchase, and improving reliability which is also the objective of smart grid implementation. A transition toward the use of smart appliances among consumers also creates scope for tapping DR potential in India. As the penetration level of electric vehicles in the smart grid increases, DR will play a significant role in load curtailment/enhancement by unidirectional charging and bidirectional charging vehicle to grid and grid to vehicle. DR along with electric vehicles can address peak shaving, valley filling in power system network thus improving efficiency as well as it can help to provide balancing service in a smart grid environment [17].

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4 Discussion on DR Status, Opportunities, and Challenges for India Most of the states have constituted DSM cell to look after energy efficiency and DR programs. On a pilot basis, various DR programs were carried out in India to understand the effectiveness of its implementation. Tata Power company undertook and implemented an integrated DR model for Mumbai city. Another pilot project was carried out by Tata Power company and Maharashtra electricity regulatory commission to understand effectiveness of DR. Tata Power company along with Delhi distribution board carried out Auto-DR program to characterize load profile of the consumers, and it was reported that technical DR potential available for industrial consumer category was approximately 25 MW [18]. Smart grid projects are being carried out under the national smart grid mission in most of the states, and DR implementation is included in all these projects. Projects related to the integration of DR with RES are also included in these projects. Technological, economic, social, and regulatory issues need to be addressed for realizing DR potential in India. Optimal implementation of DR programs by state utility will provide reliability, economic, and societal benefits to all stakeholders. Reduction of the peak to average ratio can help to defer generation capacity requirement, transmission, and distribution enhancement. Price spikes in wholesale electricity markets and congestion in transmission networks during peak hours can be avoided by using DR. To overcome load shedding problems which is most common in various states of India, DR can be an effective tool. The ability of DR to provide a fast and prompt response by either ramping up or reducing load as per need will facilitate the grid connection of intermittent RES. DR program implementation will help to decide the optimal site and size of the RES and energy storage systems. It is recommended that price-based DR for retail consumers in the country need to be prioritized with smart metering infrastructure development. Introduction and implementation of real-time pricing for large consumers, real-time pricing or critical peak pricing for commercial consumers, and time of use for residential consumers will help to enhance DR deployment. Implementation of incentive-based programs needs to be improved based on the eligibility criteria of consumers, curtailment terms, cost recovery, and incentive payments.

5 Case Study on CBL Estimation Electricity utility in Goa state is owned by the government, and it still functions like vertically integrated utility with no significant technological, economic, and organizational reforms. There are no significant developments in the area of demand response, energy efficiency, and smart grids in the state. From our analysis of load data, it is observed that utility faces power and energy deficit mostly during peak hours which results in load restrictions for industrial consumers. Implementation of

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DR programs will enable state utility to improve reliability as well as get economic and social benefits. DR can be very effective to overcome load shedding problem. For CBL estimation, hourly load consumption and temperature data of industrial feeder for three years 2016, 2017, and 2018 are collected from the 33/11 kV substation of Goa. CBL is estimated to analyze the load curve by different methods and assess DR potential available for industrial consumers.

5.1 Averaging Method This method calculates for each hour of the daily average of load at that hour across all the included days chosen based on the data selection criterion [6, 7]. For baseline estimation in March 2016, 2017, and 2018, ten weekdays load consumption data is selected. The days when demand does not follow historical patterns are excluded to improve the accuracy of baseline estimation. For 10 in 10 method out of 10 selected days, the highest 10 days data for each hour is taken and a baseline is calculated by an averaging algorithm. For 7 in 10 and 5 in 10 baselines averaging method out of 10 selected days, highest 7 days data and highest 5 days data are taken and baseline by an averaging algorithm is calculated. For three years of data of March, CBL is computed and results obtained for the year 2018 are presented in this paper. For a 10 in 10 method comparison between the CBL and actual consumption values are shown in Fig. 1. For a 5 in 10 method the comparison between the CBL and actual consumption values are shown in Fig. 2. These figures indicate that predicted baseline consumption values by 10 in 10 method closely follows actual day load consumption as compared to 7 in 10 and 5 in 10 methods. 3

CBL 10 in 10

Load (MWh)

Actual Day 2

1

0 0

4

8

12

16

Time (Hours) Fig. 1 Comparison of 10/10 CBL with actual day for March 2018

20

24

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CBL 5 in 10

Load (MWh)

Actual Day 2

1

0

0

4

8

12

16

20

24

Time (Hours) Fig. 2 Comparison of 5/10 CBL with actual day for March 2018

5.2 Maximum Value Method In this method, the maximum value of load consumption for each interval for 10 days is selected and CBL estimation is done by averaging of maximum values. The comparison between the CBL and actual data for the maximum value method is shown in Fig. 3. This method is less accurate, and the baseline load curve does not closely follow actual day load consumption since the maximum values are taken for evaluation of baseline. Adjustment Method To consider the influence of weather, actual operating conditions, and for consistent comparison between actual load and baseline values during an event, adjustment method is used. Adjustment of the baseline load is done by scaling that is multiplication with an adjustment factor. Estimated values by 10 in 10 baseline are multiplied 3 CBL (Max)

Load (MWh)

Actual Day 2

1

0 0

4

8

12

16

20

Time (Hours)

Fig. 3 Comparison of CBL with actual day for March 2018 by maximum value method

24

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3

Load (MWh)

CBL (Adj) Actual Day

2

1

0 15

16

17

18

19

20

21

Time (Hours) Fig. 4 Comparison of CBL with actual day for March 2018 by adjustment method

with an adjustment factor to get adjusted baseline values. Adjustment moment for baseline is considered for the last two hours before the event. The adjustment factor is given by the following equation.     C(d, h = n) = Al(d,h=n−2) + Al(d,h=n−1) / Pl(d,h=n−2) + Pl(d,h=n−1)

(1)

where C(d, h = n) is adjustment factor, Pl(d, h) is predicted load, Al(d, h) is actual load, d is the day number, and h is the hour [19]. For comparison between CBL and actual load data (see Fig. 4), estimated consumption values closely follow the actual day load consumption. This method is most accurate compared to other methods because baseline adjustment is as per actual situations and due to consideration of a small timeline window which is from 15:00 to 21:00 h. Regression Method The baseline is calculated from a multi-regression model based on the daily energy equation, which considers consumer’s daily energy consumption as a dependent variable, temperature and time as an independent variable [9]. In this model, past load consumption data is correlated with temperature and time. The coefficient of the model for the data analyzed is estimated by linear ordinary least square regression and is given by the following equation. CBLt = −0.2053 + 0.0106 ∗ t + 0.0859 ∗ Tt

(2)

where CBLt = Consumer base line load for particular hour, t = hour number, T t = average air temperature for particular hour t, 0.2053, 0.0106, and 0.0859 are multiregression coefficients. For the regression method comparison between the CBL and actual values are shown in Fig. 5.

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CBL (Reg)

Load (MWh)

Actual Day

2

1

0 0

4

8

12

16

20

24

Time (Hours) Fig. 5 Comparison of CBL with actual day for March 2018 by regression method

6 Performance Metrics Evaluation for CBL Methods Averaging based and maximum value CBL methods are analyzed based on accuracy, bias, and variability for understanding how closely it predicts the actual consumer load. Accuracy of baseline indicates how closely it predicts the actual load. The tendency of baseline to over or under predict actual load is known as bias. Root mean square deviation (RMSD) and normalized root mean square error (NRMSE) are used as a statistical measure of accuracy, average relative error (ARE), and normalized average relative error (NARE) is estimated to know bias. Relative error ratio (RER) and normalized relative error ratio (NRER) are used as a statistical measure of variability [9]. ⎛  N  ⎞  X 1,t − X 2,t 2 ⎠ RMSD = ⎝ N t=1

(3)

where X 1,t = Actual consumption, X2,t = Predicted consumption (10 in 10, 5 in 10) and N = Number of data points that is 24 NRMSE = RMSD/(Ymax − Ymin ))

(4)

where Y max = Maximum consumption on an actual day and Y min = Minimum consumption on an actual day ARE = X 1 / X 2

(5)

where X 1 = Average of predicted consumption − Average of actual consumption and X 2 = Actual consumption

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Table 1 Comparison of evaluated metrics for averaging and maximum value CBL methods CBL method

RMSD

NRMSE

ARE

NRER

10 in 10

0.070609

0.070014

0.009715

0.029578

7 in 10

0.077821

0.077069

0.01053

0.032611

5 in 10

0.08717

0.086332

0.01481

0.035368

Maximum value

0.20794

0.205933

0.078532

0.044553

Table 2 Comparison of evaluated metrics for adjustment and regression-based CBL methods CBL method

RMSD

NRMSE

ARE

NRER

Adjustment

0.034622

0.063786

0.00367

0.010815

Regression

0.085677

0.084849

0.010279

0.036321

R E R = Z 1 /Z 2

(6)

where Z 1 = Predicted consumption − Actual consumption and Z 2 = Average of actual consumption NRER = Standard deviation(RER)

(7)

Table 1 lists the computed values of performance metrics for all three averaging and maximum value methods. The least values are obtained for 10 in 10 baseline which shows better performance from the context of accuracy, bias, and variability over 7 in 10, 5 in 10 and maximum value methods. An averaging method is simple to understand for consumers and easy to evaluate for utility and can encourage more DR participation. Since additional data is used for analysis in adjustment and regression methods, performance metrics for these methods are listed separately in Table 2. From the computed values of performance metrics, it is seen that the adjustment factor substantially improves the performance of adjustment baseline in terms of higher accuracy and improved bias. The predicted baseline load curve closely follows the actual day load consumption in adjustment method as compared to all other methods used for analysis. From the load and solar insolation data, it was found that load peaks up from 10:00 to 16:00 h, solar insolation also peaks up during the same period. The integration of solar photovoltaic power system in the grid along with DR implementation can help to manage load effectively.

7 Conclusion Impact of DR on RES integration and smart grid in the country are highlighted and possible measures for effective implementation are suggested in this paper. From the survey, it is concluded that a wide scope for DR exists in the country and steps need

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to be initiated to tap this potential. DR implementation for the Indian power grid will enhance grid capacity, improve the quality, and reliability of power as well as promote renewable power generation. Various methods of CBL estimation for industrial feeder are analyzed. Among averaging and maximum value methods, 10 in 10 CBL method closely predicts the actual day load consumption. From performance metrics, it is found that the adjustment method of estimation is most accurate as compared to all other methods used for the analysis. Baseline methods suggested for estimation can be adopted for other feeders of the state utility to devise suitable DR programs for consumers. From the analysis of load curve data of feeder, estimated CBL values and solar insolation data of the last three years, it is suggested that integration of solar photovoltaic system along with DR can manage industrial load more effectively.

References 1. Medina, J.: Demand response and distribution grid operations: opportunities and challenges. IEEE Trans. Smart Grids 1(2), 193–198 (2010) 2. Albadi, M.: A summary of demand response in electricity markets. Electr. Power Syst. Res. 78(11), 1989–1996 (2008) 3. Brahman, F.: Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system. Energy Build. 90, 65–75 (2015) 4. Fan, Z.: Smart grid communications: overview of research challenges, solutions, standardization activities. IEEE Commun. Surv. Tutorials 15(1), 21–38 (2013) 5. Siano, P.: Demand response and smart grids—a survey. Renew. Sustain. Energy Rev. 30, 461– 478 (2014) 6. Sharifi, R.: Customer baseline models for the residential sector in the smart grid environment. Energy Rep. 2, 74–81 (2016) 7. Wijaya, T.: When bias matters: an economic assessment of demand response baselines for residential customers. IEEE Trans. Smart Grids 5(4), 1755–1763 (2014) 8. Mohajeryami, S.: The impact of customer baseline calculation methods on peak time rebate program offered to residential customers. Electr. Power Syst. Res. 137, 59–65 (2016) 9. Li, K., Wang, B., Wang, Z., Wang, F., Mi, Z., Zhen, Z.: A baseline load estimation approach for the residential customer based on load pattern clustering. In: Proceedings International Conference on Applied Energy, ICAE2017, Cardiff, UK (2017) 10. Dietrich, K.: Demand response in an isolated system with high wind integration. IEEE Trans. Power Syst. 24(1), 20–29 (2012) 11. Papvasiliou, A.: Large scale integration of deferrable demand and renewable energy sources. IEEE Trans. Power Syst. 28(2), 1385–1394 (2013) 12. Tobary, S.: Optimal sizing of PV wind battery power system considering DR programs. In: Proceedings of IEEE International Conference on Power Electronics and Drives Systems, Honolulu, USA (2017) 13. Cao, X., Zhang, J., Vincent Poor, H.: Optimal renewable penetration in energy procurement and demand response. In: Proceedings of IEEE International Conference on Communication, Kanas City, USA (2018) 14. Zeng, B.: Integrated planning for a transition to low carbon distribution system with renewable energy generation and DR. IEEE Trans. Power Syst. 29(3), 1153–1165 (2014) 15. Shahidehpour, M.: Stochastic operation security with demand response and renewable energy sources. In: Proceedings of IEEE Power and Energy Society General Meeting, San Diego, USA (2012)

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16. Leithon, J.: Demand response and renewable energy management using continuous time optimization. IEEE Trans. Sustain. Energy 9(2), 991–1000 (2018) 17. Miao Tan, K.: Integration of electric vehicles in smart grid: a review on a vehicle to grid technologies and optimization techniques. Renew. Sustain. Energy Rev. 53, 720–732 (2016) 18. Deshmukh, R., Yin, R., Ghatikar, G.: Estimation of potential and value of DR for industrial consumers in Delhi. In: Proceedings of India Smart Grid Week, Bangalore, India (2015) 19. Coughlin, K.: Statistical analysis of baseline load models for non-residential buildings. Energy Build. 41, 374–381 (2009)

Double Dielectric Barrier Discharge-Assisted Conversion of Biogas to Synthesis Gas Bharathi Raja , R. Sarathi , and Ravikrishnan Vinu

1 Introduction In recent years, due to increased energy demand and limited fossil fuel resources, there is a need to consider alternate renewable energy sources [1]. Biogas is contemplated as a promising feedstock for energy production. Currently, India has the second-largest number of biogas plants [2]. Biogas production requires lesser capital investment than other thermochemical processes to convert renewable feedstocks. The main constituents of biogas produced in anaerobic digestion of organic wastes from animal, agricultural and municipal wastes include CH4 (50–70%) and CO2 (30–50%) [3]. However, due to its lower calorific value, its use for power generation requires higher-energy consumption. Dry reforming of methane is considered as an effective way to produce syngas (H2 and CO), which is also used as a promising feedstock for various fuel and chemical syntheses via Fischer–Tropsch process, and for the production of power. CH4 + CO2 → 2H2 + 2CO H = 247 kJ mol−1

(1)

Dry reforming of methane is an endothermic process as shown in reaction (1), and requires high temperatures (above 800 °C) to attain reasonable, energy-efficient and cost-effective conversion of the highly stable CH4 and CO2 mixture [4]. Nonthermal plasma technology delivers a promising alternate means to convert biogas to syngas. Non-equilibrium conditions favor the formation of highly energetic electrons to initiate the thermodynamically unfavorable reactions at low temperatures and atmospheric pressure [5]. Non-thermal plasma can be generated by dielectric barrier Bharathi Raja (B) · R. Vinu Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India e-mail: [email protected] R. Sarathi Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_12

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discharge, corona discharge, gliding arc discharge and microwave discharges. One of the major advantages of using non-thermal plasma is that reactive processes requiring high activation energy can be easily achieved due to high electron temperature of 104 –105 K, corresponding to mean electron energy of 1–10 eV. Moreover, as a result of non-equilibrium condition among electrons, ions, radicals and neutral gas molecules, gas is at a low temperature ( commercial HZSM-5 (95.7%) > HZSM-5(20) (89.7%) > HZSM-5(60) (76.9%) ≈ HZSM-5(40) (74.2%).

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Table 2 Selectivity (%) of pyrolysates using various hydrodeoxygenation catalysts Compounds

Non-catalytic HZSM-5 HZSM-5 HZSM Comm. W2 C/γ-Al2 O3 (20) (40) 5 (60) HZSM-5

Phenolics Phenol

5.7

1.0

1.3

1.5

1.0

Cresol

8.4

0.7

1.7

2.7

0.8

0.3 0.0

Guaiacol

3.1

0.0

0.0

0.0

0.0

0.0

Methyl guaiacol

3.3

0.0

0.0

0.0

0.0

0.0

Ethyl guaiacol

0.9

0.0

0.0

0.0

0.0

0.0

Vinyl guaiacol

0.9

0.0

0.0

0.0

0.0

0.0

Other phenolics

5.6

0.0

0.0

0.5

0.2

0.0

Oxygenates Acetic acid

4.6

0.0

0.0

0.0

0.0

0.0

2-Pentanone

2.9

0.0

0.0

0.0

0.0

0.0

2-Cyclopenten-1-one

7.7

0.0

1.7

12.3

0.0

0.0

2-Cyclopenten-1-one, 2-methyl-

5.8

0.0

0.0

0.7

0.0

0.0

2-Butanone

13.3

2.0

5.4

3.1

0.0

0.0

2-Butenal

2.5

0.0

0.0

0.0

0.0

0.0

2-Cyclopentene-1,4-dione

0.1

0.0

0.0

0.0

0.0

0.0

Cyclopentanone

2.8

0.0

0.0

0.0

0.0

0.0

Other oxygenates

10.0

0.0

3.8

0.4

0.0

0.8

Furan, 2-methyl-

1.8

1.9

6.2

6.3

0.0

1.2

Furfural

3.0

0.0

0.0

0.0

0.0

0.0

2(3H)-Furanone, 5-methyl-

2.5

0.0

0.0

0.0

0.0

0.0

Furan derivatives

Benzofuran

0.0

1.3

1.6

1.6

0.0

0.0

2(5H)- Furanone

1.4

0.0

0.0

0.0

0.0

0.0

Other furan derivatives

6.6

1.0

1.9

2.7

0.0

0.2

Aromatic hydrocarbons Benzene

0.9

9.5

13.5

5.8

7.3

4.7

Toluene

1.7

22.5

13.7

15.3

24.2

10.2

Xylene

0.2

20.7

13.2

15.3

25.5

9.8

Styrene

0.0

1.1

2.3

1.5

2.8

0.1

Benzene derivatives

0.1

13.7

13.1

15.5

13.2

31.6

Indene derivatives

0.3

10.9

10.1

10.5

12.2

9.6

Naphthalene derivatives

0.1

9.2

4.8

8.8

10.1

15.4 (continued)

Hydrodeoxygenation of Bio-Oil from Fast Pyrolysis …

147

Table 2 (continued) Compounds

Non-catalytic HZSM-5 HZSM-5 HZSM Comm. W2 C/γ-Al2 O3 (20) (40) 5 (60) HZSM-5

Aliphatics hydrocarbons 1,3-Cyclopentadiene, 1-methyl-

0.0

1.9

1.3

1.8

0.2

2.7

4-Methylenecyclopentene

0.0

0.0

0.0

0.0

0.0

4.2

2-Pentene,3-methyl-, (Z)-

0.0

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References 1. Oasmaa, A., Elliott, D.C., Korhonen, J.: Acidity of biomass fast pyrolysis bio-oils. Energy Fuels 24(12), 6548–6554 (2010) 2. Adjaye, J.D., Bakhshi, N.N.: Upgrading of a wood-derived oil over various catalysts. Biomass Bioenergy 7(1–6), 201–211 (1994)

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3. Nguyen, T.S., Zabeti, M., Lefferts, L., Brem, G., Seshan, K.: Catalytic upgrading of biomass pyrolysis vapours using faujasite zeolite catalysts. Biomass Bioenergy 48, 100–110 (2013) 4. Nguyen, T.S., He, S., Raman, G., Seshan, K.: Catalytic hydro-pyrolysis of lignocellulosic biomass over dual Na2 CO3 /Al2 O3 and Pt/Al2 O3 catalysts using n-butane at ambient pressure. Chem. Eng. J. 299, 415–419 (2016) 5. Parsapur, R.K., Selvam, P.: A remarkable catalytic activity of hierarchial zeolite (ZH-5) for tertiary butylation of phenol with enhanced 2,4-di-t-butylphenol selectivity. ChemCatChem 10(18), 3978–3984 (2018) 6. Venkatesan, K., He, S., Seshan, K., Selvam, P., Vinu, R.: Selective production of aromatic hydrocarbons from lignocellulosic biomass via catalytic fast-hydropyrolsis using W2 C/γ-Al2 O3 . Catal. Commun. 110, 68–74 (2018)

Simulation of Horizontal Axis Wind Turbine Using NREL FAST Solver Asmelash Haftu Amaha, Prabhu Ramachandran, and Shivasubramanian Gopalakrishnan

1 Introduction The wind is an inexpensive form of clean energy which can be harvested from small scale to large scales without running out. Two of the most commonly employed wind energy devices are horizontal axis wind turbines (HAWT) and vertical axis wind turbines (VAWT). In the case of HAWT, the use of a tall tower allows access to higher wind speeds because wind speed increases with altitude. This can increase the power output dramatically since the power is proportional to the cube of the wind speed [12, 13]. The rotor blades rotate at a right angle to the wind direction allowing them to collect power through the full rotation. Yaw control is incorporated so that the turbine arranges itself perpendicular to the flow and gather maximum wind power when the wind direction changes. This allows the turbine to produce very high power with maximum efficiency close to the Betz limit. One of the drawbacks with HAWTs is that the efficiency of modern HAWTs increases with the increase in tower height and blade length, currently reaching 160 meters long and more [15]. However, it is difficult to make the peak efficiency a reality in many cases. Cost of transportation is more; heavy and costly cranes are needed for installation. A yawing mechanism is also required [15]. This makes it difficult to setup such technology in third-world countries. The higher the length of the blade the higher the tip speed and the noisier the turbines. In addition, there is a larger thrust force on the tower. Furthermore, the peak efficiency can be obtained at high wind speeds and only some countries have areas with high wind speeds. The HAWTs in countries with lower wind speed are not as efficient as desired. When compared with VAWT blades, HAWTs can capture more wind energy for the reason that the whole area swept by HAWT blades is perpendicular to the wind A. H. Amaha (B) · P. Ramachandran · S. Gopalakrishnan Indian Institute of Technology Bombay, Powai, Mumbai 400076, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_15

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direction. According to the diameter of their blades, HAWTs are grouped in to four categories, according to Jeng and Wang, (2016) [3]. 1. 2. 3. 4.

microscale (μSW T, diameter ≤ 0.1 m), smallscale (SSW T, 0.1 m < diameter ≤ 1 m), midscale (M SW T, 1 m < diameter ≤ 5 m), and largescale (L SW T, diameter > 5 m).

In most cases, large-scale category HAWTs are installed in grid systems or wind farms, whereas small-scale HAWTs find applications near residential areas. The wind turbine considered in this study is employed for mid-scale power production. Over the past, various ways have been applied to find the most suitable and convenient options to design and analyze HAWTs. Most of the research works explored so far, focused on different blade configurations (like 2 blades and 3 blades), and make use of research tools and theories like blade element momentum (BEM), improved BEM, commercial and open-source (CFD), and experimental studies. In this context, the experimental NREL Phase VI model with S809 airfoil is one of the most commonly studied HAWT in the past. HAWTs are primary devices in harvesting wind energy. The performances of HAWTs are improved through the evaluation of the different systems in the turbine blade design. Bai and Wang [3] performed a review on several analysis techniques (numerical and experimental) employed in the design of HAWTs. The analysis methods fall into experimental and numerical (computational) categories based on previous literature. The numerical procedures reviewed include classical BEM, modified BEM, CFD, and BEM-CFD whereas the experimental ones include wind tunnel experiment and field tests. The weaknesses and strengths of these methods have been discussed comparatively. Comparative investigation of computational and experimental procedures can aid to improve performance prediction of wind turbines and yield optimal blade design option and flow visualization. Moreover, current computational methods and future research directions have been discussed in [3]. Sun and Zhang [16] demonstrated the ability to accomplish numerical simulation of unsteady airflow around HAWT blades with the help of the ANSYS Fluent package and its sliding mesh capability. The turbine considered was the phase VI S809 blade of 10 m diameter designed for 10 kW standard power output at a rotational speed of 72 rpm. The total number of cells was 1.8 million and the sliding mesh was handled through user-defined function (UDF). Their results compared well with experiments from NREL UAE wind tunnel tests, hence the demonstrated approach can be used to predict the aerodynamic performance of wind turbine rotor. Bai and Chen [2] have designed a HAWT of 10 kW power output using BEM theory and modified stall model, and numerically simulated the blades to analyze the aerodynamic characteristics and flow structure. They have developed improved BEM theory which used various loss models (stall model, tip-loss factor and stall delay model) for predicting the performance of the turbine. The design conditions of the turbine were based on specified flow conditions of wind speed (10 m/s), tip-speed-ratio, and angle of attack using the S822 set of airfoils whose AR, blade radius, and chord length (c) are main design parameters [2, 14]. They have also

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conducted 3D steady-state RANS simulation with the Spalart–Allmaras turbulence model using commercial CFD code on 2.4 million cells. For rated wind speed of 10 m/s, comparison of the results show CFD as a better approach over improved BEM for the investigation of HAWT blades. Mohamad [14] performed aerodynamic simulations of steady and unsteady flow over NREL reference wind turbine called controls advanced research turbine (CART) which is designed for a rated power of 600 kW. The machine is designed for a specific airfoil family, S809. The method is based on BEM which used an open-source code, AeroDyn, developed by NREL. Results were verified against a finite element analysis. Power spectral density model has been applied to capture turbulence and unsteady effects on the blade. Components of normal and tangential forces, as well as lift coefficient, were determined. Anjuri [1] presented computational results for a 3D CFD model of NREL Phase VI turbine rotor with tip plate. The rotor was stall regulated, producing 20 kW power with full span pitch control. A single rotor was modeled with a periodicity using ANSYS CFX commercial CFD RANS solver. Transition model was applied along with steady wind condition without shear. The basic blade constitutes similar configurations with 0◦ yaw angle at the root and 3◦ pitch angle at the tip and with no tower and nacelle. The thrust, mechanical power, as well as spanwise force distributions were compared well with findings from experimental measurements showing the capability of the approach in extracting 3D aerodynamic effects. Bergman and Iollo [5] presented a methodology which reveals how to estimate output power that a HAWT can extract from the wind as a function of upstream wind. They used the standard two-bladed S809 airfoil as a test case due to the availability of NREL Ames test data and solved the incompressible NS equations on a fixed Cartesian grid. The rotating blades and mast were modeled by a penalization term in the governing equations. Second-order accurate scheme was chosen in space and time for solving the NS equations, and the collocated fractional algorithm was used for time integration. Even though the thrust evolution curve agreed with experiments, the results of the approach did not show a higher degree of accuracy for the curve of mechanical power versus wind velocity except for showing the same tendency. The discrepancies could be accounted for the fact that boundary layers may not have been accurately computed, turbulence may not have been modeled properly, and there may be issues with domain confinement. However, the drawbacks could be improved by creating a refined zone near the blades and using interpolation of results computed on the bigger domain with a coarser grid. In this paper, we demonstrate a numerical simulation of unsteady airflow over a horizontal axis wind turbine. The type of wind turbine studied is the NREL phase VI turbine. It is an experimental two-bladed HAWT. Simulation was run using the FAST solver [10, 11] with emphasis on power output. The objective of the present work is to obtain an aerodynamic prediction of HAWT blades using the freely available NREL FAST solver for varied parameters and flow conditions. The FAST solver produces outputs very quickly and requires a much lower computational effort as compared with a complete CFD simulation. We aim to assess the accuracy of the code for applications in wind turbines and generate

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aerodynamic data for verification with minimum computational cost and time. The work is part of a project on CFD analysis of wind energy devices. The Phase VI rotor is used to validate the FAST with available benchmarks. Results of the investigation for uniform but time-varying flow conditions are presented in the results and discussion section.

2 Methodology 2.1 NREL FAST Solver FAST is a fatigue, aerodynamic, structural, and turbulence analysis multi-physics engineering software specialized for wind energy applications. The tool has an aero-hydro-servo-elastic capability for full-scale simulation of onshore and offshore HAWTs operating in several conditions. FAST allows one to perform load and stability analysis, and obtain performance data. It helps the development and operation of wind farms involving HAWTs. The computer-aided engineering (CAE) tool is actively developed and maintained by The National Renewable Energy Laboratory of the USA (NREL). FAST is open-source available for free. FAST has been validated by measurements and verified and hence widely employed in the industry, Jonkman [10]. FAST 8, the latest version, consists of several modules for modeling physics efficiently. It utilizes low-order models that reduce computational cost. The modules available in the FAST solver are: 1. 2. 3. 4. 5.

AeroDyn for rotor aerodynamics HydroDyn for hydro-dynamics ServoDyn for control and electrical system dynamics ElastoDyn for structural dynamics, as well as TurbSim for generating turbulent inflow wind.

These modules can be coupled into a tool that enable us to perform aero-hydroservo-elastic analysis of wind turbines. The inputs and outputs connect several of the modules for specialized applications. For example, TurbSim can generate inflow wind field for use by AeroDyn module and then AeroDyn solves for the aerodynamic loads applying its BEM solver, and then ElastoDyn to obtain blade deformations etc. NREL has a software that couples OpenFOAM to FAST called SOWFA which can help in modeling and simulation of entire wind farm. In such a case, OpenFOAM models the wind farm aerodynamics with multiple turbines, involving the aeroelastic as well as wake interactions, and FAST models the turbine dynamics through actuator line model (ALM) (see Max Becker [4] and Jonkman [10]).

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2.2 Blade Element Momentum (BEM) Blade element theory (BET) is a method used to calculate forces on rotating bladed (propellers). The whole blade is first broken down into several small elements, and then calculating the forces on each element of the blade. Then these forces can be integrated along the entire blade and over one rotor revolution. The forces and moments produced by the whole propeller or rotor are then determined without considering the velocity induced on the rotor disk. Momentum theory or actuator disk theory describes the modeling of ideal actuator disk of rotor blades [4]. These techniques provide the designer a quick estimate of size and aerodynamic performance with low cost. They use the principle that the summation of aerodynamic forces in streamwise direction must be equal to the rate of change of momentum of the air which must be equal to the mass flow rate times the overall change in velocity. By equating this force to the force obtained in terms of pressure difference across the rotor, one obtains the flow velocity across the disk to be equal to half the sum of inlet and outlet velocities. Inlet velocity in this case is the freestream velocity and the pressure is freestream pressure in both ends of the streamtube. These theories are directly applied to wind turbines. Other modifications of these theories like single, double, and multiple streamtube models have been employed so far [6, 11]. The disk is a discontinuous surface through which Bernoulli’s equation is not applicable. For high rotor solidities and large tip speed ratios, the simplified 1D momentum equation and other assumptions are not valid. Therefore, the models fail to capture and predict the real phenomenon [7, 17]. Both the above methods are combined into more efficient method called blade element momentum (BEM) method to assuage some of the problems encountered when used individually (example calculating the induced velocities). The FAST solver explained in Sect. 2 uses the BEM [9, 10].

2.3 Performance Parameters In order to describe performance and operation of HAWTs non-dimensional parameters like coefficient of Power (C p ), tip speed ratio (TSR or λ), and solidity (σ ) are commonly used. Power coefficient is the ratio of extracted power to available power. C p estimates the aerodynamic efficiency of lift-based wind turbines. C p = Cq × λ. where, Cq is torque coefficient obtained from simulation. In this case, λ, is the ratio of turbine tip speed to freestream velocity (U∞ ). Cp =

P 1 3 A ρU∞ 2

.

(1)

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Ct , the torque coefficient is given as, Ct =

CL =

CD =

T 1 2 A ρU∞ 2

L 1 ρUr2 A 2

D 1 ρUr2 A 2

(2)

(3)

(4)

where, C L and C D are coefficients of lift and drag forces and Ur is relative velocity vector, ρ is density of the fluid, and A is the area swept by the wind turbine.

3 Results and Discussion NREL phase VI is a two-bladed experimental horizontal axis wind turbine for performing unsteady aerodynamics experiment. This turbine has been extensively used for testing purposes. It is a 10 kW, 10 m diameter standard turbine rotating at 72 rpm. The blades are twisted and tapered and the airfoil section is made of S809 except for the root. Tilt angle and cone angle are both 0◦ . The turbine was simulated for wind speeds of (5–25) m/s in a simple upwind configuration perpendicular to the rotor plane. We compute unsteady flow simulation over the NREL’s two-bladed HAWT turbine. The output of FAST solver via VTK files was used here to visualize the full turbine geometry given in Fig. 1. To run one simulation. The amount of time that FAST takes for converged results of one simulation is 368.00 s on intel core i7 computer with 8 GB of RAM. The model surface geometry of the blades is shown in Fig. 2. It was generated from coordinates information given by NREL software and main the features are shown in Table 1. Figure 3 (part a) plots the rotor torque versus wind speed. It starts from the lowest value of torque at 5 m/s, shows a steady increase of torque upto the maximum value at 11 m/s wind speeds. Figure 3 (parts b, c, d) describes other performance coefficients and parameters of the turbine. The evolution in the pattern of the plots can be observed relative to the torque. FAST uses an input file about the wind information which is in the form of a module called InflowWind. This module contains options for several different types of wind conditions. These types include steady wind, user-defined, binary TurbSim, and uniform wind. We first run this module for uniform wind type of ramped winds (5–25 m/s) with no shear effect, for which the results are indicated in Figs. 3 and 4. Uniform wind file is used to create (by the user) uniformly time-varying wind conditions at hub height as opposed to the steady wind which is internally calculated using steady conditions of the wind.

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Fig. 1 NREL phase VI wind turbine in paraview from FAST output

Fig. 2 NREL phase VI blade 3D model

In Fig. 4, we compare the numerical power curve obtained employing NREL FAST code with experiment and with three other standard CFD packages used earlier by other authors. The validation of results with experiment shows very good accuracy upto the location of maximum power and have some variation afterward. The CFD results of Fluent [16], Star CCM+ [18], and Ellipsys3D [17] match well with FAST. However, the verification with Fluent code show some discrepancies at higher as well as lower wind speeds, with FAST predicting the higher value of output power at high wind speeds and lower power at lower wind speeds. For wind speeds above 12

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Table 1 Parameters for NREL HAWT CFD model Parameters Value Parameters Airfoil Chord length, C Diameter of turbine, Dt Number of blade, N Reynolds number, Re

S809 0.737 m 10.058 m 2 ≈ 0.5 mil

Freestream TI Freestream velocity Rotational speed,  Tip speed ratio Pressure at outlet

Value 0.5 5–25 m/s 72 RPM Calculated 0 pa

Fig. 3 Rotor power versus wind speed, Method: NREL FAST flow solver

m/s, this particular turbine showed separation and stalled condition on the surface of its airfoil blade as shown by Yelmule [18]. The maximum power is in the order of 10 KW and occurs in the maximum wind speed range which is between 10 and 13 m/s [4, 16].

4 Conclusions The NREL FAST solver uses BEM theory, an iterative analysis and design technique, applicable only for HAWT. BEM is a mix of two theories, blade element theory and momentum theory, each formulated based on certain assumptions, combined to produce a set of equations [8] and solved iteratively. This method is simple to understand, computationally cheap, and efficient. It depends on the availability of

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Fig. 4 Comparison of rotor power of FAST with experiment and CFD. The results have been taken from references [2, 16, 18]

airfoil data for lift and drag coefficients as a function of angle of attack for certain Reynolds number. BEM is dependent on airfoil data and empirical relations which is considered to be the main limitation. In this paper, we employ the standard NREL FAST computational technique to calculate the power output of the NREL phase VI horizontal axis wind turbine (HAWT) rotor. The rotor is a two-bladed, 10 m diameter, and 10 kW HAWT with S809 airfoil blade profile. We simulate uniformly time-varying wind flow and the results of the given wind condition (read externally from a file) show a very good agreement with experiment as well as with many of the CFD solvers for unsteady aerodynamics. The work attempted in this section is part of a project on CFD analysis of wind energy devices, and the results obtained using FAST would help toward a better understanding of the field and efficient use of computational tools to solve similar problems. FAST has a scope to be improved further by coupling with other solvers such as OpenFOAM for which OpenFAST and SOWFA codes are two examples. The unsteady flow simulation was computed using the solver, and the results are in good agreement with those published in the literature. Acknowledgements The authors are thankful to Indian Institute of Technology Bombay for supporting this work.

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References 1. Anjuri, E.V.: Comparison of experimental results with cfd for nrel phase vi rotor with tip plate. Int. J. Renew. Energy Res. (IJRER) 2(4), 556–563 (2012) 2. Bai, C., Hsiao, F., Li, M., Huang, G., Chen, Y.: Design of 10 kw horizontal-axis wind turbine (hawt) blade and aerodynamic investigation using numerical simulation. Procedia Eng. 67, 279–287 (2013) 3. Bai, C.J., Wang, W.C.: Review of computational and experimental approaches to analysis of aerodynamic performance in horizontal-axis wind turbines (hawts). RSE Rev. 63, 506–519 (2016) 4. Becker, M.: fastfoam-an aero-servo-elastic wind turbine simulation method based on CFD (2017) 5. Bergmann, M., Iollo, A., Ouest, I.B.S., Team, M.: Numerical simulation of horizontal-axis wind turbine (HAWT). In: International Conference on Computational Fluid Dynamics (ICCFD7), vol. 1 (2012) 6. Chen, G., Gu, C., Hajaiej, H., Morris, P.J., Paterson, E.G., Sergeev, A.: Openfoam computation of interacting wind turbine flows and control (i): free rotating case (2014) 7. Digraskar, D.A.: Simulations of Flow Over Wind Turbines (2010) 8. Ingram, G.: Wind Turbine Blade Analysis Using the Blade Element Momentum Method. Version 1.1. Durham University, Durham (2011) 9. Jeromin, A., Bentamy, A., Schaffarczyk, A.: Actuator disk modeling of the mexico rotor with openfoam. In: ITM Web of Conferences, vol. 2, p. 06001. EDP Sciences (2014) 10. Jonkman, B., Jonkman, J.: Fast v8. 16.00 a-bjj. NREL (2016) 11. Jonkman, J., Hayman, G., Jonkman, B., Damiani, R., Murray, R.: Aerodyn v15 User’s Guide and Theory Manual. NREL, Golden, CO, USA (2015) 12. Liu, J., Lin, H., Zhang, J.: Review on the technical perspectives and commercial viability of vertical axis wind turbines. Ocean Eng. 182, 608–626 (2019) 13. Micallef, D., Van Bussel, G.: A review of urban wind energy research: aerodynamics and other challenges. Energies 11(9), 2204 (2018) 14. Mo, J.O., Lee, Y.H.: CFD investigation on the aerodynamic characteristics of a small-sized wind turbine of NREL Phase VI operating with a stall-regulated method. J. Mech. Sci. Technol. 26(1), 81–92 (2012) 15. O’Brien, J., Young, T., O’Mahoney, D., Griffin, P.: Horizontal axis wind turbine research: a review of commercial CFD, FE codes and experimental practices. Progr. Aerosp. Sci. 92, 1–24 (2017) 16. Sun, Y., Zhang, L.: Numerical simulation of the unsteady flow and power of horizontal axis wind turbine using sliding mesh. In: 2010 Asia-Pacific Power and Energy Engineering Conference, pp. 1–3. IEEE (2010) 17. Vermeer, L., Sørensen, J.N., Crespo, A.: Wind turbine wake aerodynamics. Progr. Aerosp. Sci. 39(6–7), 467–510 (2003) 18. Yelmule, M.M., Vsj, E.A.: CFD predictions of NREL Phase VI rotor experiments in NASA/AMES wind tunnel. Int. J. Renew. Energy Res. (IJRER) 3(2), 261–269 (2013)

Do Energy Policies with Disclosure Requirement Improve Firms’ Energy Management? Evidence from Indian Metal Sector Mousami Prasad

1 Introduction India’s target to reduce its emission intensity by 33–35% by the year 2030 (on the year 2005 levels) has been placed within the broad objective of keeping the global average temperature below 2 °C. Industrial activities are one of the significant sources of environmental degradation. Of the many environmental pressures, CO2 emissions are one of the most significant contributors to climate change. India alone emitted 6.6% of global CO2 in the year 2014, of which a significant source is due to the increase in consumption of fossil fuel sources of energy. As per sectoral emission inventory using energy consumption, industries in India, emit around 30% of CO2 . In this regard, energy policies play a significant role. These policy instruments could be economic, regulatory or information-based. In India, many studies have examined the role of regulations (command and control) on reducing firms’ impact on the environment [1–4]. Institutional pressure like regulations is supply-side instruments that reduce firm-level emissions by increasing the marginal penalty cost. There is also an increase in the number of studies arguing for improvement in firms’ disclosures to bring transparency and improve the accountability of the firms towards the environment. Following this, there have been energy policies that are informationbased. The policies require disclosures to be made by firms that range from general to specific and can take the form of Business Responsibility Report (BRR), Corporate Social Responsibility report (CSR) and energy consumption details as part of Perform Achieve Trade (PAT) mechanism. However, the empirical evidence on the role of energy policy with disclosure component on low carbon growth provides mixed evidence in developed nations and is very few in developing nations. In India, most of the studies have examined the environmental impact in the form of water pollution [2, 3] and emissions [5, 6]. M. Prasad (B) IIT Bombay, Mumbai, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_16

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Within emissions, studies have mostly examined the regulated emissions like SPM, SO2 and NOx [6–8] and only a few have examined CO2 [1, 9]. This study, therefore, extends previous studies and examines the role of disclosure-based energy policy on low carbon growth using data from metal firms. For this, the study first builds a firm-level CO2 emission inventory using fossil fuel consumption (direct emission) and electricity use (indirect emission). The calculated emissions are used to measure emission intensity: an indicator to examine low carbon growth. The impact of energy policy with varying disclosure component on emission intensity is modelled using the economics of emission framework. The results find that energy policy with disclosure requirements have a positive impact on the reduction of emission intensity of firms in the Indian metal sector. Further, the policy with specific quantitative disclosure is significant under various model conditions. This suggests that energy policy with disclosure components may be used as supplementary policy instruments for low carbon growth. The rest of the paper is organized as follows. Section 2 presents the literature review and conceptual framework followed by Methodology in Sect. 3. Results and Discussion are presented in Sect. 4 and conclusions along with limitations and scope for future work are discussed in Sect. 5.

2 Literature Review 2.1 The Institutional Context of Energy Policy with a Disclosure Component in India India uses a command and control approach to regulate the pollutants emitted by industries through Air Act (1981), Water Act (1974). For instance, Central Board of Pollution Control (CPCB) has prescribed emission range of SO2 , NOx and PM in iron and steel sector for each unit, like coke oven, a sintering plant, blast furnace, basic oxygen furnace, rolling mills, arc furnaces, induction furnace [10]. Similarly, for plants in the aluminium sector, emission of particulate matter is prescribed for aluminium plant and smelter plant. Emission of SOx , NOx though is not explicitly prescribed for the aluminium sector [11]. However, the status of regulatory compliance by Indian firms has attracted a lot of criticism on account of poor execution and monitoring. Further, the information provided by the regulatory body like central/state pollution control boards (CPCB/SPCB) is found to be not publically available and there is a lot of difficulty in procuring the data through Right To Information (RTI) Act [6]. As a result, in recent times there has been a focus on information-based policy instruments. The disclosures may be made either as part of the annual report or as a standalone report. The policies with the disclosure component aimed at improving the accountability and transparency of firms. There are presently three mechanisms that cover energy-related information disclosure for Indian firms.

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First, Business Responsibility Report (BRR): Capital market regulator, Securities and Exchange Board of India (SEBI) in the year 2012 mandated the top 100 firms based on market capitalization to publish information on their responsible business practices. The report is called Business Responsibility Report (BRR) and is based on the principles of National Voluntary Guidelines (NVG) on Social, Environmental and Economic Responsibilities of Business. The disclosure requirement includes information on energy use per unit of product and what has been the reduction achieved by firms in their value chain; initiatives in clean technology, energy efficiency, renewable energy; whether advocated in the advancement of energy security. Some of this information is specific but optional while some require an answer in yes/no format or general discussion. The information may be published as part of their annual report, effective from March 2012 onwards [12]. The coverage of firms was later increased to include 500 firms in the year 2015. Second, Perform Achieve and Trade (PAT): PAT is a market-based instrument under National Mission on Enhanced Energy Efficiency (NMEEE) as part of National Action Plan on Climate Change (NAPCC) [13]. PAT requires firms to reduce their specific energy consumption and covered 478 facilities from eight energy-intensive sectors (aluminium, cement, chor-alkali, fertilizer, iron and steel, pulp and paper, textiles and thermal power plants) over time-period 2012–2015. As a result of the PAT mechanism, firms have to disclose their specific energy consumption over the three year period. The information provides the details of the plant/firm, their location, specific energy consumption (TOE/Ton of Product) and Product Output (Ton) both for baseline year and target year. Finally, Companies Act 2013: The Act mandates firms to incur expenditure on social responsibility and disclose the same as the annual report of corporate social responsibility, effective form year 2014. The areas that a firm can engage as part of social responsibility are also based on NVG, similar to BRR requirement. However, as the social responsibility expenditure is directed towards society, the activities cannot be the normal course of business. As a result, there may not be a direct impact on the firm’s CO2 emissions. Some studies, on the other hand, note a positive impact of engaging in social responsibility and reduction of their impact on the environment. A brief summary of these policies is provided in Table 1. Table 1 Comparison across energy policies with disclosure component Characteristics

DISSEBI

DISCSR

DISPAT

Information required

Quantitative and qualitative

Quantitative and qualitative

Quantitative

Format specified

Yes

Yes

Yes

Periodicity

Annual

Annual

Every three years

Nature

General

General

Specific

Target

Firm

Firm

Plant

Launched

2012

2013

2012–2015 (first cycle)

Source Author’s Summarization

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As seen in Table 1, there is a similarity between the structure of disclosures to be made business responsibility requirement and CSR requirement. Further, for both these policies, the areas that qualify for disclosures are based on National Voluntary Guidelines. This suggests strong complementarities amongst the two.

2.2 Economics of Emission Firms’ production emits CO2 emissions, which is a serious threat to climate change. Energy-intensive industries are particularly responsible for the emissions as they meet their energy needs through fossil fuels. The environment has the capacity to absorb emissions. The firms may use this environmental capacity to discharge their entire emissions or a part of their emissions. However, in case of policies that regulate emissions, the firm will not discharge all the emissions in the environment, for the fear of attracting a penalty. Policies whether in the form of regulations; economic instruments and information; aim at reducing the supply of emissions in the environment. For the firm, the supply curve for emissions is given by the marginal penalty curve. The marginal penalty increases with a higher level of emissions, requiring the firm to pay extra to make an additional unit or emission [14]. The demand curve of emissions is given by the marginal abatement cost, which reduces at a higher level of emissions, making it a downward sloping curve. Marginal abatement cost is influenced by factors like the skilled workforce, environment management system employed by firms. Most of the past studies in India have examined the role of supplyside variables in reducing the firms’ impact on the environment. Within this group, a large number of studies has examined the role of regulations on water pollution [2] and some on air pollution. However, the role of information policy remains less investigated even though the disclosures are being argued as supplementary policy tools to limit emissions. Further studies examining the impact on policies on climate change is severely limited. Therefore, using the economics of emission framework, I hypothesize that there is a significant positive relationship between energy policy with disclosure component and reduction in emissions.

3 Research Methodology 3.1 Data Sample I use metal firms as sample owing to the significant contribution to the economic growth of India at around 2% of GDP and energy profile of the industry. Within the industrial emissions in India, the metal sector is one of the significant emitters of CO2 contributing more than half of the emissions over the last several decades. Also, the firms in the metal sector are classified under red category by pollution control

Do Energy Policies with Disclosure Requirement Improve …

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boards on account of high pollution intensity. The sector meets most of the energy requirement from fossil fuel sources of energy that adds to industrial emissions. The firms from the metal sector are selected based on the National Industry Classification (NIC). NIC classifies manufacture of basic metals as (a) Manufacture of basic iron and steel (Group 241), (b) Manufacture of basic precious and other non-ferrous metals (Group 242) and (c) Casting of metals (Group 243). The metal firms were shortlisted on the basis of the following criteria: (a) The firms should have published annual reports during the financial year 2006– 07 to 2014–15. (b) The firms should have disclosed information on fuel-wise energy consumption. The time period of study has been selected as years 2006–07 to 2014–15 to include an exhaustive list of metal firms. The nine-year period in the present study, saw many environmental policies being introduced; the effect of which is likely to be captured in the analysis. The process resulted in an unbalanced panel for 121 metal firms over 2006–07 to 2014–15. The sample firms are representative of the Indian metal sector. For instance, in the iron and steel sector, the sample of 76 firms in 2006-07 produce more than 70% of the nation’s crude steel. Further, within these 76 firms, six firms produce 53% of nation’s finished steel [15]. Most of the firms in group category 242 are manufacturers of aluminium followed by copper and other non-ferrous metals like zinc and lead. The sample firms produce 55% of the nation’s aluminium, 60% of the nation’s copper cathode and 90% of the nation’s zinc. The main producers in aluminium metal include public sector firm National Aluminium Company Limited (NALCO) and Hindalco Industries. Further, Hindalco is also the largest producer of copper cathode and Hindustan Zinc is the largest producer of zinc and lead [16].

3.2 Variables and Their Measurement I examine the role of disclosure-based energy policy on low carbon growth using the economics of emission framework. Low carbon growth is indicated by CO2 emission intensity. In absence of firm-level emission inventory, I calculate the emissions from energy consumption details following the steps of Prasad and Mishra [9], which includes classification of all energy consumption sources into solids, liquids and gases. After this, the emissions factors for respective fuels are arrived using their net calorific values, carbon content and oxidation factor. The resulting carbon emission coefficient used in this study are 2.242 (a ton of CO2 per ton of solid fuel), 3.089 for (a ton of CO2 per ton of liquid fuel) and 0.002 for (a ton of CO2 per ton of gaseous fuel). These emission coefficients are then multiplied by the energy consumption of fuels to arrive at CO2 emissions.

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CO2 Emissions =

n 

Cit (eCO2i )

i=1

where i is the type of energy used (solid, liquid and gas); t is the year of consumption; eCO2i is the CO2 emission coefficient for i type of energy consumed; The resulting CO2 emissionsit of the firms i in year t is divided by the total assetsit of the firm i in year t to arrive at CO2 emission intensity of an individual firm in a particular year. Disclosure-based energy policy: measured as a binary variable taking a value 1 if the firm is covered under the three energy policy discussed in the previous section; (a) business responsibility report requirement (DISSEBI) (b) corporate social responsibility disclosure requirement (DISCSR) and (c) perform achieve trade disclosure requirement (DISPAT). Of 121 sample firms, 71 firms are covered under either of the energy policy. 17 firms are covered under PAT, 63 under CSR rules and six firms under business responsibility report. Further, 13 firms are covered under both CSR and PAT both, and two firms under all three policies. As discussed in the theoretical framework of the economics of emission, the relationship between disclosure-based energy policy and emission intensity may also be influenced by firm characteristics. I include the firm characteristics like size, age of assets, research and development expenses and labour productivity as control variables. These variables have been found to be significant in influencing the firms’ emission profile in previous studies [1, 17–19]. Size of the firm is measured as natural log of sales. Age of assets is measured as gross fixed assets divided by total gross assets. Research and development expenditure is a binary variable taking a value of 1 if the firm discloses their research expenses, 0 otherwise. Labour productivity is measured as wages and salaries divided by sales. In addition, some studies have found that voluntary compliance like environment standard is a demand-side instrument that helps firms lower their marginal abatement costs [9, 17, 18], and hence the study includes ISO 14001 as an additional control variable. ISO 14001 is a binary variable that takes the value of 1 if any plant/firm is compliant with ISO 14001 and 0 otherwise. The data for company financials are obtained from CMIE Prowess and those pertaining to CSR regulation are from Ministry of Corporate Affairs website, and PAT disclosure from Bureau of Energy Efficiency website.

3.3 Model Specification The impact of disclosure-based energy policy regulation on emission intensity is modelled using fixed-effect model. Fixed-effect model controls for the endogeneity concerns arising from firm characteristics that vary across firms but remains constant over time [17]. CO2 emission intensityit = a0 + a1 EDRit + a2 qit + a3 vi + n it

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where i = firm, t—time period, EDR—energy policy with disclosure requirement; qit —firm-specific variables, vi —firm fixed-effect variables; nit —error term with zero mean and constant variance.

4 Results and Discussion Around 60% (71 out of 121) metal firms are covered by at least one of the three energy policies based on disclosure requirements. The results of fixed-effect regression of various energy policies on emission intensity are presented in Table 2. I have estimated the impact of energy disclosure regulation under three different assumptions. In Model 1, the basic relationship between various disclosure regulations and emission intensity is examined. In Model 2, I introduce the firm characteristics as control variable, and finally in Model 3, I include the year dummies as well. As seen in Table 2, the coefficient of DISCSR is significant and negatively related to emission intensity. This suggests that energy disclosure regulation of CSR alters the behaviour of firms and results in lower emissions, helping the firms achieve low carbon growth. The other two disclosure variables namely DISSEBI and DISPAT, however, are not significant. A plausible reason could be that only a small proportion of firms are covered under SEBI regulation and PAT mechanism. In the case of SEBI regulation, for the years 2012–2014, only the top 100 firms were required to submit a business responsibility report. When compared to the study sample, only six firms are covered. PAT, on the other hand, is targeted at the plant level, which, when aggregated to the firm level, reduces the number of firms. As the CSR and BRR requirements are both based on similar principles, even if one of them is significant, it suggests that the energy disclosure requirement has a positive impact on the firm’s energy management. Apart from disclosure regulations, the other firm characteristics like age of assets and ISO 14001 is negatively associated with emissions suggesting that firms with younger assets and environment management system have lower emissions intensity. R&D expenses, however, are not significantly associated with emissions. Size, on the other hand, has a non-linear relationship with emissions. Firms with smaller sizes have higher emissions and as the firm becomes larger, the economies of scale and scope help firms lower their emissions.

5 Conclusion Managing economic growth along with concerns of climate change is a challenge particularly for developing nations like India. In order to achieve low carbon growth for industries, it is required that climate responsibilities are fixed at the firm level. In this regard, there have been several policies with disclosure requirements for firms like Perform Achieve Trade, Business Responsibility Report and Corporate Social

166 Table 2 Fixed effect regression

M. Prasad Variables

Model 1

Model 2

Model 3

DISSEBI

0.086

0.197

0.144

(0.129)

(0.153)

(0.144)

−0.482***

−0.453***

−0.294**

(0.102)

(0.110)

(0.122)

−0.019

0.012

0.058

DISCSR DISPAT

(0.139)

(0.136)

(0.134)

−1.393***

−2.348***

(0.428)

(0.490)

Labour productivity (log)

0.017

0.026**

(0.010)

(0.010)

Size (log)

0.532***

0.426***

(0.143)

(0.134)

ISO 14001

−0.345***

−0.114

(0.090)

(0.101)

0.064

0.077

Age of assets (log)

R&D Size (log)2

(0.108)

(0.115)

−0.031***

−0.013

(0.009)

(0.008) −0.124***

D1

(0.035) D2

−0.279***

D3

−0.260***

D4

−0.359***

(0.049) (0.060) (0.068) D5

−0.599***

D6

−0.558***

D7

−0.547***

(0.081) (0.106) (0.101) −0.753***

D8

(0.107) Constant

−4.416***

−6.720***

−7.159***

(0.012)

(0.741)

(0.749) (continued)

Do Energy Policies with Disclosure Requirement Improve … Table 2 (continued)

167

Variables

Model 1

Model 2

Model 3

Observations

1001

1001

1001

R-squared

0.100

0.153

0.267

Number of com

121

121

121

Robust standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1

Responsibility Report. These disclosure policies cover energy, in addition to other environmental and social areas. Based on the study sample for metal firms, the present study examined and finds a positive significant impact of disclosure-based energy policy in helping firms lower their emissions. However, as the policies are based on the size of firms and their energy consumption, around 40% of sample firms are not covered by any of the alternative disclosure policy. A case is therefore made for expanding the coverage of the policies to smaller firms as well. Also, there is a need for effective execution and monitoring of disclosure regulation, such that it does not lead to becoming a tool for impression management by firms. In this regard, specific quantitative information should be encouraged by firms. The study has some limitations. The study used metal sector as a sample. Future studies may examine the disclosure regulation applicable to other energy-intensive sectors. In addition, the firms used in the study are listed firms. However, emissions are also made by firms that are not listed. This may be examined in future studies.

References 1. Mandal, S.K., Madheswaran, S.: Environmental efficiency of the Indian cement industry: an interstate analysis. Energy Policy 38(2), 1108–1118 (2010) 2. Murty, M.N., Kumar, S.: Win–win opportunities and environmental regulation: testing of porter hypothesis for Indian manufacturing industries. J. Environ. Manage. 67(2), 139–144 (2003) 3. Dutta, N., Narayanan, K.: Impact of environmental regulation on technical efficiency a study of chemical industry in and around Mumbai. Sci. Technol. Soc. 16(3), 333–350 (2011) 4. Kathuria, V.: Public disclosures: Using information to reduce pollution in developing countries. Environ. Dev. Sustain. 11(5), 955–970 (2009) 5. Murty, M.N., Kumar, S.: Measuring productivity of natural capital. In: Tendulkar, S.D., Mitra, A., Narayanan, K., Das, D.K. (eds.) India: Industrialisation in a Reforming Economy, Essays for K. L. Krishna. Academic Foundation, New Delhi (2006) 6. Shetty, S., Kumar, S.: Are voluntary environment programs effective in improving the environmental performance: evidence from polluting Indian Industries. Environ. Econ. Policy Stud. 19(4), 659–676 (2017) 7. Murty, M.N., Kumar, S., Dhavala, K.: Measuring environmental efficiency of industry: a case study of thermal power generation in India. Environ. Resour. Econ. 38(1), 31–50 (2007) 8. Murty, M.N., Gulati, S.C.: Accounting for Cost of Environmentally Sustainable Industrial Development in Measuring Green GDP: A Case Study of Thermal Power Generation State of Andhra Pradesh in India. New Delhi, E/253/2005 (2004)

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9. Prasad, M., Mishra, T.: Low-carbon growth for Indian iron and steel sector: exploring the role of voluntary environmental compliance. Energy Policy 100, 41–50 (2017) 10. Ministry of Environment and Forests: Ministry of Environment and Forests, Notification. Regd. NO. D.L-33004/99 (2012) 11. Ministry of Environment and Forests: The Environment (Protection) Act, 1986. New Delhi (1986) 12. Securities and Exchange Board of India: SEBI (2012) Business Responsibility Reports (2012) 13. Bureau of Energy Efficiency: Perform, Achieve & Trade (PAT) (2011) 14. Dasgupta, S., Hettige, H., Wheeler, D.: What improves environmental compliance? Evidence from Mexican industry. J. Environ. Econ. Manage. 39(1), 39–66 (2000) 15. Joint Plant Committee: Secretary’s DO report (Flash Report)—March 2017 (FY2016–17), Kolkata (2017) 16. Ministry of Mines: Monthly Summary on Non-ferrous Minerals and Metals March 2015 (2015) 17. Yang, X., Yao, Y.: Environmental compliance and firm performance: evidence from China. Oxf. Bull. Econ. Stat. 74(3), 397–424 (2012) 18. Nishitani, K., Kaneko, S., Fujii, H., Komatsu, S.: Are firms’ voluntary environmental management activities beneficial for the environment and business? An empirical study focusing on Japanese manufacturing firms. J. Environ. Manage. 105, 121–130 (2012) 19. Goldar, B.: Energy intensity of Indian manufacturing firms: effect of energy prices, technology and firm characteristics. Sci. Technol. Soc. 16(3), 351–372 (2011)

Power Management of Non-conventional Energy Resources-Based DC Microgrid Supported by Hybrid Energy Storage Jaynendra Kumar, Anshul Agarwal, and Nitin Singh

1 Introduction Human society requires an increasing amount of energy for domestic, commercial, agricultural, industrial and transport uses. Energy can be got from renewable and non-renewable energy sources. Non-renewable sources such as natural gas, coal and petroleum are very effective as far as the power production quality is concerned. Due to this reason, they have been conventional sources for power generation. But these sources are available in finite amount, and they are decreasing day by day [1]. Therefore, an alternative solution for power production is required. Continuous research and development in solar energy conversion technologies have made solar energy an efficient and economical source for electricity generation [2, 3]. To observe the solar energy potential in India, Indian government has set the target to generate electricity of 175 GW from renewable energy sources out of which 100 GW is from solar only. Applications of solar energy are water pumping, commercial building, and residential homes and space telecom mainly [4]. Another way to utilize non-conventional resources is to develop efficient energy conversion devices like fuel cells. Fuel cells are compatible with other conventional and non-conventional primary power sources. The electrical response time of an FC is generally fast, being mainly associated with the speed at which the chemical reaction is capable of restoring the charge that has been drained by the load. Because an FC system is composed of many mechanical devices, the whole FC J. Kumar (B) · N. Singh MNNIT Allahabad, Prayagraj 2110004, India e-mail: [email protected] N. Singh e-mail: [email protected] A. Agarwal NIT Delhi, New Delhi, Delhi 110040, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_17

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system has a slow transient response as well as slow output power ramping [5]. An energy storage system is an essential part of DCMG for stable and reliable operation with the intermittent energy sources like solar and variable loads when it is operating in islanded mode. DCMG net power can be categorized into two fractions: high-frequency components (fast power fluctuations) and low-frequency components (slow power fluctuations). Due to unavailability of a storage device which can handle both types of power fluctuations, HESS has been coming up with the effective and economical solution [6, 7]. DC system is superior over the AC system, especially for non-conventional sources like solar PV and fuel cell [8, 9]. So here, a DC microgrid (DCMG) system is proposed. However, some authors have discussed the performance of solar and fuel cells or solar with the battery storage or solar with the hybrid energy storage system separately. In this paper, the proposed system includes a hybrid energy source (one intermittent and one reliable) with the hybrid energy storage (battery: low power density and high energy capacity and SC: high power density and low energy capacity).

2 DC Microgrid System An islanded DCMG is a complete set of sources, storage system, interconnecting devices and loads. The systematic diagram of the proposed system has been shown in Fig. 1.

V fc

Charge Controller FC controller

Ifc

GFC

GS C1

GS C2

Boost converter

BDC

Fuel Cells Super-Capa citors Module

Boost converter

Charge Controller GBAT1

PV Array

Vpv Ipv

GPV MPPT Contr oller

GBAT2 BDC

Batter y-Banks Module

DC load 3kW + 2kW – 2kW

Fig. 1 Systematic diagram of proposed DCMG system

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Non-conventional energy resources include renewable sources, e.g. solar, wind, geothermal and ocean energy, etc., and energy conversion devices, e.g. fuel cells, etc. The proposed system includes both types of sources, i.e. renewable source, solar, and energy conversion device, fuel cells. To extract maximum power from the PV system, an efficient MPPT algorithm is essential. Two most developed hill-climbing MPPT algorithms are perturb and observe (P&O) and incremental conductance (INC). There is no general conclusion in the literature on which one of the two algorithms is the best one. Some literature suggests that the INC is a little more efficient compared to P&O [10, 11]. Therefore, here INC algorithm has been chosen for the PV system. The principle of the algorithm can be understood by the flow chart and the mathematical expression which is given in Fig. 2 and Eq. (1), respectively. δINC = (I /V ) + (I /V )

(1)

The sign of δINC decides the direction of next perturbation. At MPPT, δINC is zero. Fuel cells are a conversion device which converts chemical energy directly into electrical energy. Fuel cells work on electrochemical reactions (oxidation and reduction) of hydrogen with the oxygen. Various types (based on operating temperature Start Sense Vpv(k),Ipv(k) ∆V = Vpv(k)-Vpv(k-1) ∆I = Ipv(k)-Ipv(k-1)

∆V = 0

Yes

No Yes

Ipv + (∆I/∆V)Vpv = 0 No

Yes

Ipv + (∆I/∆V)Vpv > 0 No

Increase Vpvref

Decrease Vpvref

∆I = 0

Yes

No Yes

∆I > 0 No Increase Vpvref

Decrease Vpvref

Update history Vpv(k-1) = Vpv(k) and Ipv(k-1) -Ipv(k)

Fig. 2 INC algorithm for the PV system

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and fuel type) of fuel cells are used according to the application [5]. Due to the high power density of proton exchange membrane fuel cells (PEMFCs), it has been preferred choice for power generation of the developers and chosen for the proposed system. Chemical reactions for PEMFC take place at anode and cathode, and cell reaction is given in Eqs. (2, 3, 4), respectively [12]. H2 = 2H+ + 2e−

(2)

1 O2 + 2H+ + 2e− = H2 O 2

(3)

2H2(g) + O2(g) → 2 H2 O(l) + energy

(4)

The two primary attributes of storage devices are energy density and power density. Combination of battery and SC has been chosen for the proposed DCMG as HESS because of various reasons such as their availability, relatively low cost, the similarity in working principle and most notably their component attributes over each other’s limitations [7]. The converter that interfaces SC and DC bus is operating in a voltage control (VC) mode to regulate DC bus voltage which relaxes the battery from charging and discharging cycles; as a result, it improves the battery life [6]. The DCMG interfacing devices are DC/DC or DC/AC converters. Here, the unidirectional boost converter is used for the solar PV and FC interface as it works for MPPT PV system also. Bidirectional buck–boost DC/DC converter topology is chosen for integrating the battery and SC due to its various advantages such as its efficient operation, lightweight due to the absence of transformer, simple, easy to control, economical and most importantly compatibility with the system over other bidirectional DC/DC converter topologies [13]. Various parameters of the unidirectional boost converter are calculated by the following equations [14]. VO = VDC =

Vin 1− D

IO = (1 − D)Iin

(5) (6)

L=

Vin × D f × I

(7)

C=

IO × D f × V

(8)

where VO , VDC , Vin , D, IO , Iin , L, C, f , I and V are output voltage of the converter, DC bus voltage, input voltage of the converter, duty ratio of the power devices, output current from the converter, input current of the converter, inductance

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Fig. 3 Bidirectional buck–boost DC/DC converter

S1

D1

L

C DC

V DC

CE

VE

S2

D2

of the converter, capacitance of the converter, switching frequency of the power devices, ripple current of the inductor and ripple in output voltage, respectively. BDC works in two modes; buck and boost [13]. A systematic diagram of the bidirectional buck–boost converter is shown in Fig. 3. In charging mode, DC bus is connected to high voltage side or input (i.e. V DC = V i ). Battery and ultra-capacitors are connected to the low voltage side or output (i.e. V E = V O ). The power flows from the high voltage side to the low voltage side, and converter operates in buck mode. (1 − D)R 2 fS

(9)

(1 − D)   8L f S V Vo

(10)

L criCharging = CDCcri =

In discharging mode, voltage of energy storage systems (V ESS ) is the input voltage (i.e. V E = V i ) and the DC bus voltage (V DC ) is the output voltage (i.e. V DC = V O ). In discharging mode, power flows from the low voltage side to the high voltage side and converter operates in boost mode. L criDischarging =

(1 − D)2 R D 2 fS D 

C Ecri = R fS

V Vo



(11) (12)

where D, R, f S and V are duty ratio of the power devices, internal resistance of the storage device, switching frequency of the power devices and ripple in output voltage, respectively.

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3 Proposed Converter Control Since the solar irradiance and temperature keep changing throughout the day, so control strategy must fulfil the load demand in all conditions. Simultaneously, power should be balanced and microgrid should be stable. The MPPT controller adjusts the voltage by a small amount from the solar PV and calculates the power. If the power increases/decreases, further adjustment is tried until the power no longer increases/decreases. In Fig. 4, solar PV voltages and currents (V PV and I PV ) are continuously sensed and operating point of the DC/DC converter is decided by the slope (I/V-I/V ). When the slope is positive, the duty cycle (D) is increased by D and vice versa. To control the power flow from the fuel cells, a PI controller is developed as shown in Fig. 5. The fuel cell power is compared with the reference power of the fuel cells, and the power difference is divided by the reference voltage which passes through the PI controller to generate an error signal for the PWM generator. It generates the pulses for the unidirectional interfacing converter according to the power variation in DC bus. Charging and discharging of the battery and the SC are decided by variation in instantaneous DC bus voltage from the reference voltage. Variation in the DC bus voltage will govern the quantity of deficit/surplus power. Conversion of the voltage error signal (e(s)) into the power demand/supply reference signal for the battery and SC is presented by the block diagram shown in Fig. 6a. Here, three blocks have been used, namely PI controller, mean and rate limiter. PI block received the error signal as input and gives the manipulated output signal (m(s)). The output signal is ringing in its waveform, and their average value is continuously changing. So, to obtain the plot of moving average value, mean block is utilized. The power deficit/surplus are

Z -1 I PV

-

∆ I

+ +

V PV

Z

-1

∆V -

∆ I/ ∆ V + + I/V

∆D

-∆ D

T >0 F

D

+ -

Z -1

PWM Generator

Limiter

G PV

Fig. 4 MPPT controller for solar PV

P FC ref +

PWM Generator

PI Controller

-

Saturation P FC

V dc

Fig. 5 FC power controller

G FC

Power Management of Non-conventional Energy … V DC +

PI Controller

Mean

175 P BAT_REF

P REF

Rate Limiter

V DC _REF

-

+

P SC _REF

(a) +

P BAT_REF or P SC_REF V BAT or V SC

PI Controller

Z-1

Switch +

-

+

V BAT or V SC

IBAT or ISC -

+

Satutation V DC

T >0 F

+ PI Controller

Z

-1

0

T >0 F

+

PWM Generator

PWM Generator

G BAT2

G SC2

G BAT1 G SC1

Switch

(b) Fig. 6 BDC controller for battery and SC. a Reference generation. b Switching pulse generation

fulfilled by the battery and SC both. The battery response time is not much faster, so in battery power signal passing through a rate limiter blocks to limit the power flow rise into/from the battery, whereas SC response time is fast so it does not require rate limiter. The governing equations are discussed as below: e(s) = |VDC_ REF − VDC |

(13)

ki ) e(s) s

(14)

m(s) = (k p +

where k p and k i are the proportional and integral gains of the PI controller. To control the buck–boost converter operation, it is required to vary the pulse width according to power need/supply from the storage devices. To produce the pulses for the buck–boost converter from the power need/supply (power reference), a block diagram has been given in Fig. 6b, in which power signal is first converted into a current signal by dividing it with the terminal voltage of the storage device and then produces the error signal by comparing it with the battery current or SC. The error signal is passed with the different blocks (i.e. PI controller block then to delay block to summation block to division block to saturation block to switch to PWM generator block) to convert it into pulses to operate the buck–boost converter switch. The PI block manipulates the error signal. It is used because the simplicity in implementation does not require a high computation process and gives a suitable performance. Delay block is inserted here to provide the transport delay. Summation block will add the signal with the battery terminal voltage, and divide block divides the output with the DC link voltage. Saturation block is provided here to limit the

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magnitude of the upper and lower boundary of the signal. Here, the upper limit has been taken 1 and the lower limit zero. Switch block is used to decide the buck–boost converter operation either in buck mode or boost mode. It operates using if-else logic. PWM generator produces the pulses to operate the buck–boost converter. According to the power supply/demand, it adjusts the duty ratio.

4 Results and Discussion In an islanded operation of DCMG, storage devices are the only responsible unit which limits the DC bus voltage and maintains power quality. Equations (15) and (16) are the power balanced equations during charging and discharging conditions. Ppv + Pfc = Pdc + Pbat + PSC

(15)

Ppv + Pfc + Pbat + PSC = Pdc

(16)

The load varies in three steps 3 kW, 5 kW and 3 kW and changes at t = 1 s and t = 2 s, respectively. Transient and steady-state fluctuations are handled by SC. Power variations in load change are balanced by the battery. According to the net power (Available power or power from PV and FC—load demand). After a few transients, PV system output power gets constant. It delivered constant 2 kW and operates on MPPT. However, little glitches are observed during the load change. FC continuously delivers 2.5 kW power, and its response time is fast (Fig. 7a). Figure 7(b) presents PV power, voltage and current variations. Battery and supercapacitor SoC, and voltage and current variations are shown in Fig. 7c, d, respectively. The voltages are almost constant, and fluctuations appear in currents. Voltage and current waveforms of fuel cells and DC link are given in Fig. 7e, f. There are small glitches which are present in the DC link voltage during load change; otherwise, voltage is almost constant. DC link current varies according to the load demand. Voltage and current ripples for the DC link, battery, supercapacitor and FC are calculated from the obtained results as shown in Table 1 at steady state. Voltage ripples are within the permissible limit (1%), which represents the stable operation of DCMG. Current ripple in SC is ≈824%, which represents that fluctuations occur during steady state and are handled by SC.

5 Conclusion In this paper, an islanded DCMG is realized, which has been developed by the solar PV, FC, HESS and three-step variable DC load. Performances of various subsystems such as solar PV and HESS are analysed and discussed, and results are presented.

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(a). Power management of the proposed DCMG system.

(b). Power, voltage and current of the PV system

(c). Battery voltage, current and SOC Fig. 7 a Power management of the proposed DCMG system. b Power, voltage and current of the PV system. c Battery voltage, current and SOC. d SC voltage, current and SOC. e FC voltage, current and SOC. f Stable DC bus voltage and current

Ripples in the output voltage waveforms are within permissible limit. It is observed that HESS is very much compatible with the PV-FC system. The too high current ripples in SC conclude that the steady-state fluctuations are handled by the SC which relaxed battery and improves its life.

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(d ). SC voltage, current and SOC

(e). FC voltage, current and SOC

(f). Stable DC bus voltage and current Fig. 7 (continued)

301.45

48.324

48.002

45.084

DC link

Battery

SC

FC

V max

44.948

48.001

48.314

299.05

V min

45

48

48.318

300

V avg

Table 1 Ripples in voltage and current waveforms

0.80

0.302

0.002

0.02

Vavg

V Ripple (%) Vmax − V min × 100

55.72

0.97

55.32

−0.05

9.97 −31.2

10.04

I min

−30.4

I max

55.54

0.1238

−30.8

10

I avg

0.72

823.91

0.026

0.70

Iavg

I Ripple (%) Imax −I min

× 100

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References 1. Shahzad, U.: The need for renewable energy sources. Int. J. Inf. Technol. Electr. Eng. pp. 16–18 (2015) 2. Spagnuolo, G., Petrone, G., Araujo, S.V. et al.: Renewable energy operation and conversion schemes: a summary of discussions during the seminar on renewable energy systems. IEEE Ind. Electron. Mag. 4(1) (2010) 3. Rajput, S.K.: Solar Energy: Fundamental, Economics and energy Analysis (2017) 4. Annual report 2017–18: Ministry of New and Renewable Energy. Government of india 5. Farooque, M., Maru, H.C.: Fuel cells—The clean and efficient power generators. Proc. IEEE 89(12), 1819–1829 (2001) 6. Kumar, J., Agarwal, A., Agarwal, V.: A review on overall control of DC microgrids. J. Energy Storage 21, 113–138 (2019) 7. Hajiaghasi, S., Salemnia, A., Hamzeh, M.: Hybrid energy storage system for microgrids applications: a review. J. Energy Storage 21, 543–570 (2019) 8. Kumar, J., Srivastava, S., Agarwal, V.: Power management of solar based DC microgrid system enabled by solid state transformer. In: 14th IEEE India Council International Conference (INDICON), Roorkee India (2017) 9. Sanjeev, P., Padhy, N.P., Agarwal, P.: Autonomous power control and management between standalone DC microgrids. IEEE Trans. Ind. Inf. 14(7), 2941–2950 (2018) 10. Aureliano, M., Brito, G.D., Galotto, L., Sampaio, L.P., Melo, G.D.A., Canesin, C.A.: Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans. Ind. Electron. 60(3), 1156–1167 (2013) 11. Subudhi, B., Pradhan, R.: A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Trans. Sustain. Energy 4(1), 89–98 (2013) 12. Daud, W.R.W., Rosli, R.E., Majlan, E.H., Hamid, S.A.A., Mohamed, R., Husaini, T.: PEM fuel cell system control: a review. Renew. Energy 113, 620–638 (2017) 13. Ravi, D., Reddy, B.M., Shimi, S.L., Samuel, P.: Bidirectional DC to DC converters: an overview of various topologies, switching schemes and control techniques. Int. J. Eng. Technol. 7(4.5), 360–365 (2018) 14. Patil, R., Anantwar, H.: Comparative analysis of fuzzy based MPPT for buck and boost converter topologies for PV application. In: International Conference on Smart Technologies for Smart Nation (SmartTechCon), Bangalore, India (2017)

Sizing of a Solar-Powered Adsorption Cooling System for Comfort Cooling Sai Yagnamurthy, Dibakar Rakshit, and Sanjeev Jain

Nomenclature Ao A1 COP I T η

Optical efficiency of collector Negative first-order efficiency coefficient (W/m2 K) Coefficient of performance Incident radiation on collector (W/m2 ) Temperature (K) Efficiency

1 Introduction Among the various available solar cooling technologies, adsorption cooling technologies have been recommended for small-scale and mobile systems and observed to yield better results for part load conditions than the absorption counterpart [7]. Many researchers have demonstrated the feasibility of solar-powered adsorption cooling systems and studied the influence of various parameters like solar insolation, ambient temperatures, etc., and arrangements like auxiliary heating, mass recovery, etc., on their performances [4, 5, 11]. In order to realize a complete autonomous system, the primary challenge has been the sizing of solar collector and storage system for appropriate demand matching, which requires the use of dynamic simulations [10]. S. Yagnamurthy · D. Rakshit (B) Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi, Delhi 110016, India e-mail: [email protected] S. Jain Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi 110016, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_18

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TRNSYS is a commercial software developed by the University of Wisconsin, which has been widely used by researchers for performance assessment and experimental validation. Ali et al. [2] evaluated the performance of a solar adsorption cum desalination system for a solar ETC-powered two-bed silica gel–water system in Assiut, Egypt, using a model developed in TRNSYS with an inbuilt MATLAB interlinking component. Al-Rbaihat et al. [1] conducted experimental and simulation studies on a solar flat plate collector-powered adsorption cooling system for a room cooling application under Jordanian climate. The adsorption chiller performance characteristics were closely matching between TRNSYS and the real system with a maximum percentage deviation of 19.3% in chilled power. Palomba et al. [9] developed a solar heating and adsorption-powered cooling model in TRNSYS and validated the real-time results of the solar air-conditioning and heating system installed in Shanghai Research Institute of Building Science, observing an error of less than 10%. In the current study, an approach has been devised for sizing the solar collector and storage volume systems for a solar-powered cooling system, making use of minimal data such as monthly averages of the daily average cooling load and peak load along with the monthly averages of the daily average solar irradiation and ambient temperature data. This was followed by an economic analysis to arrive at the optimal values of collector area and storage volumes. A dynamic simulation has been done in TRNSYS for the same solar-powered adsorption cooling system for meeting the room cooling load requirements of school building near Roorkee, India. The accuracy of the results obtained using the proposed approach has been crosschecked with the detailed dynamic simulation results obtained from TRNSYS, in terms of solar cooling fractions yielded as well as total costs incurred per unit mass of CO2 mitigation.

2 System Description 2.1 Components of Solar Adsorption Room Cooling System Model in TRNSYS The components considered in this simulation have been taken from the TRNSYS libraries. The major components of the solar room cooling layout shown in Fig. 1 are listed below: Solar thermosiphon collector with integrated storage A thermosiphon evacuated tube collector was used for providing thermal energy input to the adsorption cooling system. The efficiency equation for the collector is given by the following equation:

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Fig. 1 TRNSYS layout of solar adsorption cooling system

η = a0 − a1

(Tin − Tamb ) I

(1)

The collector parameters a0 and a1 are considered with the values of 0.63 and 3.12 W/m2 K, respectively, corresponding to an experimentally tested thermosiphon ETC, with a tilt angle of 30°. Adsorption chiller unit The Type 909 component with the data of a commercial adsorption chiller of 3-ton nominal capacity and 0.6 nominal COP has been used in this simulation. The cooling tower’s air-to-water volumetric flow rate ratio has been fixed at 400, in accordance with the design data of the cooling tower, while the sump volume and airflow fan capacities are 0.5 m3 and 186.4 W, respectively. Cooling Room Macro Type 19 detailed single-zone model has been used for modelling the cooling room geometry and to simulate the cooling load conditions. The dimensions of the room are 8.5 m × 6 m × 3.5 m. The room is oriented at 26 degrees from north towards west direction. The ventilation rate of the room is 0.09 m3 /s as per ASHRAE Standard 62.1 [3]. The room has a total occupancy of 30 people. The occupancy hours of the room are from 8 AM to 4 PM for 6 days a week. The occupancy reaches the peak value of 30 at the beginning, end and during the lunch hour and has an average value of 2 during the rest of the day. Fan Coil Unit Type 996 fan coil component of TRNSYS was used for coupling the adsorption chiller to the cooling room.

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2.2 Components of Room Cooling Load Estimation The cooling load of the room has been estimated in TRNSYS, using the Type 19 detailed single-zone model with energy rate controller, for the considered room geometry, construction and occupancy patterns. The energy rate controller estimates the cooling load required to maintain the room below a temperature of 27 °C and RH below 60% as per the upper comfort limit recommendation for less than 10% PPD by IMAC [6].

2.3 Control Strategy There are two chiller controller units (Type 2-Aquastat C) in the system, as shown in Fig. 1. The purpose of these controllers is as follows: Cooling mode Aquastat (Type 2-Aquastat C and Type 2-Aquastat C-2) Hot water inlet control: This controller turns on the adsorption chiller including all of its pumps, only when the temperature of water in the thermal storage is in the operable range of the chiller (55–85 °C). Room temperature control: This controller ensures that the chiller delivers cooling power to the room only as long as the room temperature is above the minimum comfort temperature of 22 °C as per the recommendations of IMAC for less than 10% PPD [7]. The controller would not turn on the chiller until the temperature of the room rises beyond the mid-value of the thermal comfort range (i.e. beyond 24.5 °C). Besides these, a programmable calculator turns off the chiller when the occupancy of the room falls to zero.

3 Simulation Results and Discussion 3.1 Preliminary Estimation of Solar Thermal System Sizing An initial estimate of the collector aperture area and storage volumes was obtained making use of the minimal data available for the given application at a certain location. The collector area was estimated monthly based on the daily average cooling load, irradiation and ambient temperatures as follows: A=

Daily Cooling load Average solar radiation × Operating COP × Operating efficiency × Daylight hours

(2)

Sizing of a Solar-Powered Adsorption Cooling … Table 1 Cost and power consumption details of solar cooling system

185

Capital investment (CI)

Unit cost

Power consumption (W)

Solar collector

Rs. 6000/m2

Storage volume

Rs. 6000/100 L

Adsorption chiller

Rs. 617,548.6

395

Cooling tower

Rs. 30,000

186

Fan coil unit

Rs. 19,750

186

Additional costs

% of capital cost

Piping and installation 10% (PI) Annual operation and maintenance (AM)

2%

Salvage value (S)

25% for solar collector and storage unit and 5% for the chiller-related components

where the operating efficiency is estimated from Eq. 1 with T in = 75 °C and morning average ambient temperatures. The operating COP of the chiller was taken to be around 0.6, as it fluctuated very little with varying operating conditions. To determine the suitable collector area, an economic analysis was carried out to compare the costs incurred per unit mass of CO2 avoided in running the chiller with selected collector area of solar cooling unit, instead of a solo operation using a conventional air-conditioning unit. Table 1 shows the costs and respective electrical power consumptions of the various units involved in the solar cooling system taken from MNRE guidelines of solar water heating systems [8], and market surveys of various manufacturers. It can be seen that the cost of adsorption chiller of 3-ton capacity is higher than a conventional chiller of the same capacity. One of the key reasons for this is the usage of customized designs in adsorption cooling systems. PV = CI + PI +

S AM((1 + d)n − 1) − d(1 + d)n (1 + d)n

(3)

where PV is the present value of total costs incurred, ‘d’ is the discount rate taken to be 10.74% as per the norms of Central Electricity Regulatory Commission, India, and ‘n’ is the useful life of the plant which is taken to be 15 years. Considering an EER value of 4.0, the number of units of electricity saved (SU) for a selected collector area is given by  SU = SCF ×

Annual cooling demand EER

−Auxiliary power consumption × annual working hours) × n

(4)

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50.00

120 100 80 60 40 20 0

40.00 30.00 20.00 10.00 0.00

Aperture area (sq.m)

Thermal power (kWh)

where SCF is the fraction of cooling energy met by each collector area. Considering 0.9 kg of CO2 emissions per unit electricity produced and ignoring the costs of storage unit, the solar cooling fraction and costs were computed corresponding to each monthly collector area required (shown in Fig. 2) and the results are shown in Fig. 3. It was observed that the collector aperture area of 40 m2 yielded the least cost incurred per unit mass of CO2 mitigation. The maximum cooling load over the year obtained from the simulation was 8.04 kW. A suitable adsorption chiller has been chosen based on the minimum capacity available in the market (10 kW). Since the cooling load has been observed to be below the nominal capacity of the adsorption chiller throughout the year, a chilled water storage was considered unnecessary. Equation 6 shows the correlation proposed for storage tank volume estimation to accommodate for the difference in the solar thermal power availability and the thermal power requirements of the adsorption chiller (Fig. 4). Figure 5 shows the storage volume requirements computed monthly corresponding to the collector area of 40 m2 .

Daily chiller thermal demand Daily solar thermal availability (with 40sq.m collector area) Collector aperture area required

Months of the year

1.00

1200.00

0.80

1000.00 800.00

0.60

600.00

0.40

400.00

0.20

200.00

0.00 10.00 20.00 30.00 40.00

Cost in Rs

Solar cooling fraction

Fig. 2 Monthly estimates of daily average thermal demand and availability and the corresponding aperture areas required

Solar cooling fraction Total cost per kg of CO2

0.00

Collector area (sq.m) Fig. 3 Variation of solar cooling fraction and total costs incurred with varying collector area

187

1000

5.00

800

4.00

600

3.00

400

2.00

200

1.00

0

0 2 4 6 8 10 12 14 16 18 20 22

Cooling load (kW)

Solar radiation (W/sq.m)

Sizing of a Solar-Powered Adsorption Cooling …

Solar radiation Cooling load

0.00

Time of the day Fig. 4 Cooling load and solar radiation fluctuation over a typical day in July

V =

Peak load duration ×





Peak cooling load Operating COP − Average solar thermal power × Efficiency

C p T

(5)

Thermal power (kW)

10

350 300

8

250

6

200

4

150 100

2

50 0

0

Thermal storage capacity (L)

The graph in Fig. 5 shows that the average thermal power from the collector of 40 m2 aperture area exceeds the peak thermal power for half of the months considered and hence no thermal storage volume is necessary. However, the chiller requires an overhead tank for hot water supply for which the minimum thermal storage capacity was found to be 100 L. The optimal storage volume was computed to be 300 L based on the economic analysis, though the deviation in cost computed was less than 5% with that obtained using the storage volume of 100 L.

Thermal storage volume required (L) Daily average peak chiller thermal demand

Months of the year Fig. 5 Monthly estimates of daily average peak thermal demand and availability and the corresponding storage volumes required

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3.2 TRNSYS Simulation Results A detailed simulation analysis was conducted in TRNSYS with the collector areas varying from 10 to 60 m2 and the storage volumes varying from 100 to 800 L. The performance was evaluated in terms of solar cooling fraction which is defined as follows: SCF =

No. of hours in comfort condition ( 1200 °C) to get the desired phase and at that temperature, La2 Zr2 O7 phase is seen as an impurity phase which restricts the ionic movement. Further Li7 La3 Zr2 O12 is not stable at humid air. Similarly, Li2 S-P2 S5 glass-based ionic conductors are also not chemically stable and react with the moisture of the air and create harmful H2 S gas [4]. Comparative to other SSEs, NASICON has advantages with respect to its high chemical stability in contact with Li metal and has easy synthesis process. Al+3 substituted LAGP (Li1+x Alx Ge2-x P3 O12 ) with NASICON structure has been studied in detail and is found compatible with Li metal anode and A. Das · M. Goswami (B) · M. Krishnan Glass and Advanced Material Division, Bhabha Atomic Research Centre, Mumbai 400085, India e-mail: [email protected] S. K. Deshpande UGC-DAE CSR, IUDCT, Mumbai 400085, India P. Preetham · S. Mitra Electrochemical Energy Laboratory, Department of Energy Science and Engineering, Indian Institute of Technology-Bombay, Powai 400076, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_23

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shows high ionic conductivity (~10−4 Scm−1 ) at room temperature. In this study, LAGP has been substituted by Si4+ in place of P5+ (Li1+x Alx Ge2-x P3-y Siy O12 , LAGPS) to increase the stability against moisture and see the effect on the structure and ionic conductivity [5]. XRD has been used to see the nature of the phase formed in the material and also Rietveld analysis to find out the exact cell structure, amount of Al+3 substitution and the cell parameters. EIS at low temperature helped to separate the conductivity due to grain and grain boundary. The cell has been fabricated with developed electrolyte and commercially available anode and cathode and has been tested for its performance.

2 Experimental 2.1 Synthesis of LAGPS Solid Electrolyte Glasses with nominal composition Li1.5 Al0.5 Ge1.5 P2.9 Si0.1 O12 (LAGPS) were prepared by the conventional melt-quenching technique. Every 100 g batch was prepared by mixing the initial constituents, in the form of carbonate and diammonium hydrogen phosphate of proportionate amount. Calcination was carried out for sufficient time to convert the initial constituents into their corresponding oxide form. This process was repeated to ensure complete decomposition after through mixing and grinding. After calcination, the charge was mixed and grounded properly and melted in a Pt-Rh crucible. The melt was held at the melting temperature for 1–2 h for homogenization and cast onto a metal plate. The glass was annealed at around 450–550 °C for 4–5 h and cool down to room temperature slowly. The glass was crystallized using predetermined heat schedule.

2.2 Characterization of the Electrolyte The phase formation in the sample was confirmed using XRD (Model Bruker 8 tools X-Ray Diffractometer). The electrical conductivity was measured on 1–2 mm thick and 10–12 mm diameter crystallized LAGPS samples. Gold coating was done on both the surfaces for good electrical contact. Novacontrol make frequency analyzer was used for conductivity measurement. The measurements were carried out in the frequency range of 1–106 Hz and the temperature range of 223–303 K.

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2.3 Assemble of LiFePO4 /LAGPS/Li Cell For cell fabrication, LiFePO4 (LFP) coated aluminum foil was used as cathode. The loading of the active material was 9.47 mg cm−2 . Li metal was used as anode. LFP/LAGPS/Li cell was assembled with 2032-coin cell inside argon-filled glove box. To reduce the LFP/solid electrolyte interfacial resistance, a small amount of liquid electrolyte [1 M lithium hexafluorophosphate (LiPF6 ) solution dissolved in ethylene carbonate (EC) and dimethyl carbonate (DMC) (EC:DMC = 1:1)] was added between them. The charge/discharge performance of the cell was evaluated using Arbin automatic battery analyzer within the voltage range of 2.6–4.0 V at the constant current of 0.05C (1C = 170 mAg−1 ).

3 Theoretical Consideration In ionic conduction, an ion (or charge carrier) hops from one site to another and hence charge flows. Charge carrier formation process is similar to defect pair generation, but, in NASICON conductors, charge carriers are already present in the sample. Trapping of the ions is not possible in this kind of conductors because the dopants are similar in size and electronegativity with the constituents is to be replaced. So, the thermal generation of charge carriers is not possible here. The temperature dependency of ionic conductivity comes only from the ion hopping rate  ν = ν0 exp

Sm k



  Hm exp − kT

(1)

 Hm where ν0 = 2Md 2 is the fundamental attempt frequency of ion hopping, ν is the hopping rate of mobile ion or “relaxation frequency” [6], Hm is the enthalpy for charge carrier migration, M is the mass of lithium ion, and Sm is the entropy [7]. In general, for all solid-state ionic conductors, the temperature dependency of DC ionic conductivity is given by Arrhenius-type equation   Ea σ0 exp − σ (T ) = T kT

(2)

where σ0 is the pre-exponential factor, k is the Boltzmann constant, E a is the energy barrier which has to be overcome for long-range ionic hopping or better known as activation energy for ionic conduction. E a can be evaluated from the slope of ln(σ T ) versus T1 plot. If l and A are the thickness and area of the sample, respectively, the total DC conductivity can be evaluated by

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σt =

1 l Rt A

(3)

where Rt is the total (Grain + Grain boundary) resistance calculated from the complex impedance plot.

4 Results and Discussion 4.1 Structural Analysis X-ray diffraction result confirms the formation of NASICON-based LiGe2 (PO4 )3 (LGP) phase [8]. Pattern showed shift in peak position, and it is due to the substitution of Al and Si in place of Ge and P, respectively [9]. The crystal structure consists of GeO6 octahedra and PO4 tetrahedra connected each other by their corners. There are two positions of Li+ ion: M1(0,0,0) and M2(0.07,0.34,0.08) in this structure [5]. Figure 1 shows the Rietveld refinement of the XRD pattern with R-3c space group. Absence of any broad hump in Fig. 1 suggests maximum conversion of glass to crystalline phase. The refined structural parameters of the major phase (LAGPS) are given in Table 1. The refinement shows that 6.69% AlPO4 (LAGPS: 93.31%) of the total weight exists in the sample. All the estimated standard deviations (e. s. d.) are obtained from Berar’s method [10, 11].

*

20000

Intensity/a.u.

15000

*

10000

5000

*

*

* #

Phase quantification:

93.31% *# LAGPS: AlPO4: 6.69%

#

0

* * *

** * * * * * * * ** * **

-5000 20

30

40

50

60

70

2θ/ Degree

Fig. 1 Experimental (•) and calculated (-) XRD pattern of LAGPS. The vertical lines show the Bragg positions. The difference profile is shown at the bottom

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Table 1 Typical crystallographic and structure parameters after refinement a

b

c

Rwp , Rexp

Volume Density Goodness of (unit cell) (Calculated) fit (χ2 ) and Z

8.2498(5) Å 8.2498(5) Å 20.644(2) Å 1405.01 A3 , 6

3.62 gcm-3

4.03

7.10%, 3.54%

4.2 Electrical Analysis Cole-Cole [12] plot for the glass ceramics sample measured at 223 K is shown in Fig. 2. In LAGPS systems, the ionic conductivity contribution, from Li+ ions, present in bulk/grain is shown at high-frequency region and at low frequency, it is from grain boundary. So the resistivity is related to grain and grain boundary at high and low frequencies, respectively [13]. In Fig. 2, two depressed semicircle segments are observed, corresponding to grain and grain boundary. These semicircles were fitted using nonlinear least square fit method, and the grain and grain boundary resistances are found from the intercepts of the fitted semicircles with real axis [9, 14]. Resistance is found to decrease with the increase in temperature suggesting lower-energy barrier for charge carrier hopping. Total DC ionic conductivities at different temperatures are calculated using Eq. (3). The calculated activation energy of ionic conduction (E a ) is 0.45 eV. To resolve the contribution to the impedance from bulk and grain boundary, measurements were carried out at low temperatures ranging from 223 to 273 K apart -2.0x105

Fitted curve for grain boundary Fitted curve for grain 223K

Z"/ohm

-1.5x105

-1.0x105

-5.0x104

0.0

0.0

5.0x104

1.0x105

Z'/ohm Fig. 2 Z” versus Z’ plot at 223 K

1.5x105

2.0x105

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Fig. 3 σac versus frequency plot at different temperatures

σac/Scm-1

10-3

303K 273K

10-4

253K 10-5

10-6 102

223K

103

104

105

106

Frequency/Hz

from the measurement at 373 K. Figure 3 shows the real part of AC conductivity at different temperatures for heat-treated sample. Due to increase in ion hopping rate with temperature (Eq. 1), σac increases significantly up to a value 2.03 × 10−4 Scm−1 at room temperature (303 K). Figure shows a sharp drop in σac at lower-frequency region (103–104 Hz). With the increase in temperature, the curves merge with each other. This indicates the accumulation of space charge at the electrode interface, and it is significantly high at higher temperature due to more ionic hopping. There are two almost flat regions in the mid-frequency range at lower temperatures (223–273 K), and they are attributed to the DC conductivity due to grain and grain boundary, respectively. But, at 303 K, these two regions are merged and give a combined contribution to DC conductivity. Further, it is seen that the ionic conductivity increases at high-frequency region because of the reduction in energy barrier with the increase in temperature [15]. Figure 4 shows the first three cycles of galvanostatic charge–discharge profiles of LFP/LAGPS/Li cell in the voltage range of 2.6–4.0 V at a constant current rate of 0.05C. The voltage plateau is nearly at 3.63 V for charging and at 3.21 V for discharging of the first cycle at 0.05C. These values are different from the reaction potential (3.45 V) of lithium intercalation/deintercalation of LFP, and this is due to high interfacial resistance [4]. At the first charging cycle, the specific capacity calculated is 154 mAhg−1 which is approximately 90% of the theoretical capacity (170 mAhg−1 ) and after that the charge and discharge capacities are almost fixed at nearly 130 mAhg−1 (approximately 76% of theoretical capacity). The low values of charge and discharge capacities of the cell are attributed to the high interfacial resistance at the electrolyte/electrode interface. Figure 5 depicts the values of discharge capacity and coulombic efficiency at different cycles. The capacity retention up to 20th cycle is almost 100% which reveals that LAGPS can be used in solid-state Li ion batteries. Figure 6 shows the impedance spectra of LFP/LAGPS/Li cell at room temperature. The high-frequency and mid-frequency fitted semicircles reveal the resistance of bulk electrolyte and the

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237

4.0

Voltage/V vs Li/Li+

3.8 3.6

1st cycle 2nd cycle 3rd cycle

3.4 3.2 3.0 2.8 2.6

0

20

40

60

80

100

120

140

160

-1

Specific capacity/mAhg

Fig. 4 Galvanostatic charge and discharge curves of LFP/LAGPS/Li cell at 0.05C at room temperature

100

150 80

125

60

100

Discharge Capacity Coulombic efficiency

75

40

50 20

25 0

0.05C

Room temperature

0

5

10

15

20

Coulombic efficenecy/%

Discharge Capacity/mAhg-1

175

0

Cycle Number Fig. 5 Cyclic performance of specific discharge capacity and coulombic efficiency of the cell at 0.05C at room temperature

electrolyte/electrode interface, respectively. The values of these two resistances are found to be 500 and 1507 , respectively. In future work, we are focusing to improve the interfacial contact to achieve maximum capacity.

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Fig. 6 Cole-Cole plot of LFP/LAGPS/Li cell at room temperature

Fitted curve for interfacial resistance Fitted curve for bulk electrolyte resistance

-1400

Experimental data

Z"/Ohm

-1200 -1000 -800 -600 -400 -200 0 0

200

400

600

800

1000 1200

1400

1600

Z'/ohm

5 Conclusions LGP glass ceramics doping with Al and Si was successfully prepared using the conventional melt-quenching technique. Material showed correct phase formation and microstructure, and a good value of ionic conductivity (~2.03 × 10−4 Scm−1 ) was achieved at room temperature. Rietveld refinement confirmed the formation of desired phase (Li1.5 Al0.5 Ge1.5 P2.9 Si0.1 O12 , LAGPS) and used to quantify the amount of major and impurity phases. Low-temperature electrical measurement could distinguish the contribution of grain and grain boundary to the overall bulk resistance. Performance of the solid-state cell exhibited a good capacity retention and coulombic efficiency. Improvement in electrochemical performance by reducing the overall resistance of the cell can succeed for use of the material as commercial solid-state electrolytes.

References 1. Huang, M., Liu, T., Deng, Y.-F., Geng, H.-X., Shen, Y., Lin, Y.-H., Nan, C.-W.: Effect of sintering temperature on structure and ionic conductivity of Li7−x La3 Zr2 O12−0.5x (x = 0.5 ~ 0.7) ceramics. Solid State Ionics 204–205, 41–45 (2011) 2. Ito, S., Nakakita, M., Aihara, Y., Uehara, T., Machida, N.: A synthesis of crystalline Li7 P3 S11 solid electrolyte from 1,2-dimethoxyethane solvent. J. Power Sources 271, 342–345 (2014) 3. Kim, K.-M., Shin, D.-O., Lee, Y.-G.: Effects of preparation conditions on the ionic conductivity of hydrothermally synthesized Li1+ x Alx Ti2-x (PO4 )3 solid electrolytes. Electrochim. Acta 176, 1364–1373 (2015) 4. Zhao, Erqing, Ma, Furui, Guo, Yudi, Jin, Yongcheng: Stable LATP/LAGP double-layer solid electrolyte prepared via a simple dry-pressing method for solid state lithium ion batteries. RSC Adv. 6, 92579–92585 (2016)

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5. Das, A., Krishna, P.S.R., Goswami, M., Krishnan, M.: Structural analysis of Al and Si substituted lithium germanium phosphate glass-ceramics using neutron and X-ray diffraction. J. Solid State Chem. 271, 74–80 (2019) 6. Wert, C., Zener, C.: Interstitial atomic diffusion coefficients. Phys. Rev. 76(8), 1169–1175 (1949) 7. Francisco, B.E., Stoldt, C.R.: Energetics of ion transport in NASICON-type electrolytes. J. Phy. Chem. C 119(29), 16432–16442 (2015) 8. Alami, M., Brochu, R., Soubeyroux, J.L., Gravereau, P., Le Flem, G., Hagenmuller, P.: Structure and thermal expansion of LiGe2 (PO4 )3 . J. Solid State Chem. 90(2), 185–193 (1991) 9. Das, A., Goswami, M., Krishnan, M.: Study on electrical and structural properties in SiO2 substituted Li2 O-Al2 O3 -GeO2 -P2 O5 glass-ceramic systems. Ceram. Int. 44(11), 13373–13380 (2018) 10. Berar, J.F., Lelann, P.: E.s.d.’s and estimated probable error obtained in Rietveld refinements with local correlations. J. Appl. Cryst. 24, 1–5 (1991) 11. Berar, J.F.: Data optimization and propagation of errors in powder diffraction. Acc. Pow. Diff. II, NIST Sp. Pub. 63, 846 (1992) 12. Cole, K.S., Cole, R.H.: Dispersion and absorption in dielectrics I. Alternating current characteristics. J. Chem. Phys. 9(4), 341–351 (1941) 13. Chung, H., Kang, B.: Increase in grain boundary ionic conductivity of Li1.5 Al0.5 Ge1.5 (PO4 )3 by adding excess lithium. Solid State Ion. 263, 125–130 (2014) 14. Crawford, J.F.: A non-iterative method for fitting circular arcs to measured points. Nucl. Instrum. Methods Phys. Res. 211, 223–225 (1983) 15. Brahma, S., Choudhary, R.N.P., Thakur, A.K.: AC impedance analysis of LaLiMo2 O8 electroceramics. Phys. B 355(1–4), 188–201 (2005)

Adaptive Relaying Scheme for a Distribution Network with Highly Penetrated Inverter Based Distributed Generations Kirti Gupta and Saumendra Sarangi

1 Introduction With the increase in deployment of inverter-based distribution generations (IBDGs) in the modern grid, the analysis to develop a reliable protection scheme also needs modification. Earlier, the protection system was designed according to the synchronous generators used conventionally which have a contribution of 6–10 p.u. in the fault current. On the contrary, the inverter operation in the IBDGs limits this contribution to a maximum of 3 p.u. in order to protect its electronic devices connected [1]. This behaviour imposes to modify the setting of overcurrent relays (OCRs) used for the protection of the distribution system.

2 Literature Review The literature survey suggests a solution to this problem can be broadly classified into two groups. The first group consists of variation in IBDG parameters like the disintegration of DGs [2, 3], integration of distributed generations (DG)s with its optimum placement, rating, type, etc. [4, 5]. It is obtained through various optimization techniques. The second group consists of modifications in overcurrent relay (OCR) parameters by either offline or online method. The offline method consists of applying optimization techniques to find the optimum setting of the relays corresponding to a particular operation scenario keeping in mind the various constraints K. Gupta (B) National Institute of Technology Uttarakhand, Srinagar, India e-mail: [email protected] S. Sarangi Motilal Nehru National Institute of Technology Allahabad, Allahabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_24

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and the objective function [6, 7]. On the other hand, the online setting consists of adaptive setting obtained through Artificial Neural Network, multi-agents, hybrid multi-agents, etc. [8, 9]. In this paper, a novel method has been presented considering the application of low voltage ride through (LVRT) [10], problem of staircase current waveform in highly penetrated distribution system [11, 12]. A novel way is presented for the coordinated operation of OCRs using superconducting fault current limiters (SFCLs). In order to maintain the coordinated operation of OCRS appropriate value of SFCL is selected to limit the extra fault current injected in the network. The paper has been organized in the following manner namely, Sect. 3 formulates the problem, Sect. 4 proposes the novel scheme, Sect. 5 shows the result and discussions, Sect. 6 finally concludes the work.

3 Problem Formulation Considering a case of highly penetrated distribution network shown in Fig. 1. The protection scheme recommended by IEEE standard 929 is tabulated in Table 1.

Fig. 1 Variation in fault current from substation

Adaptive Relaying Scheme for a Distribution Network … Table 1 Protection scheme for PV system

243

Terminal voltage (p.u.)/grid frequency (Hz)

Disintegration time (cycles)

V < 0.5

6 cycles

0.5 ≤ V < 0.88

120 cycles

0.88 ≤ V < 1.1

Normal operation

1.1 ≤ V < 1.37

120 cycles

1.37 ≤ V

2 cycles

f < 59.3

6 cycles

59.3 ≤ f ≤ 60.5

Normal operation

60.5 < f

6 cycles

A fault occurs at the location shown making the bus voltages of IBDGs nearer to fault location to dip below 0.5 p.u. making it to disconnect in six cycles. The other IBDGs also shows the disintegration behaviour according to the table shown. The IBDGs showing normal operation will ride through during fault and will provide reactive power support. However, after disconnection of IBDGS within six cycles would increase the substation fault current making the bus voltages to drop further. The IBDGs now would disconnect from the system according to the updated values of bus voltages. The time of operation (t op ) of the overcurrent relay is shown by (1): top =

0.14(TMS) PSM0.02 − 1

(1)

where t op : Time of operation TMS: Time Multiplier Setting PSM: Plug Setting Multiplier The PSM defines the severity of the fault occurred. It is denoted as the ratio of fault current to the threshold value of current. The threshold value is the minimum value of the current necessary to activate the operation of relay. The TMS is used to provide a delay between the operation of primary (nearer to fault) and backup (neighbouring to the primary) relays for coordinated operation. As the operation of overcurrent relays depend on the value fault current and considering the above example it is clear that fault current will have a varying nature. Consequently, demands either the modifications in relay setting or limit this fault current variations in order to maintain the reliable and coordinated operation of relays.

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4 Proposed Method The flowchart of the proposed method is presented in Fig. 2. A particular fault contingency is taken into account. The load flow and short circuit analysis are carried out to obtain the bus voltages. According to the protection scheme of the PV systems tabulated in Table 1 corresponding PV is disconnected in the allowed time. If the extra current after IBDG disintegration in the lines doesn’t affect the coordinated operation of the protective devices then no SFCL addition is needed. On the contrary, if the increment of substation current effects the coordination of relays then suitable value of SFCL should be computed and need to be connected in the system. Fig. 2 The flowchart of proposed method

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Fig. 3 Equivalent model for PV system

4.1 Fault Analysis Many authors have presented different ways for analysis of fault in case of IBDG integrated system [12]. Here BIBC and BCBV matrix approach is taken into account with IBDGs represented as shown in Fig. 3. Considering a four bus balanced three phase distribution system with IBDGs connected on buses 2 and 3 in Fig. 4. The bus numbers, bus voltages, branch currents, node currents, relays and loads are mentioned in it. Applying Kirchoff’s current law (KCL) the relation between the branch and node currents is obtained as follows: I34 = I 4 I23 = (I3 − Iinv3 ) + I34 = (I3 − Iinv3 ) + I4  − →  I12 = I2 − I inv2 + I23 = (I2 − Iinv2 ) + (I3 − Iinv3 ) + I4 Consequently, on applying Kirchoff’s voltage law (KVL) the relation between bus voltages and line drops are obtained. V2 = V1 − Z 12 I12 V3 = V2 − Z 23 I23 = V1 − Z 12 I12 − Z 23 I23 V4 = V3 − Z 34 I34 = V1 − Z 12 I12 − Z 23 I23 − Z 34 I34

Fig. 4 Sample distribution network with IBDG penetration

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In matrix form, the KCL equations can be transformed as: ⎤ ⎡ ⎤ ⎤⎡ 111 I12 (I2 − Iinv2 ) ⎣ I23 ⎦ = ⎣ 0 1 1 ⎦⎣ (I3 − Iinv3 ) ⎦ I4 I34 001 ⎡

In compact form: Ibranch = [BIBC]Inode

(2)

where BIBC = Bus Injection to Branch Current Similarly, the KVL equations can be written in matrix form ⎤ ⎡ ⎤ ⎡ ⎤⎡ ⎤ V2 Z 12 0 0 I12 V1 ⎣ V1 ⎦ − ⎣ V3 ⎦ = ⎣ Z 12 Z 23 0 ⎦⎣ I23 ⎦ V1 V4 I34 Z 12 Z 23 Z 34 ⎡

V = [BCBV]Ibranch

(3)

V = [BCBV][BIBC]Inode

(4)

Combining (2) and (3): or,

where, BCBV = Branch Current to Bus Voltage Note: all are vector quantities The analysis for different symmetrical and unsymmetrical faults to obtain post fault voltages can be obtained by (4). The analysis has not been presented here due to shortage of pages.

4.2 Reactive Current Injection by IBDGs The factors which govern the fault response of IBDG through the control action has been presented and verified both analytically and through simulation. The dynamic voltage support during fault can be expressed as shown in Fig. 5.   du = Vld f  − |Vfault |

(5)

Case: 1 if voltage drop, du > 0.1 p.u. then:



Im i1PQ = Im i1PQldf + K factor ∗ (du − 0.1)

(6)

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Fig. 5 Current contribution by IBDG

if Im(i1PQ ) < imax then:





Re i1PQ = min i max − Im i1PQldf , Re i1PQldf

(7)

Im i1PQ = i max

(8)



Re i1PQ = 0

(9)

otherwise,

where du: Voltage drop at PV bus in (p.u.) V ldf , i1PQ ldf : Load flow voltage and current at PV bus (p.u.) V fault: Voltage at PV bus during fault (p.u.) Im(i1PQ ): Imaginary part of current supplied by PV (p.u.) Re(i1PQ ): Real part of current supplied by PV (p.u.) Case: 2 if voltage drop du ≤ 0.1



Im i1PQ = Im i1PQldf

(10)





Re i1PQ = Re i1PQldf

(11)



where Iinv = Re i1PQ + jIm i1PQ .

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4.3 Superconducting Fault Current Limiters SFCLs have special property that when the current is below its critical value then it remains in the superconducting state (i.e. Z = 0 ) but during fault conditions the current exceeds this critical value and S-N transition occurs, i.e. it goes to normal state from the superconducting state [13]. Equation (12) represents the impedance of SFCL as ⎧ ⎪ 0, if (t < t0 ) ⎪ ⎪ t−t0  21 ⎨  , if (t0 ≤ t < t1 ) (12) Z (t) = Z n 1 − exp − T F ⎪ a , if (t1 ≤ t < t1 ) − t + b (t ) ⎪ 1 1 1 ⎪ ⎩ a1 (t − t1 ) + b2 , if (t ≥ t1 ) where Z n : Impedance saturated at normal temperature T F : Time constant t o : quench starting time t 1 : first recovery starting time t 2 : secondary recovery starting time a1 , b1 , b2 : Coefficients of first-order linear function.

4.4 Coordination Criteria Figure 6 shows the coordination boundary between primary and backup relays. The limits of current has been shown for which the coordination (hatched area) between the relays will be maintained. t 1 and t 2 are the minimum and maximum coordination time interval (CTI) limits. Generally, this limit is in between 0.2 and 0.8 s. Summarizing all these points we can apply this technique proposed in order to obtain the reliable and coordinated operation of the protection devices.

5 Result and Discussions In this paper, the IEEE 12 bus, 11 kV system has been selected which is shown in Fig. 7 with its line and load data in [14]. The proposed method is simulated and tested on DIgSILENT PowerFactory software. The switches are operated according to the system scenarios. Here generator, relays, buses, switches, loads are represented by G, R, B, S, L, respectively, with the corresponding subscripts. A set of SFCLs are placed in series from the external source to the system. The analysis can be carried out for both symmetrical and asymmetrical faults at different locations in the system.

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Fig. 6 Coordination between relays

Fig. 7 Highly IBDG penetrated IEEE 12 bus distribution system

Fig. 8 a Current contribution by IBDG, b Bus voltage profile for symmetrical fault at bus 12

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Fig. 9 Staircase waveform of the network

The fault current contribution by PV is shown in Fig. 8a. It is clearly visible that during fault there is an increment in reactive current injection in order to support bus voltages. Correspondingly, there is a decrement in active current injection maintaining the overall current to be limited within imax . The bus voltages are shown in Fig. 8b for a symmetrical fault at bus 12. According to the protection scheme the corresponding PVs will disintegrate at prescribed time. The staircase waveform of the branch currents are shown in Fig. 9. Here four cases are considered. In first case, all IBDGs are connected in the system. Further, according to the protection scheme different stages are mentioned. The stage 1 denotes the first event of disintegration. Similarly, stage 2 and 3 depicts the second and third event of disintegration, respectively.

6 Conclusion The proposed method is validated on the IEEE 12 bus distribution system and is found to be effective in maintaining the coordinated operation of the protective devices in the network. This approach can further be extended to other inverter-based DGs with different types of faults considering different values of fault impedances.

References 1. Haj-ahmed, M.A.: The influence of inverter-based DGs and their controllers on distribution network protection, vol. 9994 (2013) 2. Brahma, S.M., Girgis, A.A.: Development of adaptive protection scheme for distribution systems with high penetration of distributed generation. In: 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No. 03CH37491), vol. 4, no. 1, pp. 56–63 (2003) 3. Tailor, J.K., Osman, A.H.: Restoration of fuse-recloser coordination in distribution system with high DG penetration. In: IEEE IEEE Power Engineering Society General Meeting, pp. 1–8 (2008)

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4. Chaitusaney, S., Yokoyama, A.: Reliability analysis of distribution system with distributed generation considering loss of protection coordination. In: International Conference on Probabilistic Methods Applied to Power Systems, pp. 1–8 (2006) 5. Naiem, A.F., Hegazy, Y., Abdelaziz, A.Y., Elsharkawy, M.A.: A classification technique for recloser-fuse coordination in distribution systems with distributed generation. IEEE Trans. Power Deliv. 27(1), 176–185 (2012) 6. Yen, M., Conde, A., Hsiao, T., Martín, L., Trevi, T.: Enhanced differential evolution algorithm for coordination of directional overcurrent relays. Electr. Power Syst. Res. 143, 365–375 (2017) 7. Bayati, N., Dadkhah, A., Sadeghi, S.H.H.: Considering variations of network topology in optimal relay coordination using Time-Current-Voltage characteristic (2017) 8. Lai, K., Illindala, M.S., Haj-ahmed, M.A.: Comprehensive protection strategy for an Islanded Microgrid using intelligent relays, vol. 53, no. 1, pp. 47–55 (2017) 9. Jamali, S., Borhani-bahabadi, H.: Self-adaptive relaying scheme of reclosers for fuse saving in distribution networks with DG, vol. 1, no. 1, pp. 8–19 (2017) 10. Heong, K., Tan, C., Bakar, A.H.A., Seng, H., Mokhlis, H., Illias, H.A.: Establishment of fault current characteristics for solar photovoltaic generator considering low voltage ride through and reactive current injection requirement. Renew. Sustain. Energy Rev. 92(May), 478–488 (2018) 11. Fani, B., Dadkhah, M., Karami-horestani, A.: Adaptive protection coordination scheme against the staircase fault current waveforms in PV-dominated distribution systems, pp. 2065–2071 (2018) 12. Hooshyar, H., Baran, M.E.: Fault analysis on distribution feeders with high penetration of PV systems. IEEE Trans. Power Syst. 28(3), 2890–2896 (2013) 13. Rebizant, W., Solak, K., Brusilowicz, B., Benysek, G., Kempski, A., Rusin, J.: Electrical power and energy systems coordination of overcurrent protection relays in networks with superconducting fault current limiters, vol. 95, pp. 307–314 (2018) 14. Gupta, K., Sarangi, S.: Adaptive overcurrent relay setting for distribution system using superconducting fault current limiters. In: 2018 IEEE 8th Power India International Conference (PIICON), Kurukshetra, India, pp. 1–6 (2018)

Optimization in the Operation of Cabinet-Type Solar Dryer for Industrial Applications Vishal D. Chaudhari, Govind N. Kulkarni, and C. M. Sewatkar

1 Introduction Drying is one of the most common and essential processes in agriculture, food, paper and pulp industries. It is a process of moisture removal from a product in order to attain the desired moisture content. Drying of food products helps to increase shelf life and reduce post-harvest losses. Open sun drying is a popular method of drying but has limitations like dust contamination, insect infestation, spoilage due to rain, etc. The solution to these difficulties forms the basis for design and use of solar dryer. Solar dryers can be classified as direct and indirect solar dryers. In direct mode, the product to be dried is exposed directly to solar radiation [1, 2]. Exposure to direct sunlight leads to discoloration and vitamin loss of the product. Local temperature rise is observed to be of unacceptable level in some products [3]. The indirect solar dryers are equipped with solar collectors where air is heated and transported to the stacks in which products are kept. A better control over temperature and air flow is possible in such dryers [4, 5]. Solar dryers are further classified as active and passive. In active dryers, circulation of air is by forced convection, while in passive types the same is through natural convection. The duration of drying reduces with better quality of product in passive dryers compared to open sun drying [4, 6]. Continuous drying of the product is not guaranteed due to the non-uniformity of the temperature in natural convection dryers V. D. Chaudhari · C. M. Sewatkar (B) Department of Mechanical Engineering, Govt. College of Engineering and Research, S.P. Pune University, Avasari, Pune, India e-mail: [email protected] V. D. Chaudhari Department of Mechanical Engineering, Cusrow Wadia Institute of Technology, Pune, India G. N. Kulkarni Department of Mechanical Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_25

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[7, 8]. In active dryers, temperature of the air and flow rate of air could be controlled in a better way as against passive dryers [9–11]. The temperature of the drying medium is a major factor that determines quality of the dried product. High drying temperature damages the nutritional quality of the product, while low drying temperature results in longer drying periods [12– 15]. Moreover, different permissible drying temperatures are maintained to prevent damage to the product [16]. Investigations on performance of solar dryers for industrial applications are not common in the literature. Studies on integration of solar dryers with auxiliaries are also not readily traceable in the literature. On the back ground of escalating energy prices solar heat is emerging as a prospective complementary option in various low temperature industrial drying processes up to 60 °C. Drying temperature governs the drying efficiency and drying time [17]. A correct drying temperature must be maintained to conserve the industrial resources. The challenge lies in the integration of solar input with the auxiliaries to maintain the desired dryer temperature. This paper proposes a mathematical model of an improvised cabinet-type industrial solar dryer that will operate in batches with auxiliaries at set temperature and solar radiation intensity for the location as input and estimates the quantum of auxiliary energy needed to maintain this temperature in the dryer. The objective of the proposed analysis is to ensure effective utilization of solar heat by minimizing the energy consumption of auxiliaries while maintaining a constant temperature.

2 The Improvised Cabinet-Type Dryer Figure 1 shows schematic diagram of the proposed model of the industrial solar dryer. The dryer comprises of a metallic cabinet with pentagonal cross section. Inclined dimension of the pentagon forms width of the dryer (W ). Length of the dryer (L) is normal to the plane of the paper and is not visible. Solar radiation is incident

Fig. 1 Schematic of the improvised cabinet-type industrial solar dryer

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on the aperture W × L. Figure 1 also shows the control volume using dashed line. The dryer space comprises of air, internal surfaces of the walls, tray frames, trays, product to be dried, auxiliary electric heaters, and exhaust fans. Electric heaters are sized suitably to attain uniform heating of the dryer space. Openings at the front and top ensure air circulation in the dryer. Aperture has a double glass glazing. A control panel is used for modulation of electric supply to the heaters and fans. Dryer walls are insulated with glass wool. The insulation is clad with aluminum composite sheets for imparting strength and prevents heat loss to the surrounding. Dryer can be installed at a fixed location in the sunlight, suitably oriented to receive solar heat through the double glass glazing. With solar heat gain, dryer space temperature increases, increasing the product temperature. The drying temperature can be set as per the requirement of the product. Moisture evaporated from the product is driven out in the atmosphere. Fresh ambient air enters the dryer through front openings, mixes with the hot air in the dryer, and becomes hot and humid. Hot and humid air is drawn out of the dryer with the help of the exhaust fans through the top openings. If solar radiation intensity is insufficient to attain the desired temperature, auxiliary heater will be switched on to heat the dryer space and balance demand will be met. In the event of receipt of excessive solar heat, dryer space temperature may rise beyond the desired value. The exhaust fans will then accelerate enabling enhanced mixing of ambient air with the hot air in the dryer space. The operation will continue till dryer space attains the desired temperature. Auxiliary heater and exhaust fan consumption constitute auxiliaries. If auxiliary consumption becomes minimum, utilization of solar heat will be maximum. The aim of this work is to minimize the consumption of auxiliaries over a day.

3 The Mathematical Model Figure 2 shows energy balance across the dryer. Energy transfers across the control volume will have an effect on its internal energy. Energy balance of the control volume can be expressed as follows: Fig. 2 Energy balance of the dryer

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m i cpi

∂T ∂t

= Q S − Q LT − Q LS − Q air

(1)

Left-hand side of Eq. (1) describes change in the internal energy of the dryer space, while right-hand side describes energy interactions across the control volume. In the above equation, mi is the mass of a single internal constituent of the dryer. Internal constituents of the dryer comprise inner wall, tray frames, trays, product to be dried, and air occupying the dryer space. C pi indicates specific heat of the corresponding internal constituent. Cumulative heat capacity of the dryer space can be evaluated by knowing masses and specific heats of the internal constituents. Heat capacities of electric heaters and fans are omitted for simplicity. In the above expression, T is the dryer space temperature. All the internal constituents are assumed to be in thermal equilibrium with each other and at temperature T. Time step under consideration is t. Dryer space receives heat from sun (Qsun ) as well as the auxiliary (QAux ). Total heat supplied to the dryer is denoted as Qs . Heat loss from glazing surface is (Q LT ), while that from the sides is (Q Ls ). The heat losses can be evaluated as follows. Q s = Q Sun + Q Aux

(2)

Q LT = Ut Aa (T − Tamb )

(3)

Q LS = U S A S (T − Tamb )

(4)

The extent of heat loss depends on the temperature of the air in the dryer (T ). Heat carried away by humid air drawn out of the dryer (Q air ) can be estimated as Q air = m air × C p,air × (T − Tambient )

(5)

Merging Eq. (3) and (4) in Eq. (1), 

m i cpi

∂T ∂t

= Q S − Ut Aa (T − Tamb ) − U S A S (T − Tamb ) − m air C p,air (T − Tamb )

(6)

Solution of the differential Eq. (6) may be obtained analytically over a time step t.       Q S − Ut Aa T f − Tambient − U S A S T f − Tamb − m air C p,air T f − Tamb Q S − Ut A g (Ti − Tamb ) − U S A S (Ti − Tamb ) − m air C p,air (Ti − Tamb ) =e

  U A +U A +m C t a air p,air S S  ×t − m c i pi

(7)

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With known values of top loss coefficients (U t ), side heat loss coefficients (U s ), and air flow rate (mair ), Eq. (7) determines the total heat to be supplied (Qs ) to maintain the desired dryer space temperature T. In this equation, subscript i indicates parametric values at the initial instance, while subscript f indicates values at the final instance. Final temperature of the dryer space at the end of a certain time step t can be determined by simplifying Eq. (7). 

T f = − C1 Q S − Ut A g (Ti − Tamb ) − U S A S (Ti − Tamb ) − m air C p,air (Ti − Tamb ) 

    −Q S − Ut A g + U S A S + m air C p,air Tamb / Ut Aa + U S A S + m air C p,air (8)  U A +U A +m C  t a air p,air S S  − ×t m c

i pi where C1 = e . Mass flow rate of the exhaust fans can be obtained by again writing energy balance across the dryer. The heat carried out by the air is equal to the difference between heats supplied and heat utilized and lost from the surfaces. The equation obtained for mass flow rate of the blower is

m˙ blower =

(Q S − Q Sun − Q LT − Q LS ) + Minimum air circulation C p,air (Ti − T )

(9)

If a pressure difference of hw m of water column is maintained across the fan, then the energy consumption of the fans can be calculated. Q fan = PB. t = m˙ blower . g . h w . t kWh

(10)

The auxiliaries supplied to the dryer are auxiliary heat QAux and fan consumption QFan . The aim of the study is to minimize the consumption of auxiliaries. Minimize : Auxiliaries = Q Aux + Q Fan

(11)

4 Illustration The mathematical model is illustrated with a typical solar dryer of 4 × 6 m aperture. The parameters chosen for illustration are given in Table 1. Hourly mean values of the solar radiation on 15th March at Pune are included. The time horizon of 24 h is used with a time step of 10 min. In the industrial drying applications, temperature specifications are stringent. A strict maintenance of temperature ensures optimization of time, economy, and product quality. Drying processes of different products are specified at prescribed temperatures in various investigations [17]. Thus, any value of dryer space temperature up to 85 °C can be set in this dryer.

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Table 1 Data for the illustrative example Dryer aperture W × L

4m × 6 m

Slope of the aperture

21°

Mass of dryer internal constituents

933 kg

Specific heat of the dryer internal constituents

477 kJ/kg-K

Mass of single batch of the product to be loaded 50 kg in the dryer Specific heat of the product

2180 kJ/kg-K

Inner wall

Stainless steel, 1 mm thickness

Wall insulation

Glass wool 75 mm thick, density 48 kg/m3 thermal conductivity—0.04 W/mk

Outer wall

Aluminum composite sheet 3 mm thick. Thermal conductivity—0.3 W/mk

Location

Pune (18.530 North, 73.850 East), India

Day

15th March

Minimum air circulation rate

0.16 kg/s

Pressure difference across the exhaust fans, m of water column

0.35

The mathematical model is solved to obtain dryer space temperature. The dryer space temperature varies with the solar radiation intensity and ambient temperature. The mathematical model also estimates energy consumed by auxiliaries to maintain a set dryer temperature. Auxiliaries can be modulated to obtain the set value of dryer space temperature. For the chosen dryer configuration at Pune, India, on 15th March, variation of dryer space temperature over a single day is shown in Fig. 3. Energies entering the dryer space include solar flux only, and no auxiliary energies are set on. Figure 4 shows the variation of dryer space temperature with a constant temperature line of 55 °C. The linear characteristic indicates dryer space maintained at 55 °C temperature. Three regions A, B, and C can be identified in Fig. 4. Regions A and B appear below the set temperature line. During this period (1 AM to 9 AM and 5 PM to 1 AM), auxiliary heating is needed to maintain the set temperature. Region C above the set temperature line signifies the need of heat removal by enhanced air circulation from 9 AM to 5 PM. The proposed configuration of dryer can be operated at any set temperature. For example, at a set temperature of 55 °C, the dryer space will be maintained at 55 °C for 24 h. The auxiliary heat requirement will be 97.7 kWh while the fan energy consumption of 15.4 kWh during a day. The total auxiliaries are estimated as 113.1 kWh per day. With a variation in set value of temperature total auxiliary energy varies. The objective is to minimize the auxiliaries. Further, with a reduction in set value, the total auxiliary energy required reduces and reaches a minimum at 45 °C. This is shown in Figs. 5 and 6. Figure 6 shows three characteristics with set value of dryer space temperature as abscissa and energy

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Fig. 3 Variation of dryer space temperature over a day with solar heat only

Fig. 4 Region of heat addition and heat removal at a set temperature of 55 °C

consumption in kWh per day as ordinate. First characteristic relates variation of auxiliary heat. The consumption of auxiliary heat increases with set value in a linear way, while exhaust fan consumption decreases nonlinearly. Third characteristic in Fig. 8 shows the effect of increase in set temperature on total auxiliary energy consumption. This characteristic indicates that at a set temperature of 45 °C total auxiliary energy

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Fig. 5 Region of heat addition and heat removal at a set temperature of 45 °C

Fig. 6 Energy consumption at various set temperatures. Note that total auxiliary energy is minimum at set temperature of 45 °C

will be at 97.3 kWh per day. If the dryer is operated at 45 °C, solar energy utilization will be maximum and auxiliary energy consumption will be minimum. The phenomenon can be explained with the help of Fig. 6. Total auxiliary energy consumption initially decreases. This may be attributed to a reduced auxiliary heat

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Fig. 7 Variation of dryer space temperature if dryer is operated in day time with optimum set temperature of 50 °C

requirement at lower values of set temperatures. Blower energy predominates in the total energy consumption at lower values of set temperatures. Blower energy consumption is not as acute as electric heater consumption. Thus, the cumulative effect is reduction in total power with increase in set temperature up to 45 °C. With increase in set temperature beyond 45 °C, contribution of electric heater increases. Total energy consumption rises with set temperature beyond 45 °C. Optimum set temperature is influenced by solar radiation intensity. The higher the solar radiation intensity, the higher will be the value of optimum set temperature. In many industries, drying process is not in demand for 24 h. Especially, when the process is combined with the availability solar heat, it is beneficial to operate a solar appliance in day time. Figure 7 shows the result of operation of the proposed dryer during day time. The dryer operates from 6 AM to 7 PM for 13 h. The optimum value of set temperature increases to 50 °C with a total auxiliary consumption of 39.4 kWh per day. This underlines more effective utilization of solar heat as compared to 24-hour operation. It is beneficial to operate this dryer in daytime. The proposed mathematical model uses basic energy balance and is easy to apply. The model thus assures to be a simple tool in the resource estimation of batch-type industrial dryers suitable for integration with solar heat.

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5 Conclusion Mathematical model of an improvised design of cabinet-type solar dryer suitable for industrial applications is proposed. The model becomes suitable for industrial applications due to inclusion of auxiliaries needed to maintain a constant temperature in the dryer space over a day. The model ensures effective utilization of solar heat by minimizing the auxiliary energy. Results indicate that at a set temperature of 45 °C total auxiliary energy will be minimum at 97.3 kWh per day. If the dryer is operated at 45 °C, solar energy utilization will be maximum and auxiliary energy consumption will be minimum. The optimum value of set temperature increases to 50 °C with a total auxiliary consumption of 39.4 kWh per day if the dryer is operated in daytime. This underlines more effective utilization of solar heat as compared to 24-hour operation. It is beneficial to operate this dryer in daytime. The model thus assures to be a simple tool in the resource estimation of batch-type industrial dryers suitable for integration with solar heat.

References 1. Lawand, T.A.: A solar cabinet dryer. Sol. Energy 10, 158–164 (1966) 2. Gbaha, P., Andoha, H.Y., Sarakaa, J.K., Kouab, B.K., Toure, S.: Experimental investigation of a solar dryer with natural convective heat flow. Renew. Energy 32, 1817–1829 (2007) 3. Ekechukwu, O.V., Norton, B.: Review of solar-energy drying systems II: an overview of solar drying technology. Energy Convers. Manag. 40(6), 615–655 (1999) 4. Sharma, A., Chaen, C.R., Lan, N.V.: Solar-energy drying systems: a review. Renew. Sustain. Energy Rev. 13, 1185–1210 (2009) 5. El-Sebaii A.A., Shalab, S.M.: Experimental investigation of an indirect-mode forced convection solar dryer for drying thymus and mint. Energy Conversion Manag. 74, 109–116 (2013) 6. Sharma, V.K., Sharma, S., Garg, H.P.: Mathematical modelling and experimental evaluation of a natural convection type solar cabinet dryer. Energy Convers. Manag. 31, 65–73 (1991) 7. El-Sebaii, A.A., Aboul-Enein, S., Ramadan, M.R.I., El-Gohary, H.G.: Experimental investigation of an indirect type natural convection solar dryer. Energy Convers. Manag. 43, 2251–2266 (2002) 8. Prasad, J., Vijay, V.K., Tiwari, G.N., Sorayan, V.P.S.: Study on performance evaluation of hybrid drier for turmeric (curcuma longa L.) drying at village scale. J. Food Eng. 75, 497–502 (2006) 9. Bennamoun, L., Belhamri, A.: Design and simulation of a solar dryer for agriculture products. J. Food Eng. 59, 259–266 (2003) 10. Sreekumar, A., Manikantan, P.E., Vijayakumar, K.P.: Performance of indirect solar cabinet dryer. Energy Convers. Manag. 49, 1388–1395 (2008) 11. Benhamoua, A., Fazouane, F., Benyoucef, B.: Simulation of solar dryer performances with forced convection experimentally proved. Phys. Procedia 55, 96–105 (2014) 12. Goyal, R.K., Tiwari, G.N.: Performance of a reverse flat plate absorber cabinet dryer: a new concept. Energy Convers. Manag. 40(4), 385–392 (1999) 13. Leon, M.A., Kumar, S., Bhattacharya, S.C.: A comprehensive procedure for performance evaluation of solar food dryers. Renew. Sustain. Energy Rev. 6, 367–393 (2002) 14. Saleh, A., Badran, I.: Modelling and experimental studies on a domestic solar dryer. Renew. Energy 34, 2239–2245 (2009)

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15. Pin, K.Y., Chuah, T.G., Abdull Rashih, A., Law, C.L., Rasadah, M.A., Choong, T.S.Y.: Drying of betel leaves (Piper betle L.): Qualityand drying kinetics. Drying Technol. 27(1), 149–155 (2009) 16. Chen, H.H., Hernandez, C.E., Huang, T.C.: A study of the drying effect on lemon slices using a closed-type solar dryer. Sol. Energy 78, 97–103 (2005) 17. Hii L.C., Jangam S.K., Ong S.P., Solar drying: fundamentals, applications and innovations, Singapore (2012). ISBN: 978-981-07-3336-0

Modeling of Solar Photovoltaic-Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell to Charge Nissan Leaf Battery of Lithium Ion Type of Electric Vehicle Kamaljyoti Talukdar

1 Introduction At present, vehicles use non-renewable sources as fuel such as petrol, diesel, kerosene, etc., which are depleting in nature. These sources of fuel will become exhausted in coming future. Hence, alternate sources of fuel should be implemented. In ref. [1] authors investigated possibility of charging battery electric vehicles at workplace in Netherlands using solar energy. Also, the feasibility of integrating a local storage to the EV–PV (electric vehicle-photovoltaic) charger to make it grid independent was evaluated. In ref. [2], authors focused on the evaluation of theoretical and experimental aspects related to the different operation modes of a laboratory power architecture, which realized a micro-grid for the charging of road electric and plug-in hybrid vehicles. A first phase of simulations was aimed to evaluate the main energy fluxes within the studied architecture and to identify the energy management strategies, which optimize simultaneously the power requirements from the main grid and charging times of different electric vehicles. A second phase was based on the experimental characterization of the analyzed power architecture, implementing the control strategies evaluated in the simulation environment, through a laboratory acquisition and control system. A brief overview of working of solar vehicle is being discussed in [3]. In [4], authors presented a solar/hydrogen hybrid power system, which reduced the required hydrogen fuel cell power output by combining batteries and supercapacitors in an electric vehicle. In ref. [5], authors designed and simulated a hybrid photovoltaic (PV)-fuel cell generation system employing an electrolyzer for hydrogen generation. In [6], authors described a demonstrative plant, located near Rome (Italy), built to investigate and test some commercial solar-hydrogen technologies. An early proof-of-concept for K. Talukdar (B) Department of Mechanical Engineering, Bineswar Brahma Engineering College, Kokrajhar 783370, Assam, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_26

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a distributed hydrogen fueling option in which renewably generated, high-pressure hydrogen was dispensed at an fuel cell electric vehicle (FCEV) owner’s home is being described in [7]. In the present paper, solar photovoltaic combined with electrolyzer-PEM fuel cell for operating Nissan Leaf electric vehicle of lithium ion type is being presented for Kolkata City, West Bengal, India.

2 System Layout Figure 1 shows the schematic view of combined solar photovoltaic(PV) and electrolyzer-polymer electrolyte membrane(PEM) fuel cell for running the Nissan Leaf’s battery(ion type) which is similar to Fig. 1 in ref. [13] except hospital in ref. [13] is replaced by electric vehicle/car in which during sunshine hours solar radiation falling on PV modules produces current (IPV ) and after meeting the electric vehicle/car requirement (IEV ), extra current (IPV -IEV ) goes to PEM electrolyzer for hydrogen production and stored in tank after compressing to be used by PEM fuel

Solar RadiaƟon (G)

Solar RadiaƟon (G)

Photovoltaic modules IG

Photovoltaic modules

Inverter

IPV

IG

Charge Controller

PEM Electrolyzer IPV-IEV

Gas storage

PEM fuel cell stack

IEV IEV-IPV Electric car

Fig. 1 Schematic view of combined solar photovoltaic modules and electrolyzer-fuel cell stack

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cell during non sunshine hours. Figure 2 shows brief layout along with car during sunshine hours. During non-sunshine hours, current required for electric vehicle’s battery(IEV IPV ) is obtained from PEM fuel stack. Hydrogen stored in gas cylinder is used by PEM fuel cell and supplied to electric battery after passing through charge controller. Figure 3 shows a brief layout along with car during non-sunshine hours. PV modules and inverter for gas compressor

Solar radiaƟon Nissan Nissanleaf leafbaƩery baƩery Charge controller IPV

IPV-IEV

IEV

ConnecƟng rod

PV modules

ConnecƟng rorod

Fuel cell and electrolyzer assembly

Car movement

Fig. 2 Schematic view of car and PV modules and fuel cell assembly during sunshine hours

Nissan Nissanleaf leafbaƩery baƩery

Charge controller IEV-IPV

PV modules Solar radiaƟon and inverter for gas compressor ConnecƟng rod

IEV

ConnecƟng rod

PV modules

Fuel cell and electrolyzer assembly

Car movement

Fig. 3 Schematic view of car and PV modules and fuel cell assembly during non-sunshine hours

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3 Modeling 3.1 Modeling of Lithium Ion Type Rechargeable Battery of Nissan Leaf Electric Vehicle For operating the Nissan Leaf electric vehicle, 40 kWh lithium ion type rechargeable battery which gives 400 km distance coverage [8] at single charge is taken. The system voltage of battery is taken to be 360 V [9]. The vehicle is assumed to be moving at a speed of 35 km/h. In order to convert kWh into Ah, 40 kWh is divided by 360 V. The Ah value obtained is the capacity of the battery. Now, in order to calculate total current(A) required from solar PV modules equation no. 1 is used [10]. i spv,EV =

battery_capacity(Ah) × DoD × EBC autonomy_days × ηchargecontroller

(1)

where depth of discharge (DoD) is considered to be 80% [11], and expected battery capacity (EBC) is considered 130%. The battery charging efficiency is considered to be 90% [12], η charge controller (efficiency of charge controller) to be 85% [10].

3.2 Modeling of Solar PV System The equations used for calculating current from PV modules are obtained from [13]. Also solar radiation, ambient temperature, and wind speed data are obtained from references given in ref. [13]. Wind speed is assumed to be summation of vehicle speed(35 km/h) and wind speed in Kolkata City (for December and May). The number of PV modules in series (Ns ) is given by equation no.8 from [13]: Ns =

Vsystem,EV Vmodule

(2)

where Vsystem, EV is the system voltage of the PV modules (considered 360 V in the present study) and Vmodule is the maximum voltage of a PV module [14]. The number of PV modules in parallel (Np ) is given by equation no. 12 from [13]: where imp is the maximum current of a PV module [14]. Np =

i spv,EV i mp

(3)

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3.3 Modeling of PEM Fuel Cell The different voltage calculations are obtained from ref. [13]. Number of PEM fuel cell stacks in parallel (Nfcparallel ) can be obtained from Eq. 25 from [13] as: Nfcparallel =

i fuelcell,EV i cell

(4)

where ifuelcell,EV is the maximum current required during non-sunshine hours(17:00 h to 5:00 h) and icell is obtained from ref. [13]. Number of fuel cell connected in series (Nfcseries ) is given by equation no.26 from [13], where Vsystem is system voltage (360 V) and Vfc is net voltage obtained from single fuel cell. The hourly hydrogen consumption of fuel cell stack is obtained from Eq. 27 from [13]: m fc,EV =

i fuelcell,EV × Nfcseries × 3600 × 2 2 × F × ηfuel

(5)

where F-Faraday constant (96,500 C/mol), ηfuel -fuel utilization factor for fuel cell(considered 0.9).

3.4 Modeling of PEM Electrolyzer Excess current (IPV -IEV ) after meeting the requirement of Nissan Leaf battery of electric vehicle is sent to PEM electrolyzer for dissociating water present in electrolyzer into oxygen and hydrogen. The number of cells in stack in series is taken as 300, and effective cell area is considered to be 86.4 cm2 [15]. Amount of hydrogen produced (in gram mol)in electrolyzer with 300 cells in series in hourly basis is obtained from Eq. 30 given from [13]: Melec,EV =

(IPV − IEV ) × 300 × 3600 2×F

(6)

3.5 Modeling of Gas Compressor Power required to run gas compressor is obtained from [13]. Also assumed values are obtained from [13].

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Number of solar photovoltaic modules in parallel needed for running gas compressor is obtained from [13]. i spv,compr essor i mp

(7)

i compressortotal × DF peaksunshinehours

(8)

N p,compr essor = where i spv,compressor =

DF—derating factor of photovoltaic modules(1.25) [10] and peak sunshine hours7 h per day [16], icompressortotal —summation of current required for pressurization of hydrogen to be stored in cylinder(during sunshine hours, 6:00 h to 18:00 h). Number of solar photovoltaic modules in series needed for running gas compressor is given by: Ns,compressor =

Vsystem,compressor Vmodule

(9)

where Vsystem,compressor, i.e., system voltage of compressor is considered to be 48 V and Vmodule —34 V [14].

4 Results and Discussions From Sect. 3.1, rated capacity of battery is found to be 111.111 Ah after dividing 40 kWh by 360 V. The current required from PV modules ispv,EV is found to be 45.315 A from equation number 1. The number of PV modules needed in series and parallel for electric vehicle are 11 and 10 obtained from equation no. 2 and 3, respectively. The number of PEM fuel cell needed in parallel is obtained from equation no. 4 which is 1, where ifuelcell,EV is 1.888 A. The PEM fuel cells needed in series is found to be 350. The hourly hydrogen consumption (in non sunshine hours) by PEM fuel cell and hydrogen production in PEM electrolyzer (in sunshine hours) are obtained from Eqs. 5 and 6, respectively, and shown in Fig. 4 and 5 for the month of December and May, respectively. It is seen that summation of hydrogen consumption during non-sunshine hours from 1:00 h to 5:00 h and from 19:00 h to 24:00 h is 301.191 gm mol for both December and May, and summation of hydrogen generated during sunshine hours from 6:00 h to 18:00 h is 710.544 gm mol and 1245.128 gm mol for December and May, respectively. It is seen that hydrogen generation increases from 6:00 h to 12:00 h and again decreases to 18:00 h due to the fact that solar radiation increases from 6:00 h to 12:00 h and again decreases to 18:00 h. Hence, greater radiation means that greater current generation (IPV ) by PV modules and greater

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120

Hydrogen consumed(gm mol/h)

Gram mole(gm mol)

100

Hydrogen produced (gm mol/h)

80 60 40 20 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (in hours) Fig. 4 Hydrogen consumption and production for the month of December

180

Hydrogen consumed(gm mol/h) Hydrogen produced(gm mol/h)

160

Gram mole(gm mol)

140 120 100 80 60 40 20 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (in hours) Fig. 5 Hydrogen consumption and production for the month of May

current (IPV -IEV ) availability by electrolyzer thereby greater production of hydrogen according to Eq. 6. For gas compressor, number of PV modules needed in parallel and series are 34 and 2 from Eqs. 7 and 9, respectively. Table 1 below shows rating of different power system components.

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Table 1 Rating of different power system components Components of power system

Rating

No. of photovoltaic modules in parallel(Np ) for electric vehicle

10

No. of photovoltaic modules in series(Ns ) for electric vehicle

11

Electrolyzer input at 360 V

15.661 kW

No. of fuel cell in a stack(Nfcseries )

350

No. of fuel cells stacks(Nfcparallel )

1

Maximum output of each fuel cell stack

59.32A,21.24 kW

Gas compressor rating at 48 V

7.213 kW

No. of photovoltaic modules in parallel for gas compressor(Np,compressor )

34

No. of photovoltaic modules in series for gas compressor

2

5 Conclusions Based on the observations, it is found that electric vehicle having Nissan Leaf battery of 400kWh capacity can be operated throughout the day and year with the help of 10 and 11 photovoltaic modules in parallel and series, respectively, of Central Electronics Limited Make PM 150 with 15.661 k W electrolyzer, 350 fuel cell stacks collection in series with 21.24 k W power. Also compressor rating of 7.213 k W and 34 and 2 photovoltaic modules in parallel and series, respectively, of Central Electronics Limited Make PM 150 for powering compressor are sufficient. The advantage of this work presented in this paper is that electric vehicle can be recharged at any location, thereby needing no re-fueling station.

References 1. Mouli, G.R.C., Bauer, P., Zemen, M.: System design for a solar powered electric vehicle charging station for workplaces. Appl. Energy 168, 434–443 (2016) 2. Capassoa, C., Iannuzzib, D., Veneria, O.: DC Charging Station for Electric and Plug-In Vehicles. Energy Procedia 61, 1126–1129 (2014) 3. Wamborikar, Y.G., Sinha, A.: Solar Powered Vehicle. In Proceedings of the World Congress on Engineering and Computer Science, ISBN: 978-988-18210-0-3, San Francisco, USA(2010) 4. A power system combining batteries and supercapacitors in a solar/hydrogen hybrid electric vehicle, http:// ieeexplore.ieee.org/document/1554636/), last accessed 2018/12/20 5. El Shatter, ThF, Eskandar, M.N., El Hagry, M.T.: Hybrid PV/Fuel cell system design and simulation. Renewable Energy 27(3), 479–485 (2002) 6. Galli, S., Stefanoni, M.: Development of a solar hydrogen cycle in Italy. Int. J. Hydrogen Energy 22(5), 453–458 (1997) 7. Kelly, N.A., Gibson, T.L., Ouwerkerk, D.B.: Generation of high-pressure hydrogen for fuel cell electric vehicles using photovoltaic-powered water electrolysis. Int. J. Hydrogen Energy 36, 15803–15825 (2011) 8. Electric vehicle lithium ion battery|NISSAN|TECHNOLOGICAL…, https://www.nissan-glo bal.com/EN/TECHNOLOGY/OVERVIEW/li_ion_ev.html, last accessed 2018/12/2

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9. Nissan LEAF-roperld, http://www.roperld.com/science/NissanLEAFII.htm, last accessed 2018/12/5 10. Telecommunication Engineering Centre (TEC),New Delhi, Planning and maintenance guidelines for SPV power, http://www.tec.gov.in/guidelines.html, last accessed on 2011/8/15 11. A Comparison of Lead Acid to Lithium-ion in Stationary Storage Applications, https://www.bat terypoweronline.com/wp-content/uploads/2012/07/Lead-acid-white-paper.pdf, last accessed on 2018/11/3 12. Car Battery Efficiencies - Stanford University, http://large.stanford.edu/courses/2010/ph240/ sun1/, last accessed on 2018/12/7 13. Talukdar, K.: Modeling and Analysis of Solar Photovoltaic Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell For Running a Hospital in Remote Area in Kolkata, India. International Journal of Renewable Energy Development 6(2), 181–191 (2017) 14. Solar photovoltaic modules PM 150,http:-celindia-coin.preview1.cp247.net/cal/PM150.pdf, last accessed on 2012/4/16 15. Dale, N.V., Mann, M.D., Salehfar, H.: Semi-empirical model based on thermodynamic principles for determining 6 kW PEM electrolyzer stack characteristics. J. Power Sources 185(2), 1348–1353 (2008) 16. Some insights into solar photovoltaics-solar home lighting system, NABARD Technical Digest 7,http://www.nabard.org, last accessed on 2009/6/26

Performance Study of an Anode Flow Field Design Used in PEMFC Application S. A. Yogesha , Prakash C. Ghosh, and Raja Munusamy

1 Introduction The impact on the environment due to non-renewable energy sources and the shortage of fossil fuel reserves have given the opportunity to the present civilization for considering alternative energy options. Renewable energy sources are the energy sources which are produced continuously in nature. Some of the renewable energy sources are solar energy, hydroelectric energy, wind energy, biomass energy, and geothermal energy [1]. Renewable energy resources can provide a sustainable solution to fulfill the world’s future energy demand as they are environmentally friendly. Many types of renewable energy technologies are presently used around the world for small-scale electricity generation. These include solar, wind, tidal, hydroelectric, and geothermal technologies, and these technologies can be used for various applications such as transportation, cooling or heating of water, fulfilling rural energy demand problem, and so on [2]. All of these alternative energy resources have their own potential to fulfill the future energy demand, and at the same time, they all have their respective limitations. Intensive research has already been carried out throughout the world for last few decades on all of these renewable energy sources. Still, every one of them suffers from some disadvantages. So, it requires some improvement and evolution in these renewable technologies with time. The most concerned limitation with the solar (both photovoltaic and thermal), wind, and tidal is the lack of certainty as well as the availability of sunshine, wind speed, and tides depending on location and different time in a year limit for the application of this kind of renewable technologies. In the case of hydroelectric technology, the reservoir required for the dam in the upstream S. A. Yogesha · P. C. Ghosh (B) Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India e-mail: [email protected] R. Munusamy Engineering Research Centre, Tata Motor Limited, Pimpri, Pune 400014, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_27

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causes habitat problem on both water and in surrounding land. It also suffers from flow shortage and often leads to ecosystem damage. A non-renewable resource (also called a finite resource) is a resource that cannot be renewed or regenerated quickly enough to maintain with their use. Examples for non-renewable energy sources are fossil fuels (such as coal, wood, nuclear fuels petroleum), oil, and natural gas. Carbon is the main element of fossil fuels. Other nonrenewable resources such as nuclear fission are also existing and promising energy technologies on a global basis because of its large radioactive resources. India and Australia’s large thorium and uranium reserves can be given a few examples of for nuclear resources. But the safety and environmental concerns are two major issues associated with nuclear technology [3, 4]. Nuclear power is a relatively clean form of energy and is suitable for large-scale electrical power generation; however, the highlevel radioactive waste is a major concern and the issues related to the nuclear waste management must be considered before planning any large-scale energy production [5]. Among different energy conversion options, fuel cell systems offer an efficient and sustainable energy conversion solution. The pollution and contamination issues caused by the burning of conventional fuel sources can be fairly reduced by using hydrogen fuel in the fuel cell. Due to the production of hazardous gases like carbon dioxide, greenhouse gases, toxic pollution, shortage of oil production, and excess requirement of electric power throughout the world, fuel cells must be considered for coming generation [6]. In the present condition, the requirement of fuel cells in vehicles has become more in demand. In opposite to the conventional battery, the fuel cell generates electrical energy without storing it and continues the same as long as the hydrogen fuel is supplied [7, 8]. The fuel cell powered vehicles has a longer driving capacity without charging the battery for a long period. It has high energy efficiency and very fewer emissions due to the direct conversion of hydrogen fuel into electrical energy in comparison with the internal combustion engine [9, 10]. Single cell assembly construction as shown in Fig. 1.

2 Components of a PEMFC Air-Cooled Stack 2.1 Anode and Cathode Bipolar Plates Anode in a PEMFC provides the sites for fuel gas to react with O2 ion delivered by the electrolyte. It also conserves the charge neutrality of the complete system. The electrochemical reaction of the fuel occurs at the interface between the anode and electrolyte which depend on the material properties of anode. Due to high electrical conductivity, low ionic conductivity, and high activity of the electrochemical reactions, mostly used anode material is graphite. Similar to the anode bipolar plate,

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Fig. 1 Construction details of single-cell assembly

the cathode bipolar plate material should possess the all properties like high electrical conductivity, high catalytic activity, gas-impermeable commonly used anode material is graphite.

2.2 MEA (Membrane Electrode Assembly) Membrane electrolyte assembly plays the most important role in fuel cell by forming a bridge between cathode and anode through an electrochemical reaction. Therefore, it is designed as the heart of a fuel cell. Sandwiching the membrane between GDLs at a predefined temperature, pressure, and time form a MEA. The inhouse prepared membrane electrode assembly as shown in Fig. 2.

2.3 Sealing Sealing is used for providing compression and leak proof. Silicone material is used widely material for sealing.

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Fig. 2 Membrane electrode assembly

2.4 End Plates As the name suggests, the function of end plates is to hold entire cell components firmly between the two. It is conventionally made of metal to strengthen the hold and therefore less bending of the plates that confirms uniform clamping load over the active area. Nut bolt pairs are normally adopted to clamp the cell outside the active area. End plates also act as current collectors in many cases.

2.5 Current Collectors Current collectors collect the current of the electrochemical reaction in a controlled area. Current collector used copper plate with gold plating.

3 Results and Discussion 3.1 Flow Configuration Developed conceptual design of the graphite plates with four different types flow channels or flow fields is used for anode side flow configuration as shown in Figs. 3, 4, 5, and 6. The designs are namely (a) serpentine flow with uniform curvature, (b) combined pin and parallel flow, (c) serpentine flow with 90-degree uniform curvature, and (d) combination of parallel and serpentine flow channels, respectively.

Performance Study of an Anode Flow Field Design …

Fig. 3 Combination of parallel and serpentine flow

Fig. 4 Combined pin and parallel flow

Fig. 5 Serpentine flow with uniform curvature

Fig. 6 Serpentine flow with 90-degree uniform curvature

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3.2 Numerical Simulation Comparison of velocity profiles for four different flow channels is discussed. Control volume approach is used for solving the velocity and pressure drop using commercial software. Velocity contours and distribution graph are as shown in Fig. 7, 8, 9, and 10. It is clear that the 1st and 2nd case velocity profiles are non-uniform over 3rd and 4th case flow channels. It is evident the 3rd and 4th cases have more uniform flow channels which make the selection flow channels.

Fig. 7 Velocity contours and distribution curve combination of parallel and serpentine flow

Fig. 8 Velocity contours and distribution curve combined pin and parallel flow

Fig. 9 Velocity contours and distribution curve serpentine flow with uniform curvature

Fig. 10 Velocity contours and distribution curve serpentine flow with 90-degree uniform curvature

Performance Study of an Anode Flow Field Design …

a

281

b

Fig. 11 Schematic of the manufactured flow fields structure a combination of parallel and serpentine flow b combined pin and parallel flow

a

b

Fig. 12 Schematic of the manufactured flow fields structure a serpentine flow with uniform curvature b serpentine flow with 90-degree uniform curvature

The cell unit is manufactured with four different anode flow channels configuration keeping same flow geometry of cathode configuration. The single-cell unit considered in this paper is 150 cm2 active area of the membrane electrolyte membrane (MEA). MEA consists of five layers standard membrane electrode assembly prepared. Dispensed type silicon gasket is used for assembly. Its components and assembly are shown in Fig. 13. This paper mainly focuses on the flow field impact in performance of aircooled fuel cell stack unit. Four different flow configuration anode bipolar plates are designed as shown in Fig. 11a, b, 12a, b. The serpentine flow with uniform curvature, combined pin and parallel flow, serpentine flow with 90-degree uniform curvature, and combination of parallel and serpentine flow channels were designed and fabricated.

Fig. 13 Fabricated air-cooled stack

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3.3 Experimental Procedure The test unit consists of single-cell unit assembly along with balance of plant system components, which includes air system blower, hydrogen system regulator and actuator and controlling system. The suction-type blower is used with operating voltage of 24VDC, and hydrogen side pressure is regulated to 0.3 barg for all the operating currents. The flow field impacted in the performance of single-cell unit as observed in the experimental setup. The flow field with the serpentine flow with uniform curvature has higher performance comparatively other flow field designs as shown in Fig. 14. The combined pin and parallel flow design offers lowest performance.

Fig. 14 Polarization curve for four different designs of anode flow channels

Fig. 15 Temperature variation at different locations

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Fig. 16 Polarization curve temperature variation

Similarly conducted experiment for 35 °C, 40 °C, 45 °C, 50 °C with initial ambient temperature of 25 °C. The polarization results of single cell performance as shown in Fig. 16, due to increase in temperature the performance of cell is increased. Combination of parallel and serpentine with uniform curvature flow field is around 30 W, which is 1.4 higher than the worst-case design. To test the temperature impact on the fuel cell stack performance heater pad attached to the end plate and initially ambient temperature is 250 C started heating the cell up to 350 C. Three thermocouples are mounted inside open-cathode channels to measure the temperature the thermocouples are mounted at start, middle, end of the cathode cells. The variation of temperature at three different locations is plotted in Fig. 15.

4 Conclusion The single-cell open-cathode PEMFC single cell designed to study the effects of anode flow geometry. Four different anode flow configurations are designed with 150 cm2 active area. The fuel cell stack unit is manufactured, and results confirmed that flow field with uniform curvature derived best results of 350 mA/cm2 . The tested results indicated that serpentine flow with uniform curvature has maximum power density compared to other three designs. Design offered steady performance for more than 45 min when it was operated at a current of density of 350 mA.cm2 . In addition, serpentine flow with uniform curvature tested for increasing temperature up to 60°C and confirmed that higher temperature is given good results compared to lower operating temperature. Therefore, the flow field designs may be selected

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properly to get improved performance in polymer electrolyte membrane fuel cell stack. Results of this paper certainly provide useful guidelines for anode flow fields used for air-cooled PEM fuel cell stack.

References 1. Squadrito, G., et al.: Design and development of a 7 kW polymer electrolyte membrane fuel cell stack for UPS application. Int. J. Hydrogen Energy 35(18), 9983–9989 (2010) 2. Han, I., Jeong, J., Shin, H.K.: PEM fuel-cell stack design for improved fuel utilization. Int. J. Hydrogen Energy 38(27), 11996–12006 (2013) 3. I. Journal, H. Energy, S. S. Foundation, and M. Road, “Development of polymer electrolyte membrane fuel cell stack 4. L. Mu, W. Cheng, L. Zhi-xiang, and M. Zong-qiang, “The development and performance analysis of all-China- made PEM fuel cell unit and 1 kW level fuel cell stack,” vol. 7, pp. 2–7, 2011 5. R. Bove, T. Malkow, A. Saturnio, and G. Tsotridis, “PEM fuel cell stack testing in the framework of an EU-harmonized fuel cell testing protocol: Results for an 11 kW stack,” vol. 180, pp. 452– 460, 2008 6. D. Chu and R. Jiang, “Comparative studies of polymer electrolyte membrane fuel cell stack and single cell,” pp. 226–234, 1999 7. P. Corbo, F. Migliardini, and O. Veneri, “Performance investigation of 2. 4 kW PEM fuel cell stack in vehicles,” vol. 32, pp. 4340–4349, 2007 8. Han, I., Kho, B., Cho, S.: Development of a polymer electrolyte membrane fuel cell stack for an underwater vehicle. J. Power Sources 304, 244–254 (2016) 9. Sasmito, A.P., Birgersson, E., Lum, K.W., Mujumdar, A.S.: Fan selection and stack design for open-cathode polymer electrolyte fuel cell stacks. Renew. Energy 37(1), 325–332 (2012) 10. H. I. Lee, C. H. Lee, T. Y. Oh, S. G. Choi, I. W. Park, and K. K. Baek, “Development of 1 kW class polymer electrolyte membrane fuel cell power generation system,” vol. 107, pp. 110–119, 2002

Effect of Top Losses and Imperfect Regeneration on Power Output and Thermal Efficiency of a Solar Low Delta-T Stirling Engine Siddharth Ramachandran , Naveen Kumar, and Mallina Venkata Timmaraju

1 Introduction The productive utilization of renewable energy sources is pivotal for all nations due to persistent increase in energy demand and environmental concerns associated with rise in the usage of fossil fuels. Therefore, Stirling engines are better candidates at present as they have no emission, good lifespan, environment-friendly devices, and reasonable competency in converting low-grade thermal energy into mechanical work. Exhaustive studies have been carried out during last two decades on design, optimization, and analysis of concentrating-type Stirling engines [1]. Some authors [2–4] used finite time thermodynamic analysis (FTT) as the tool for design and optimization of solar dish Stirling engines. The performance of a dish-type solar Stirling engine using FTT analysis has been studied and optimized the design parameters for maximum power output condition at high-temperature ranges, i.e., 1300–923 K. Although few authors [5] explored the effective practice of non-concentrating-type Stirling engines, but design parameters were not optimized using FTT. The feasibility of existing low-temperature differential Stirling engines (LTDSE) is very cost effective in terms of small-scale distributed low-grade thermal energy [5]. The thermodynamic performance of LTDSE on a laboratory scale supports the fact that these systems can be implemented on a large scale with watt-level power production. Modified Schmidt models are being used to predict the performance of LTDSE by adding real-time losses and optimized some power influencing geometric parameters such as swept volume ratio, dead volume ratio as well as phase angle for various configurations of Stirling engines [6]. These models are inadequate to predict accurately because of large deviation between the ideal cycle and real cycle

S. Ramachandran (B) · N. Kumar · M. V. Timmaraju Indian Institute of Information Technology, Design and Manufacturing Kancheepuram, Chennai 600127, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_28

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[7]. Recently, Grosu et al. [8] introduced a fairly accurate modified Schmidt thermodynamic model, in which various real-time losses were combined and tested on laboratory-level LTDSE model. While accounting various energy losses in Stirling engines, the heat transfer imperfections cause the majority of the losses, i.e., up to 51% of heat loss. Although, thermal losses due to heat transfer (top losses) is a major loss to be considered in solar Stirling engines, literature is not available in this regard. The goal of the current work is to formulate a fairly accurate model to predict the performance of solar LTDSE using FTT analysis. Therefore, a model of a solar non-concentrating-type Stirling engine with thermal losses and thermodynamic irreversibilities is developed. The effect of top loss coefficient and absorption of radiation in the glazing is also incorporated with FTT analysis for the first time. Further, the significance of time for regeneration process on maximum power output and thermal efficiency of the system are also discussed. The modified FTT model with imperfect regeneration is validated with experimental data available from literature [7].

2 Finite Time Thermodynamic Model of LTDSE Thermodynamic cycle of solar LTDSE operates between the temperature of the absorber plate (source) and ambient air (sink). The absorber plate of the solar nonconcentrating Stirling engine is single glazed in order to reduce the top losses (see Fig. 1). Generally, the working fluid is air and material of the absorber plate, displacer, and regenerator are aluminum, plastic foam, and metal wire mesh, respectively. The mechanical prime mover is the unit where the indicated power available from the Stirling cycle is converted to mechanical work/electrical energy with a certain

Fig. 1 Schematic of a non-concentrating solar Stirling engine

Effect of Top Losses and Imperfect Regeneration on Power Output …

287

Fig. 2 T-S diagram of non-concentrating solar Stirling engine combined with the electrical analogy of various heat transfer processes

conversion efficiency. As shown in Fig. 2, the solar non-concentrating Stirling engines consist of a thermodynamic Stirling cycle with different heat transfer processes like conduction, convection, radiation, or combination of these processes. Ideally, processes 1–2 are the isothermal process, in which the working fluid at temperature T c rejects heat Q0 to the atmosphere/ambient at constant temperature T a. Q o = [h C2 (Tc − Ta )]τ12 = mRTc ln rv + mC V (1 − )(Th − Tc )

(1)

where h C2 is the convective heat transfer coefficient between bottom plate (sink) and working fluid. Since the temperature difference between the working fluid at cold end and ambient (process 1–2 in Fig. 2), Tc − Ta is lesser (less than 20 K) in LTDSE, the contribution of convective heat transfer alone is considered. Then, the working fluid passes through the regenerator while absorbing the heat stored in the regenerator QR and this process (2–3) is isochoric in nature. Further, the working fluid gets in contact with higher temperature absorber plate and expands and transfers absorbed heat Qi to the regenerator (3–4). The amount of heat released from the absorber (source) at temperature, T p , is isothermally absorbed by the working fluid (3-4 in Fig. 2) via simultaneous processes of convective and radiative heat transfer.      Q i = h C1 T p − Th + h R1 T p4 − Th4 τ34 = mRTh ln rv + mC V (1 − )(Th − Tc )

(2)

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In process 4–1, only a part of the heat transferred to the regenerator QR is stored and transferred to the working fluid, while the rest is passed onto the working fluid at process 2–3 due to regenerative imperfections. From Eq. (1) and (2), the time for isothermal heat addition process, τ34 , and the time for isothermal heat rejection process,τ12 , can be depicted below, τ34 =

m RTh ln rv +mC V (1−)(Th −Tc) h C1 (T p −Th )+h R1 (T p4 −Th4 )

(3)

τ12 =

m RTc ln rv +mC V (1−)(Th −Tc ) [h C2 (Tc −Ta )]

(4)

Thus, the total cyclic time can be expressed as, τ = τ12 + τ23 + τ34 + τ41 = τ12 + τ34 + 2τ R

(5)

where the τ R is the time for the regeneration process and must be evaluated as a part of internal irreversibility. Additionally, the direct heat leak from the absorber to atmosphere through the engine walls and insulation is generally termed as conductive thermal bridge losses,Q C is also considered [4].   Q C = k0 T p − Ta τ

(6)

This heat loss is assumed to be directly proportional to the temperature difference between absorber and ambient, total cyclic time τ , and a proportionality constant termed as conductive thermal bridge loss coefficient k0 . By applying the finite time thermodynamic approach to the solar Stirling engine by considering heat transferred through both convection and radiation, the maximum power output of the Stirling engine and maximum power thermal efficiency for a cyclic period are given by [4], Pmax =

(1−θ )



1+δ(1−θ) h C1 (Ta −Th )+h R1 T p4 −Th4

(

ηmpt =

)

+h

θ +δ (Th −θ ) 2τ R + m Rlnr v C2 (θ Th −Ta )

Pmax Th +δ(Th −θ )+k0 (T p −Ta )



(7) (8)

whereδ = C V (1 − )/Rlnrv , θ ,θ is the temperature ratio, τ R is the time for the regeneration process, h C1,2 is the Convective heat transfer coefficient between working fluid and heat source, and working fluid and heat sink, respectively. In order to maximize the power output, derivative of Eq. (4) is taken with respect to Th and equated to zero, ∂ P/∂ Th = 0, which gives the optimal working fluid temperature, Th as, Th = (Ta + γ T p )/(θ + γ )

(9)

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289

where,  γ =

θ 2 + δθ (1 − θ ) 1 + δ(1 − θ)

0.5

In order to incorporate the heat transfer losses due to top losses Qtop-loss , an electrical resistance analogy of various heat transfer processes is represented schematically in Fig. 2. To estimate the top loss coefficient Ut , a fairly accurate (less than ±3% error) non-iterative solution proposed by Mullick et al. [9] is considered, ⎡ ⎢ Ut = ⎢ ⎣

(( (

12.75

)

)

1

0.264 T p −Tgi cos β 0.46 0.21 Lg T p +Tgi

)

+

+

(

2 σ T p2 +Tgi

1 1  p + g −1

1

hw +

)(T p +Tgi )

(

4 −T 4 σ g Tgo s (Tgo −Ta )

)

+

tg kg

⎤−1 ⎥ ⎥ ⎦

(10)

where β is the collector tilt angle (°), T p is the absorber plate temperature (K), Ta is the ambient temperature (K),ε p is the emissivity of the absorber plate, εg is the emissivity of the glass cover,αg is the absorptance of the glass cover,k g is the thermal conductivity of the glass cover material (W/mK), tg is the thickness of the glass cover (mm),L g is the air gap space between the absorber plate and glass cover (mm), h w is the wind heat transfer coefficient (W/m2 K), It is the solar irradiation (W/m2 ) and σ is the Stephan–Boltzmann constant (W/m2 K4 ). The instantaneous thermal efficiency of the single-glazed collector is derived from energy balance of absorber plate and expressed as, η0 =

Qu A p It

= (τ0 α0 ) −

Ut (T p −Ta ) It

(11)

The maximum thermal efficiency of the solar Stirling engine is the product of instantaneous thermal efficiency of the glazing and thermal efficiency at maximum power output,  ηm = η0 ηmpt = (τ0 α0 ) −

Ut (T p −Ta ) It



Pmax Th +δ(Th −θ )+k0 (T p −Ta )

 (12)

By using this newly formulated relations (Eq. 9 and 12), the working fluid temperature in solar Stirling engine is predicted and validated with experimental data from elsewhere.

3 Validation Majority of previous investigations on solar Stirling engine dealt with hightemperature regime and only a few authors [7, 10] reported experimental data for

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100

1200

80

1000

60

800

Absorber Plate Working fluid ( Experimental [7] ) Working fluid (Predicted) Irradiaon

40 20 0 7:00

8:00

9:00

10:00

11:00 12:00 Day Time

13:00

14:00

600 400 15:00

16:00

200

Fig. 3 Variation of irradiation and absorber plate temperature with day time

Thermal efficiency (%)

25

ηIdeal Srling ηCurzon-Ahlborn ηT-Experimental ηm-predicted

20 15 10 5 0

L.Grosu et.al [8]

N.Boutammachte et.al [7] A.R Tavakolpour et.al[11]

Fig. 4 Comparison of developed model with various LTDSE designs

N.Martaj et.al [10]

Irradiaon (W/m2)

Temperature ( )

LTDSE. The experimental data provided by N. Boutammachte et al. [7] is considered for validation and performance prediction. The performance of a non-concentrating solar collector coupled with a Stirling engine connected to a water pump (SSM-IV) for rural areas of Meknes/Morocco was evaluated and provided the data of absorber plate temperature and working fluid temperature which was measured in real-time conditions. (see Fig. 3). The data for variation in temperature of the absorber plate and irradiation with respect to day time is taken from the literature [7] and used to solve for Th . The present thermodynamic model could predict the working fluid temperature with an error lesser than ±10% on real-time experimental data. The absorptivity of the glass cover is considered to predict the top loss coefficient of the solar Stirling engine. It has to be noted that the same authors have reported that the existing Schmidt model shows a deviation of five times in ideal and real cycle. By using the absorber plate temperature data [7], the variations in the glass cover (inside and outside) and working fluid temperature are predicted using Eq. (9) and

Effect of Top Losses and Imperfect Regeneration on Power Output …

291

Fig. 5 Variation of different temperatures in solar LTDSE with solar irradiation

shown in Fig. 5. An almost linear increase in the working fluid temperature is noted with the increase in solar radiation. Further, the predicted and experimental working fluid temperatures are reasonably close to each other (coefficient of determination, R2 = 0.9817) indicating this approach can be adopted for calculating the working fluid temperature. From Fig. 3 and 4, it can be observed that the FTT thermodynamic model with thermal losses due to heat transfer is effective in predicting the performance of an LTDSE and can be used for the further parametric study.

4 Results and Discussion In order to evaluate the effect of top loss coefficient and absorptivity of the glass cover on performance of solar LTDSE, all the other parameters are kept constant as θ = 0.95, h C1 = h C2 = 200W/K, h R1 = 4 × 10−8 wK−4 , m = 9 × 10−3 kg, ε = 0.5, Ta = 298K , k0 = 2.5 W/K, αg = 0.08, τ0 α0 = 0.8, C V = 718 J/kgK, R = 218 J/kgK [4]. The results obtained are as follows: The absorptivity of the glass cover is considered in order to predict the top loss coefficient of the solar Stirling engine (See Fig. 6). Individual temperatures of the glass cover are evaluated and the same is further substituted in Eq. (1) to find top loss coefficient. It is found that the deviation in top loss coefficient and optical efficiency with and without considering the absorptivity of the glass cover about 0.3% in the case of LTDSE. From Fig. 4, it is also observed that there is a linear dependency among top loss coefficient and solar irradiation, which is well known. However, due

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0.52

5.3

0.5 5.1

0.48

4.9

0.46 0.44

4.7

Thermal Efficiency

Top loss coefficient (W/m2K)

5.5

0.42

4.5 350

550

750 Solar Radiaon, It (W/m2)

Top loss Coefficient with Absorptance Opcal efficiency with Absorptance

950

0.4

Top loss Coefficient without Absorptance Opcal efficiency without Absorptance

Fig. 6 Variation of irradiation and absorber plate temperature with day time

6

7

5

6 5

4

4

3

3

2 Pmax Thermal Efficiency Srling Cycle Maximum thermal efficiency of solar Srling engine

1 0 7:00

8:00

9:00

10:00 11:00 12:00 13:00 14:00 15:00 16:00 Day Time

Efficiency (%)

Maximum power output (W)

to the direct influence of the absorber plate temperature on Eq. (6), there exists a significant variation (7%) in optical efficiency. When it comes to the maximum thermal efficiency of the solar Stirling engines, the effect of optical efficiency becomes further crucial (see Fig. 7). It is found that there exists a maximum deviation of 3% approximately between thermal efficiency with and without considering top losses. Experimentally, the efficiency of solar LTDSE considered for this investigation is around 1.3%. The maximum thermal efficiency by modified FTT analysis with top losses is around 2–3.5% time and without considering top losses is around 5–7% at peak time. This observation further supports the fact that

2

1 0

Fig. 7 Variation of maximum power output and maximum thermal efficiency with day time

Effect of Top Losses and Imperfect Regeneration on Power Output … 2

25

1.8

Power output (W)

20

1.6 15 1.4 Maximum power output

10

Thermal efficiency 5

Efficiency (%)

Fig. 8 Variation of power output and thermal efficiency with the regeneration process time

293

0

0.0005 0.001 0.0015 Time for regeneraon process τr (sec)

1.2 1 0.002

by incorporating top losses with FTT analysis provides a much realistic prediction of solar LTDSE performance. The effect of time for regeneration process on thermal efficiency and power output of solar LTDSE is illustrated in Fig. 8. The thermal efficiency and power output of the engine increase and reach a peak regenerative time of 0.0005 s and 0.0003 and thereafter decrease, respectively. The decrease in thermal efficiency is negligible whereas the decrease in power output is about 8 W when there is an increase in regenerative time 0.002 s. This is because longer regenerative time duration increases the thermal efficiency by adding more heat during the isochoric process [12]. This can only be achieved by reducing the engine speed, which decreases the power output. Thus, there needs to be an optimal regenerative time with respect to the speed of a practical solar LTDSE. This can be achieved by selecting proper regenerative materials and working fluids for solar LTDSE.

5 Conclusions The influence of top loss coefficient and absorptivity of the glass cover on the performance of solar LTDSE is investigated and found that • the absorptivity of the glass cover has a negligible effect on the thermal efficiency of the solar LTDSE. • the thermal efficiency of solar LTSDE is deviated by 3% if top losses are not considered. • the power output can be increased with negligible drop in thermal efficiency by operating the solar LTDSE at higher speed, i.e., by reducing the regenerative time. • by incorporating top losses to FTT approach, it is now possible to fairly predict the performance parameters of the solar LTDSE.

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Therefore, the modified FTT approach adopted here can be used to design the Solar LTDSE effectively at the preliminary stage of engine design.

References 1. Hachem, H., Gheith, R., Aloui, F., Ben, Nasrallah S.: Technological challenges and optimization efforts of the stirling machine: a review. Energy Convers. Manag. 171, 1365–1387 (2018) 2. Ahmadi, M.H., Sayyaadi, H., Dehghani, S., Hosseinzade, H.: Designing a solar-powered stirling heat engine based on multiple criteria: Maximized thermal efficiency and power. Energy Convers. Manag. 75, 282–291 (2013) 3. Tlili, I.: Finite time thermodynamic evaluation of endoreversible Stirling heat engine at maximum power conditions. Renew. Sustain. Energy Rev. 16, 2234–2241 (2012) 4. Yaqi, L., Yaling, H., Weiwei, W.: Optimization of solar-powered stirling heat engine with finite-time thermodynamics. Renew Energy 36, 421–427 (2011) 5. Wang, K., Sanders, S.R., Dubey, S., Choo, F.H., Duan, F.: Stirling cycle engines for recovering low and moderate temperature heat: a review. Renew. Sustain. Energy Rev. 62, 89–108 (2016) 6. Ahmadi, M.H., Ahmadi, M.-A., Pourfayaz, F.: Thermal models for analysis of the performance of stirling engine: A review. Renew. Sustain. Energy Rev. 68, 168–184 (2017) 7. Boutammachte, N., Knorr, J.: Field-test of a solar low delta-T stirling engine. Sol. Energy 86, 1849–1856 (2012) 8. Li, R., Grosu, L., Queiros-Condé, D.: Losses effect on the performance of a gamma type stirling engine. Energy Convers. Manag. 114, 28–37 (2016) 9. Akhtar, N., Mullick, S.C.: International journal of heat and mass transfer effect of absorption of solar radiation in glass-cover on heat transfer coefficients in upward heat flow in single and double glazed flat-plate collectors. Int. J. Heat Mass Transf. 55, 125–132 (2012) 10. Martaj N, Grosu L, Rochelle P. Thermodynamic study of a low temperature difference stirling engine at steady state operation. Int J Thermodynamics. 10:165–76 (2007) 11. Tavakolpour AR, Zomorodian A, Akbar Golneshan A.Simulation, construction and testing of a two-cylinder solar Stirling engine powered by a flat-plate solar collector without regenerator. Renewable Energy. 33:77–87(2008) 12. Dai DD, Yuan F, Long R, Liu ZC, Liu W.: Imperfect regeneration analysis of Stirling engine caused by temperature differences in regenerator. Energy Convers Manag 158, 60–69 (2018)

Investigations on Recovery of Apparent Viscosity of Crude Oil After Magnetic Fluid Conditioning A. D. Kulkarni

and K. S. Wani

1 Introduction Transportation of viscous crude oil through subsea pipelines is a critical energyintensive job in the oil industry. When the temperature of crude oil falls below the wax appearance temperature (WAT), paraffin wax precipitates which increases the apparent viscosity of crude oil. This requires extra pumping power thereby leading to an increase in the number of pumping stations. The overall result is decreased production rate, equipment breakdown and production shutdown [1]. Heating of pipelines is the most commonly used treatment method. It is an efficient method but the energy requirements make it highly expensive. On the other hand, magnetic fluid conditioning method claims to be economical. It is based on the fact that when crude is treated with magnetic field, the apparent viscosity decreases thereby easing the transportation of oil. The mechanism of reduction in viscosity is based on two different theories, viz. the aggregation theory and the disaggregation theory. The aggregation theory is based on Einstein’s suspension theory as interpreted by Tao [2] which accounts for the localization of paraffin particles when subjected to magnetic field thereby reducing the viscosity. On the other hand, the disintegration theory states that magnetic treatment results in the disaggregation of paraffin particles in crude oil. Under normal circumstances, as temperature approaches WAT, the paraffin particles begin to agglomerate. Energy induced by the magnetic field disintegrates these particles. They acquire weak dipole moments and get aligned in the direction of the magnetic field. These A. D. Kulkarni (B) Department of Petroleum and Petrochemical Engineering, Maharashtra Institute of Technology, Pune, India e-mail: [email protected] K. S. Wani Department of Chemical Engineering, SSBT College of Engineering and Technology, Jalgaon, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_29

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dipoles generate repulsive forces and lead to disturbance in the crystal agglomeration process. The rheological properties change and the viscosity of the oil decreases. Experimental evidence in the literature has been found in the favour of the disintegration theory. Loskutova [3] considered crude oil as a dispersion of paraffins, asphaltenes and resins in lower hydrocarbons. Application of magnetic field leads to the destruction of this colloidal structure and resulted in a decrease in viscosity. Using scanning electron microscopy, Rocha [4] had shown that the embryos of paraffins which just began to grow in size near the WAT underwent disintegration under the action of magnetic field. Experiments by Evdokimov [5] showed that oils when subjected to magnetic field underwent ultraviolet (UV) spectrum extinction. This suggested a decrease in the size of suspended particles as a result of transient break up of hydrogen bonds in the hydrocarbons. Jiang [6] performed tera-hertz time-domain spectroscopy to study the aggregation characteristics of crude oil constituents under the action of magnetic field. The decrease in the extinction coefficient suggested the disaggregation of suspended colloidal particles. The interlocked paraffin particles underwent disintegration when subjected to magnetic field and regained the original state when the field subsided. Moreover, the reduction in viscosity is not permanent. The apparent viscosity tries to regain its original value. But the time for recovery as well as the degree of recovery has been found to vary. Rocha [4] and Zhang [7] had obtained a complete recovery in 8 h irrespective of the composition of crude oil, time of exposure and the type and intensity of magnetic field. Tung [8] had achieved a viscosity recovery of around 95% in 14 h. On the other hand, Loskutova [3] recovered the initial value of viscosity in 24 h which further exceeded by 20%. In another experiment, Loskutova [9] observed that the recovery of viscosity started after 2 h and took more than 24 h to reach the original value. The time required for the viscosity to regain its original value is of prime importance. If the viscosity regain is fast then the magnetic treatment will become less effective. This will result in the deposition of paraffins leading to an increase in the pressure drop. Therefore, its knowledge can help in the design of pipelines along with the number of pumping stations required to affect the transportation of crude oil. The reduction in the heating requirements and number of pumping stations can improve the energy efficiency and overall economics. The objective of this paper was to investigate the recovery pattern of viscosity after magnetic treatment in terms of % regain of viscosity and total time of recovery. Experiments were performed at the most optimum conditions suggested in the literature by varying the magnetic field for crude oils with different wax content. The data thus obtained was used for the explanation of possible mechanism for the trend.

2 Materials and Methods Crude oils (C1 , C2 and C3 ) obtained from western parts of India were characterized for density at 15 °C, 0 API, wax content (%), asphaltene content (%), wax appearance

Investigations on Recovery of Apparent Viscosity …

297

a

b

Fig. 1 Experimental set-up (a) Electromagnet (b) Brookfield viscometer with temperature control

temperature (WAT) (°C) and viscosity at WAT (cP) as per the standard procedures. Magnetic field was generated using electromagnet set-up (EMU-50 V obtained from M/s Scientific Equipment and Services, Roorkee, India) consisting of a U-shaped soft iron yoke with two pole pieces having 50 mm diameter each (Fig. 1a). Accurately measured crude oil samples (C1 , C2 and C3 ) of volume 50 ml were held stationary between the pole pieces of the electromagnet. These were subjected to electromagnetic fields of strength 1000, 3000, 6000 and 9000 gauss for a period of 1 min at the WAT which was the most effective time as per the literature [4]. The viscosities were measured using Brookfield DVII+viscometer with temperature control mechanism (Fig. 1b) at time t = 0, 1, 2, 3, 4, 5 and 6 h. The final readings were taken at 24 h.

3 Results and Discussion Table 1 shows the physico-chemical properties of crude oil samples.

Table 1 Properties of crude oils used in experimentation C1 Density (g/cc) at 15 °C

0.8754

C2 0.877

C3 0.887

Method Hydrometer

0 API

30.1

29.8

28

Formula

Wax content (%)

25.1

35.5

45.7

UOP 46–64

Asphaltene content (%)

12.2

5

7.2

WAT

(0 C)

Viscosity at WAT (cP)

IP 143

30

43

50

Viscometry

69.3

55.4

82.1

Brookfield DVII+Pro viscometer

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Figures 2, 3 and 4 show the trend for regaining of viscosity after magnetic conditioning of samples C1 , C2 and C3, respectively. The viscosity recovery patterns are found to be different for different magnetic fields and crude oils. Preliminary observations show that more the reduction in viscosity, more is the time taken to reach the original value. A thorough investigation reveals that it may not be the case always. It varies for different oils and magnetic fields. Sample C1 having lower wax content has greater viscosity reduction for higher magnetic fields and also takes more time for recovery. Accordingly one may expect that C2 and C3 having higher wax content will give greater viscosity reduction for higher magnetic fields. But it is not observed so. According to Tao [2], reduction in viscosity under the influence of magnetic field is dependent on the paraffin content, intensity of magnetic field and the time of exposure. If the time of exposure is increased, then the local aggregation of the paraffin particles increases thereby increasing the viscosity. Conversely, when the paraffin content and magnetic field increase for a given time of exposure, 75

Viscosity (cP)

70 65 60 55

50

0

2

Crude oil

4

6

8

10 12 14 16 Time elapsed (hours) 3000 gauss

1000 gauss

18

20

22

24

26

9000 gauss

6000 gauss

Fig. 2 Variation of viscosity with time elapsed for crude oil 1 (C1 ) when subjected to magnetic fields for 1 min 60

Viscosity (cP)

50 40

30 20

0

Crude oil

2

4

6

1000 gauss

8

10 12 14 16 Time elapsed (hours) 3000 gauss

18

20

6000 gauss

22

24

26

9000 gauss

Fig. 3 Variation of viscosity with time elapsed for crude oil 2 (C2 ) when subjected to magnetic fields for 1 min

Investigations on Recovery of Apparent Viscosity …

299

Viscosity (cP)

80 70 60 50 40 30

0

Crude oil

2

4

6

1000 gauss

8

10 12 14 16 Time elapsed (hours) 3000 gauss

18

20

22

6000 gauss

24

26

9000 gauss

Fig. 4 Variation of viscosity with time elapsed for crude oil 3 (C3 ) when subjected to magnetic fields for 1 min

the viscosity reduction would decrease. This is exactly observed for samples C2 and C3 . Moreover, paraffins are not the only entities responsible for viscosity reduction. Goncalves [10] had performed NMR spectroscopy on six different crude oils to find out the ratio of aromatic to aliphatic molecules and water content alongwith EPR spectroscopy to detect paramagnetic ions. It was observed that higher was the aromatic-to-aliphatic ratio better was the viscosity reduction. In the present experimentation, the asphaltene-to-paraffin ratio for C2 and C3 (0.14 and 0.16, respectively) is found to be less than that of C1 (0.48) and hence exhibit less viscosity reduction. Although the viscosity reduction is small for C2 and C3 as compared to C1 for higher magnetic fields, the rate of recovery is also less indicating that the effect of magnetic field remains for a longer time. This is in line with Rocha [5] who proposed that higher is the paraffin content more is the interaction with magnetic field.

3.1 Effect of Magnetic Field on % Regain of Viscosity The % regain of viscosity after a certain time interval is important from the viewpoint of transportation. More the time it takes to regain the viscosity, more efficient is the transportation with less pumping power. The % regain has been calculated as follows: % regain =

Increase in viscosity after maximum reduction at a given time interval Total viscosity reduction

Figures 5, 6 and 7 show the % regain in viscosity over a time period of 24 h. It is observed that the % regain varies for different magnetic fields for different crude oils. The viscosity recovery for C1 varies from 68% to 95% for different magnetic fields. Similarly, in case of C3 , it varies from 61% to 74%. The complete recovery of viscosity is not observed in case of C1 and C3 in a period of 24 h. Loskutova [11] had obtained complete recovery of viscosity for two of their samples. The other two

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samples could not attain their original viscosity even after 48 h. In case of C2 , the viscosity after 24 h exceeded the original by around 5%. The result obtained is in line with Goncalves [12] who has attributed the final viscosity increase to various factors like intermolecular forces and packing between the molecules.

3.2 Effect of Wax Content C1 , C2 and C3 have increasing order of wax content. It is observed that for the oils with less wax content, lower magnetic field leads to faster recovery whereas for higher wax content, higher magnetic field gives better recovery. One of the explanations for this behaviour has been by Loskutova [13]. Crude oils are considered as dispersions of asphaltenes, resins and paraffins in lower molecular weight saturates. These dispersions are thermodynamically unstable. The degree of dispersion changes with changes in external factors such as pressure temperature, chemicals and physical fields like magnetic field. When magnetic field is applied, paraffins undergo changes in structural pattern which in turn changes the viscosity. After the excitation subsides, the relaxation process begins and the viscosity regains its original value based on the phenomenon of thixotropy. Thus, more paraffinic crude will require more relaxation time to come to its original state.

3.3 Relation Between Initial Reduction and % Regain of Viscosity Figures 8, 9 and 10 show that less is the initial reduction more is the regain of viscosity. Magnetic field provides certain energy for change in the orientation of the 94.5

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60 40 19.9 20 0

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paraffin molecules. It is the inherent property of any system to regain its original state of rest or equilibrium. The more the equilibrium is disturbed, the faster the system tends to regain it. Hence, all the above cases show that the viscosity reduces with magnetic field and it tries to attain the original value after some time. But the time required for the recovery differs from case to case. The reason for this can be explained as follows. The reduction in viscosity due to magnetic field is due to disaggregation of the paraffin agglomerates in crude oil and subsequent alignment in the direction of the field. It is the inherent property of any system to regain its original state of rest or equilibrium. When the magnetic effect subsides, the paraffin particles lose their alignment and begin to form aggregates. The viscosity begins to increase. But this process has no driving force and is uncontrolled. The paraffin molecules are free to align themselves. The process is from orderliness to randomness. As time progresses, these molecules tend to regain their original state, i.e. random state as quickly as possible. Hence, there is no definite pattern or time interval for viscosity increase. Moreover, the composition of oil especially the asphaltene, paraffin and resin composition will play an important role. The interspecies forces as suggested by

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Original

t = 0 hours

t = 2 hours

Randomly arranged paraffins

Aligned aer magnezaon

Losing alignment

t = n hours

Regaining original random nature

Fig. 11 Schematic of regain of viscosity after initial reduction due to magnetic field

Shiryaeva [14] will affect the process. Hence, the time taken by different samples is different and it may or may not regain the original viscosity. Also, during the recovery process, these molecules may come too close to each other which will further reduce the viscosity during the relaxation process. This is evident from samples C1 and C3 as seen in Figs. 2 and 4. The viscosity increases and decreases in the first 3–4 h and finally continues to increase. The schematic of the process is shown in Fig. 11.

4 Conclusions The pattern for recovery of apparent viscosity of crude oil subjected to magnetic field has been studied. This regain of viscosity has been observed to take place between 8 h to more than 24 h. More is the initial reduction in viscosity, slower is the regain. The regain of viscosity depends on factors like the initial viscosity, the strength of magnetic field and the wax content of crude oil. The viscosity regained can exceed the original value. A mechanism for the same has also been discussed. Similarly, crude oils with higher wax content require more time for relaxation owing to increased interaction between the paraffins and magnetic field. The time for regain of viscosity can also be used to decide the pumping power, number of pumping stations and the heating requirements for transportation. The present findings can thus be used as a basis for performing detailed energy analysis of a crude oil trunk line. The study can be extended further by varying other parameters like the asphaltene content and the time of exposure to magnetic field.

References 1. Frenier, W.W., Ziauddin, M., Venkatesan, R.: Organic deposits in oil and gas production. 1st edn, Society of Petroleum Engineers (2010) 2. Tao, R., Xu, X.: Reducing the viscosity of crude oil by pulsed electric or magnetic field. Energy & Fuels 20, 2046–2051 (2006) 3. Loskutova, Y.V., Yudina, N.V.: Effect of constant magnetic field on the rheological properties of high-paraffinicity oils. Colloid J. 65, 469–473 (2003)

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4. Rocha, N., González, C., Marques, L., Vaitsman, D.S.: A preliminary study on themagnetic treatment of fluids. Pet. Sci. Technol. 18, 33–50 (2000) 5. Evdokimov, I.N., Kornishin, K.A.: Apparent disaggregation of colloids in a magnetically treated crude oil. Energy & Fuels 23, 4016–4020 (2009) 6. Jiang, C., Zhao, K., Zhao, L.J., Jin, W.J., Yang, Y.P., Chen, S.H.: Probing disaggregation of crude oil in a magnetic field with terahertz time-domain spectroscopy. Energy & Fuels 28, 483–487 (2014) 7. Zhang, W., Zhang, G., Dong, H.: The effect of magnetic radiation on pipeline transportation of crude oil. In: Proceedings of International Conference in Digital Manufacturing and Automation-ICDMA 2010. 2, pp. 676–678. IEEE, Changcha, China (2010) 8. Tung, N.P., Vinh, N.Q., Phong, N.T.P., Long, B.Q.K., Hung, P.V.: Perspective for using Nd-FeB magnets as a tool for the improvement of the production and transportation of Vietnamese crude oil with high paraffin content. Phys. B 327, 443–447 (2003) 9. Loskutova, Y.V., Yudina, N.V.: Rheological behavior of oils in a magnetic field. J. Eng. Phys. Thermophys. 79, 105–113 (2006) 10. Gonçalves, J.L., Bombard, A.J.F., Soares, D.W., Carvalho, R.D.M., Silva, M.R., Alcantara, G.B., Pelegrini, F., Vieira, E.D., Pirota, K.R., Izabel, M., Bueno, S., Maria, G., Lucas, S., Rocha, N.O.: Study of the factors responsible for the rheology change of a Brazilian crude oil under magnetic fields. Energy & Fuels 25, 3537–3543 (2011) 11. Loskutova, Y.V., Yudina, N.V., Pisareva, S.I.: Effect of magnetic field on the paramagnetic, antioxidant, and viscosity characteristics of some crude oils. Pet. Chem. 48, 51–55 (2008) 12. Gonçalves, J.L., Bombard, A.J.F., Soares, D.A.W., Alcantara, G.B.: Reduction of paraffin precipitation and viscosity of Brazilian crude oil exposed to magnetic fields. Energy & Fuels 24, 3144–3149 (2010) 13. Loskutova, Y.V., Prozorova, I.V., Yudina, N.V., Rikkonen, S.V.: Change in the rheological properties of high-paraffin petroleums under the action of vibrojet magnetic activation. J. Eng. Phys. Thermophys. 77, 1034–1039 (2004) 14. Shiryaeva, R.N., Kovaleva, L.A., Gimaev, R.N.: Improving the rheological properties of highviscosity crude oil. Modifying additive and high-frequency electromagnetic field. Chem. Technol. Fuels Oils 41, 36–38 (2005)

Investigation on Different Types of Electric Storage Batteries Used in Off-grid Solar Power Plants and Procedures for Their Performance Improvement Anindita Roy, Rajarshi Sen, and Rupesh Shete

1 Introduction Electric storage batteries are the central part of an off-grid solar photovoltaic plant. On-grid solar/wind farms and rooftop installations also need battery storage for ensuring maximum utilization of renewable energy, grid voltage/frequency stabilization and peak load shifting. There are about 14,000 micro-/mini-grids (DC and AC) in India, with many new off-grid and grid hybrid installations coming up [1]. All these plants have solar PV capacity generally in range of 250 W to 100 kW with battery for providing backup for couple of hours to over twelve hours. However, failure of batteries within four years of installation leading to degradation of plant output has troubled customers [1]. From extensive survey of off-grid power plants and revival of some of them, it was found that most of the degradation had been due to lack of proper operating information on batteries [1]. While selection of battery type and sizing were a part of the cause, the problem in understanding battery operation and maintenance in solar PV plants was the main factor leading to capacity degradation. Electrical storage applications in off-grid solar power plants usually employ lead acid batteries. These batteries are typically designed for daily deep cycling (discharge and recharge) applications, and as the solar radiation is available for a limited period for charging the battery, these are prone to periods of low or partial charge followed by very deep discharge. Degradation of lead acid battery capacity on successive cycling is a common phenomenon observed. Positive grid corrosion is one of the main reasons for capacity degradation in stationary batteries which are charged by float current [2]. Other factors contributing to capacity degradation are i) positive plate corrosion, ii) positive active mass (PAM) degradation and contact loss with grid, and iii) sulphation. During normal cycling, corrosion of positive plate naturally A. Roy (B) · R. Sen · R. Shete Pimpri Chinchwad College of Engineering, Pune 411044, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_30

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occurs as metallic lead plate is thermodynamically unstable [2]. However, a layer of corrosion also protects the grid from further fast corrosion. Nevertheless, excessive corrosion reduces conductivity of grid, thus reducing overall capacity. High voltage, acid concentration and high temperature are three major factors instrumental in accelerating corrosion [3]. Adding element like antimony, selenium or calcium with lead to form a compact metallographic structure helps to reduce rate of grid corrosion. Equation (1) shows the reaction occurring during charge and discharge in a lead acid battery: PbO2 + Pb + 2H2 SO4 = PbSO4 + PbSO4 + 2H2 O

(1)

During discharge, lead dioxide (PbO2 ) gets converted to lead sulphate (PbSO4 ) which has larger volume relative to PbO2 . This results in morphological shape change of the PAM [2]. As a result, there is a reduced contact between the PAM and the grid, causing loss of contact. Higher depth of discharge [3] and repetitive cycling are responsible for softening of PAM which may get detached. Shedding (detachment of active mass) and slugging may cause short circuit. Lead antimony grids are less affected by it than antimony free grids [4]. Use of high density pastes and additives can help in reducing active material softening [4]. Further, during discharge reaction, dilute sulphuric acid (electrolyte) reacts with PbO2 and Pb to form soft lead sulphate crystals which are deposited on the plates. If the battery remains in a partially discharged condition, some of the soft lead sulphate crystals do not get converted to lead dioxide/lead and these aggregate into larger and hence hard crystals over time. The plates thus loose porosity and are difficult to convert back to soft active materials, viz., lead and lead dioxide during every day recharge by solar. Formation of irreversible hard sulphate resulting in loss of capacity in active mass is referred to as sulphation. It is mainly caused by two reasons (i) batteries are not fully charged for longer duration of time or left idle for quite long and (ii) electrolyte stratification. Stratification of electrolyte causes charge to be unequally distributed, resulting in undercharging of certain portion of the cell, thereby leading to sulphation [5]. Battery thus loses its capacity partially. Further, this hard sulphate acts as resistance to energy output of solar power plants. Timely equalizing or full charging with proper mixing of electrolyte by using pump is a possible remedy to reduce sulphation. This study reports the results of capacity degradation on cycling under laboratory conditions for different types of solar batteries. Tests were designed and performed by simulating solar charging conditions in the laboratory. A trend of capacity degradation was observed across different types of solar batteries. The remedial action is suggested, applied and found to revive the battery to its full capacity. Further, it was possible to estimate the frequency of servicing the solar power plant batteries so as to get a higher service lifetime.

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2 Methodology Figure 1 illustrates the schematic arrangement of a DC-coupled photovoltaic battery system used for off-grid electrification. In such a configuration, the photovoltaic array is coupled to the DC bus though a DC–DC charge controller. The solar photovoltaic modules are connected in series or parallel, depending on the total voltage and current required to be supplied. The power from the solar array is collected in one or more junction boxes and thereafter fed into one or multiple solar charge controllers. The electric storage batteries are charged by DC current and voltage from these solar charge controllers. The charged batteries in turn, supply pure AC power on demand, through an inverter to the AC load. The objective of the present study is to study the cycling performance of the storage battery and its charge/discharge controls that may lead to the performance, efficiency and life enhancement of the solar power plant. The limited 7.5–8 h of solar charging during a day is insufficient to ensure full charge. In order to study the performance of batteries used in off-grid solar power plants, following methodology was adopted: 1. Selection types of solar application battery 2. Determining capacity of the battery through capacity tests 3. Simulated solar cycling tests to study degradation on cycling. Each of these steps is described in the following sections.

2.1 Battery Selection Lead acid batteries used in solar power plants can be categorized broadly into two major types, viz., flooded lead acid and valve regulated lead acid (VRLA) or sealed batteries. Figure 2 shows the classification of various types of batteries used in solar

Photovoltaic Array AC Load

Inverter Solar Charge Controller

AC Bus

Battery

DC Bus

Fig. 1 Schematic and power flow in a DC-coupled off-grid photovoltaic-battery systems

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Flooded

Flat Plate

Tubular

Thin tubular Plate

Thick tubular Plate

VRLA

AGM lead calcium

Gel electrolyte

Tubular Plate

Lead Carbon

Flat Plate

Fig. 2 Classification of solar lead acid batteries

and wind solar hybrid power plants. While flooded batteries contain dilute sulphuric acid in liquid form as electrolyte which needs to be replenished with distilled water from time to time in order to make up for the water loss by electrolysis and evaporation, VRLA batteries have their electrolyte in the form of a semi-solid gel requiring no maintenance or topping up with distilled water. Flooded batteries are available in two configurations of electrodes, viz., tubular plates and flat plate. Due to increased positive plate surface area, tubular batteries have 20% more electrical capacity than flat plate batteries of comparable size and weight. Further, tubular batteries also provide up to a 30% longer service life than flat plate batteries due to reduced positive plate shedding [6]. The major advantage of flooded battery is their ruggedness and ability to survive abusive conditions as in solar power plants. Valve regulated lead acid (VRLA) batteries are available in three types, viz., absorptive glass mat (AGM), gel electrolyte and lead carbon foam batteries. VRLAAGM batteries have both positive and negative plates of flat plate construction, lead calcium grids and an absorptive glass mat separator, designed for holding electrolyte in its pores and allowing recombination of gases, thus ensuring no water loss during charge. They are primarily used in UPS and telecom applications. However, a large number of them are also deployed in solar PV applications due to the advantage of no regular requirement of topping up with water. VRLA battery with gel electrolyte operates on a gas recombination technology similar to AGM VRLA battery. The difference being that tubular positive plates (different than flooded tubular) are used instead of flat pasted positive plates and the electrolyte absorbed in silica gel is used. There is a charge voltage limit as in AGM VRLA but tubular positive plates provide a high cycle life as in flooded tubular batteries, that is, 1200 cycles or more at 80% DOD, 2500 cycles at 50% and 5000 cycles at 20% DOD. The lead carbon foam battery is similar to AGM VRLA but the negative plates have a carbon foam grid which is designed to aid in very efficient charge and discharge due to the relatively better current conductivity of carbon as compared to the lead antimony or lead calcium used in other batteries.

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It is necessary to know the capacity of the battery in Ah as specified by the manufacturer before undertaking detailed cycling tests. The procedure and results of capacity tests on these batteries are described in the following sections.

2.2 Capacity Test A capacity test is required to determine the battery capacity (Ah) at various discharge currents. Depending on the connected load, a solar power plant may be required to supply current in a certain range. It is necessary to predict the battery cut-off voltage during a discharge at a specific rate. This helps in selecting the inverter cut-off. Wrong setting of inverter cut-off voltage without taking into account the discharge current may lead to either over discharge or low capacity utilization of the battery. This can be done by discharging the battery at various currents and noting down its end voltage and capacity by calculating Ah discharged. For instance, if the discharge rate of a battery is 0.1 C, then for a 100 Ah battery, the discharge current is 0.1 × 100 = 10 A. Similarly, for discharge rate 0.15 C, the discharge current is 0.15 × 100 = 15 A. If the discharge current, its duration and the lowest permissible voltage on discharge rate are known, the required capacity of battery can be calculated. Figure 3 shows the discharge characteristics of a flooded tubular plate solar battery after capacity tests from 0.05 to 0.5 C. It is observed from Fig. 3 that at 10 A (C10) discharge, the battery was able to supply the load for more than 615 min. However, when a higher discharge current was used (20–50 A), the capacity of the battery was observed to be reduced from 78.2% at 20 A to about 50% at 50 A rate. Table 1 summarizes the inferences from the capacity test done at different discharge rates (currents). Discharge duration obtained from laboratory tests and those specified by 2.15

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Fig. 3 Discharge voltage ss.time characteristics of 105 Ah flooded lead acid battery derived from repeated capacity tests in the laboratory

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Table 1 Approximate output as a percentage of C10 capacity Maximum discharge duration in hours for flooded tubular battery (IS13369: 1992)

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IS13369:1992 are compared. It is seen that the battery has given approximately the same or more discharge duration when a constant discharge current was maintained. The approximate output, as a percentage of C10 capacity, is also calculated depicting reduction in capacity at higher discharge rates than C10. Slightly higher discharge durations are obtained in certain tests. This is attributed to the variation in the current during the tests owing to the limitation of charging devices. Overall, it may be concluded that the test results have validated the manufacturer’s specification.

2.3 Simulated Solar Cycling Tests Off-grid photovoltaic plant batteries are charged when sunlight is available and discharged as per load demand. A battery used in a solar photovoltaic energy storage application picks up a little lesser charge in every cycle of discharge and recharge. This is because, the regulation voltage setting in the charge controller limits the charging current after reaching the 80% charge state(referred to as the gassing point voltage) in order to avoid overcharge. This limited current available for about 7– 8 h/day cannot fully recharge the battery. As a result, some of the lead sulphate formed during discharge is not converted back into active materials and becomes harder with repeated cycling. Field experience of the authors in revival of batteries in failed solar power plants has proved that in absence of proper operation and maintenance of batteries; most solar power plants loose almost 50–60% of their output in 3–4 years. By the fifth year, the battery fails catastrophically [1]. In order to avoid failure of the batteries, periodic full charge of the battery is necessary to convert the sulphates into active material. The process of full charge takes about 20 h and is referred to as equalizing charge. Prolonged equalizing charges at low current (3– 4% of battery capacity) help in reducing hard sulphation and revive the battery to improved capacity. The stepwise procedure for equalizing charge is as follows. • Charge the battery with the solar charge controller in boost charge mode and continue till the voltage of 2.4 VPC is reached. While 2.4 VPC setting is preferable

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for quickly charging the battery, many charge controllers have a single setting of 2.25 VPC. The recommended charging cut-off voltage in flooded and sealed VRLA batteries is 2.45 to 2.5 VPC and 2.35 to 2.4 VPC, respectively. Put the controller on equalizing charge mode. For equalizing charging to a flooded battery, a voltage setting of 2.7/2.6 VPC is needed, that is 64.8 V/62.4 volts for a 48 V battery. For VRLA batteries, a voltage of 2.45 VPC or 57.6 for 48 V battery is required. Controllers should have separate settings for flooded and VRLA batteries but most have only one setting at 2.5 VPC. Continue charging at a low constant current till battery voltage reaches and remains constant at the maximum voltage setting for 2–3 h. Equalizing charge current should be limited to 3% of the battery capacity in amperes. For example, the equalizing current for a 100 Ah battery should be 3 A and that for a 600 Ah battery should be 18 A. The solar charge controller should have a current limiting function during equalizing mode. Check temperature rise during the entire process and discontinue charging if it increases by more than 4–5 °C during the equalizing charge. Continue charging after temperature drops. After equalizing, give it a rest for a few hours and then check the battery specific gravity as well as open circuit voltage of flooded and VRLA battery, respectively. If they show full charge, battery backup will increase to original level.

The following section describes in detail the performance of flooded tubular and gel electrolyte VRLA batteries in solar simulated laboratory conditions.

3 Cycling Test on Batteries Used for Solar Power Applications In a cycling test, batteries are discharged up to specific voltage during in every discharge. The battery in every cycle was discharged up to a fixed voltage corresponding to 80% depth of discharge or rather, a 20% state of charge (SOC). Change in capacity in every cycle is calculated from the Ah obtained during the entire discharge time period of Td . This is done by constantly recording the discharge current (I d ) for every time step t (e.g. 2–10 min) stating from an initial time t = t i up to the end of the discharge t = T d . A summation of the all these readings over the entire discharge time horizon of T d gives the Ahout as follows: Ahout =

t=T d

Id t

(2)

t=ti

Solar charging conditions were simulated by setting the charging current to 0.12 C constant current followed by 2.4 VPC constant voltage for total 8 h. During the recharge, the energy fed into the battery in Ah is similarly recorded over a time

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horizon of T c by discretely recoding the charging current(I c ) for every time step t (e.g. 2–10 min). Thus, Ahin is determined as: Ahin =

t=T c

Ic t

(3)

t=ti

Figure 4 illustrates the results of cycling tests conducted on a C10 rated flooded lead acid battery of 105 Ah for about 60 days in laboratory conditions. The charge acceptance and hence the Ah input into the battery is a function of the regulation voltage which is set in the charge controller. In the initial 12 cycles (Fig. 4a), the regulation voltage was set at 2.7 VPC. Hence, a higher charge was put in the battery. As the discharge was limited up to the 80% DOD, this resulted in a lower Ah efficiency. Therefore, in the next set of cycles (i.e. 13–51), the regulation voltage was lowered to 2.4 VPC. This improved the Ah efficiency from earlier ~82 to ~92% as seen from the reduced difference between and charge (input) and discharge (output) obtained (Fig. 4b). It is noted that on the 26th cycle (on 26th day), the battery output diminished from 80 to 56 Ah, which is a drop of 30% capacity in 26 cycles. Equalizing charge was imparted on 27th cycle as per the procedure for equalizing charge mentioned in Sect. 2.3. The charging was possible by raising the charging voltage to 2.75 VPC as against the regular cycling charge voltage of 2.4 VPC to overcome high internal

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Fig. 4 a Capacity degradation of flooded flat plate battery at 80% DOD during cycling with charging regulation voltage at 2.7 VPC b capacity degradation of flooded flat plate battery during cycling at 80% DOD and charging regulation voltage at 2.4 VPC at laboratory solar simulated conditions

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resistance of battery. On the 28th cycle, the battery regained 100% capacity and provided the same 80% output or 80 Ah as on 1st day/1st cycle. The battery performed much better for next 25 days, and then, next equalizing charge was due. Figure 5 shows the effect of cycling on the capacity drop of a VRLA gel electrolyte battery under solar simulated laboratory charge and discharge cycles. The charge controller regulation voltage was set to 2.4 VPC. The discharge capacity reduced from 121 Ah in the first cycle to 105 Ah in the 22nd cycle showing 13.2% degradation. Equalizing charge was imparted on the 23rd cycle and subsequent discharge capacity was obtained as 120.8 Ah. Thus, 100% revival of capacity was obtained on equalizing charge. In order to investigate the effect of regulation voltage, setting the charge controller regulation voltage for charging was set at 2.25 VPC from the 24th cycle (as against 2.4 VPC in the earlier cycles). However, it is noted that the battery charge acceptance is seen to fall drastically to about 26% showing a case of undercharging (Fig. 5b). During cycling from 23 to 31, the capacity degradation is also found to increase to about 21% in comparison to 13.2% drop observed in cycle 1–22. Thus, the charge controller is seen to be unable to provide required energy to Input Ah

(a)

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Fig. 5 a Capacity degradation for VRLA gel electrolyte battery at 80% DOD and regulation voltage of 2.4VPC during cycling in laboratory at solar simulated conditions b capacity degradation on cycling at regulation voltage of 2.25 VPC

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Table 2 Observations on capacity degradation on cycling Test performed Battery type

Observation

Inference

Cycling test

• 30% capacity degradation of in 25 days. • Capacity drop of 1.2% per cycle at 2.4 V/cell cut-off voltage.

• Frequency of equalization needed is once per month

Flooded tubular

VRLA gel electrolyte • 13.2% Capacity degradation of in 22 days. • Capacity drop of 0.56% per cycle at 2.4 VPC cut-off voltage. • Lower charge acceptance (undercharging) at 2.25 VPC

• Frequency of equalization needed is once in two months • Optimum charge voltage setting is 2.4 VPC

recharge the battery from last discharge resulting in undercharging and progressive lower output due to sulphation. An undercharge may occur due to lower regulation voltage setting of the charge controller or inadequate capacity of solar array. Table 2 provides an overview of the performance on cycling duty for both the battery types tested. It is clearly noted that while the flooded batteries are rugged, their capacity degradation on cycling is of the order of 1.2% per cycle. However, the VRLA battery subjected to similar charge–discharge cycles degraded by about 0.56% per day. Improved performance of the VRLA gel electrolyte battery may be attributed to better physical contact between electrodes and electrolyte. Further, the capacity drop was also found to be a function of the charging cut-off point. It was noted that when the charging cut-off was lowered to 2.25 VPC, there was a 26% degradation of the battery on cycling.

4 Conclusions The capacity of battery reduces progressively during cycling duty in off-grid photovoltaic power plants. In this work, the capacity degradation of batteries used in solar power applications was studied by through experimentation in laboratory conditions. Through the testing, it has been shown that (i)

The batteries were found to be losing both charge input and energy output in every cycle. Capacity degradation is thus universal in such batteries.

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(ii) It was possible to simulate the capacity degradation process with a battery set and SPV plant in laboratory by daily charging the battery with SPV and discharging the battery up to 20% state of charge in the night. (iii) A pattern of degradation was found—that a flooded tubular battery degrades by 1.2% in each cycle when discharged by 80% each cycle (day), whereas a VRLA gel electrolyte battery degrades by 0.56% per cycle under similar conditions. This provided an indication of the service recharge/equalizing periodicity. (iv) Almost full capacity of battery can be recovered by a service charge process with controlled low current and high voltage charging also known as equalizing charge in common battery parlance—This proved that the degradation was only temporary and can be revived by proper servicing. (v) A proper equalizing charge requires an additional 10 h over the 80% state of charge(SOC) point of the battery or about 20 h from 20% SOC. It is proven through the tests conducted on solar power plant batteries that their life can be prolonged to the designed lifetime by maintaining proper periodicity of equalizing charge. This periodicity depends on the type of battery used and the depth of discharge. Commercial solar charge controllers do have an equalizing mode; nevertheless, it is ineffective as the entire equalization takes place in duration of 1–2 h against the prescribed time of 20 h required for full equalization as obtained from laboratory tests. Acknowledgements This work was supported by Customized Energy Solutions Pvt Ltd., Pune under the Clean Energy Access Network (CLEAN) project, Award Number AID-386-A-14-00013. The authors are thankful to Mr. Viral Patel and Ms. Chaitra Dandavatimath who were instrumental in carrying out all the tests described in this manuscript.

References 1. Clean Energy Access network ‘A Detailed Manual On Lead Acid Battery Operation & Maintenance For Solar PV Plants’ Homepage. http://www.thecleannetwork.org/resources/reports-pub lications/technology/. Last accessed 11 Feb 2019 2. Ruetschi, P.: Aging mechanisms and service life of lead–acid batteries. J. Power Sources 127(1– 2), 33–44 (2004) 3. Brik, K., Ben Ammar, F.: The fault tree analysis of lead acid battery’s degradation. J Electr Syst 4–2, 1–12 (2008) 4. May, G.J., Davidson, A., Monahov, B.: Lead batteries for utility energy storage: a review. J Energy Storage 15, 145–157 (2018) 5. Merrouche, W., Achaibou, N., Bouzidi, B., Kasser, M., Trari, M.: Lead-acid battery degradation mechanisms in photovoltaic systems PVS. In: The 3rd International Workshop on Integration of Solar Power into Power Systems SIW2013, At London, UK, Volume: 2013 6. Comparison between Flat and Tubular Positive Plates, White paper: Storage Battery Systems, LLC Homepage. https://www.sbsbattery.com/PDFs/SBS_WP_101_BattComp-WithRefs.pdf. Last accessed 12 Feb 2019

Saving Electricity, One Consumer at a Time K. Ravichandran, Sumathy Krishnan, Santhosh Cibi, and Sumedha Malaviya

1 Introduction Residential electricity consumption in India has tripled since 2000 [1]. It is further projected to rise by more than eight times under the business-as-usual scenario [2]. Urgent efforts are needed to curtail this rise and mitigate emissions from the sector. Demand Side Management (DSM) is a widely implemented and recognized concept that utilities globally have implemented to counter rising energy demand. “Demand Side Management” means the actions of a Distribution Licensee, beyond the customer’s meter, with the objective of altering the end-use of electricity— whether it is to increase demand, decrease it, shift it between high and low peak periods, or manage it when there are intermittent load demands—in the overall interests of reducing Distribution Licensee costs [3]. Historically utilities in India have promoted energy-efficient lights, ACs, and refrigerators through replacement or buy-back schemes offering the energy-efficient alternative at a discount. However, while these technological interventions may substantially bring down electricity consumption, the role of behavior in selecting those technologies, and using them, to deliver the savings remains crucial. Consumer behavior is complex and routinely deviates from rational economic choices. A growing volume of research on energy-consumption behavior of households points to the deviation from the expected impact [4]. Consumers demonstrate behavior driven by their biases, motivations, and social norms. Understanding and changing these motivations is the key to making Energy Efficiency (EE) policies that respond to different types of customers living under different social, demographic, and cultural situations. K. Ravichandran · S. Krishnan (B) · S. Cibi Technology Informatics Design Endeavour, Bangalore, India e-mail: [email protected] S. Malaviya World Resources Institute India, Bangalore, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_31

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Recognizing this need, academics and researchers in the stream of Behavioral economics across the globe have been studying consumer behavior towards electricity use and have concluded that changes in consumer behavior, attitudes, and practices can contribute to electricity savings if the right nudges are provided to consumers [5]. Studies done by utilities in collaboration with behavior scientists in developed countries have experimented with nudges of different types to manage electricity use. But behavior is local and not global in character, so such programs have limited replicability across geographies. This challenge is accentuated in a diverse country like India, requiring a disaggregated approach (based on income levels, size of houses, etc.) to influencing behavior choices. Unfortunately, data or evidence to guide such approaches are missing. Also, behavior change focused programs need long-term implementation to warrant evaluation of the interventions. VidyutRakshaka promoted by a civil society organization and a research organization is a first of its kind attempt in India to use behavior change strategies for sustained reduction in electricity consumption among residential electricity consumers in Bangalore. Its uniqueness comes from the fact that it is an ongoing and growing field-level program, uses bottoms-up data for designing customized nudges, and has a partnership with the utility.

2 Review of Behavior Change Initiatives for Electricity Conservation In an analysis done by European Environment Agency and other partners [6], savings from behavior change programs typically range from 5 to 15% and comprise of both antecedent (pre-program) interventions like information, goal-setting and commitment and consequent (post-program) measures like feedback and rewards [7]. A JPAL study reports a two-percentage reduction in an analysis of Home energy reports on energy consumption by the company OPOWER in the United States [8]. This program has gone beyond the pilot stage and is operational in twelve utilities in the US. A small pilot in Bangladesh reported 9% savings based on nudges like the feedback given to consumers [9]. Researchers in India have captured large variations in electricity consumption even among the consumers holding similar assets and indicate the role of consumer behavior in these differences [10]. Most recently, in partnership with Oracle, Delhi-based utility, BSES Rajdhani rolled out a behavior change program for 5 lakh consumers in New Delhi in 2018. The only other energy use behavior study in India with a small sample of households

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In Delhi, reported that nudges in the form of comparison of electricity use with neighbors resulted in 7% energy savings [11]. The same study reported an increase in electricity use in households given the same nudge but with additional financial rewards. VidyutRakshaka is recognized as DSM initiative by the public electricity utility BESCOM in Bengaluru [12].

3 VidyutRakshaka VidyutRakshaka was conceptualized and is being implemented as an action-oriented program. It has evolved from a pilot of about 500 consumers in two residential neighborhoods of Bangalore in 2014–15. Based on the experience and pilot’s results, a larger city-wide enrollment drive was initiated in 2016 and has resulted in about 3800 voluntary signups for the program as of January 2019. The basic tenets of the program are: • Conservation forms the foundation for any energy efficiency efforts. Impacts of energy efficiency without promoting energy conservation are not sustainable and are known to have a rebound effect [13]. • Energy conservation is primarily driven by behavior change; and positive nudges can encourage conservation. • Continuous reinforcement of conservation measures combined with knowledge on efficient appliances and renewable energy can lead to long-term changes towards sustainable consumption. The program’s unique strengths are building capacity in local communities through one-on-one customized engagement and leveraging social/community influences. The program invites engagement from various stakeholders (consumers, consumer groups, utility) and has helped create a data-driven platform for deeper research to drive policies. Most importantly, it enables consumers to take control of their electricity consumptive actions.

3.1 The Rationale VidyutRakshaka combines both antecedent (information and goal-setting) and consequent interventions(feedback) as highlighted in the schematic Fig. 1 describing the process flow. Feedback is primarily the report with recommendations and nudges which were carefully worded to avoid paternalism or bias to a particular solution.

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Fig. 1 Energy conservation through behavior change

4 Methodology The methodology adopted in VidyutRakshaka (See Fig. 2). arises from the goal of having a continually running program where multiple stakeholders are contributors as well as the receivers of benefits accrued, and the need to make it cost and resourceefficient in the long run.

4.1 Sign up VidyutRakshaka consciously adopted a process of nurturing champions by reaching out to various forums, Resident welfare associations, corporate and educational institutions. Some of them were trained as stewards, those who would do consumer outreach, and sign up consumers for the program. The data input, processing, and report generation have been standardized through an android app [14].

4.2 Data Processing There were two streams of data: (1) profile and asset-related from the consumer and (2) the electricity consumption data for the same consumer from the utility. The latter ensured data accuracy and continuous availability of data removing the dependency on consumers to provide this. The two streams are cleaned up and merged for further analysis.

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Fig. 2 Stepwise implementation of VidyutRakshaka

4.3 Data Analysis The consumer data is first profiled on BHK (bedroom, hall, kitchen has used a surrogate instead of household for practical reasons.) categories: 1, 2, 3, and 4+. The consumption of the consumers is then analyzed on a per month basis (averaging over a year) and on a per capita basis (based on the occupancy details shared by the participants). The annual averages are used to avoid any biases due to variations across months. Consumption was not normalized for seasons as data did not show direct correlation between seasons and consumption for Bangalore. However, for individual participants, information is provided on their seasonal consumption variations. VidyutRakshaka then applies three unique self-iterative models to benchmark each participant as shown in Table 1 1. Neighborhood model 2. Historical consumption model 3. Optimal use model.

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Table 1 Data models for VidyutRakshaka Neighborhood model

Historical model

Optimal—use model

*BHK as classifier in all models Compares use with the average consumption in the neighborhood

Captures use trend from previous years (eliminates seasonality)

Compares split of different enduses against an “optimal use” model

Participants categorized into: • Energy saver—using less than average • Champion—using just at the average • Future champion—above average

Participants categorized into: • Consistent saver • Spender to saver • Consistent spender • Saver to saver • Random behavior

Participants were given comparison of their use and optimal use as per end uses below and recommendations against each: • Lighting • Cooling • Heating • Appliances • Entertainment • Miscellaneous

*BHK (Bedroom Hall Kitchen) is used as a proxy indicator for the house size

Neighborhood Model Among the myriad factors that influence household electricity consumption, normative social influence is found to be playing a definite role [15]. In VidutRakshaka, this aspect is built-in through the neighborhood benchmarking. Every household is benchmarked in his BHK category in his immediate neighborhood and is categorized both on monthly consumption and per capita consumption as follows: • Energy Saver—Those consuming below the neighborhood average • Champion—Those consuming at the neighborhood average • Future Champion—Those consuming above the neighborhood average Historical consumption model Historical consumption model is constructed for each consumer, unlike the neighborhood and optimal model. It captures the electricity consumption trend for the last 3 years, prior to joining the program (Table 2). Optimal use model The average ownership and usage of different types of electrical assets (classified into lighting, heating, cooling, appliances, and entertainment) is modeled for each BHK category. Each consumer is then benchmarked in its BHK category based on this optimal model. This optimal model is iterated periodically based on current data/ usage patterns.

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Table 2 Categorization based on the historical trend Category

Description

Consistent savers

Historically was showing a decreasing trend which is continuing after joining the program

Saver to spender

Historically was showing a decreasing trend but started increasing after joining the program

Spender to saver

Historically was showing increasing trend but started decreasing after joining the program

Consistent spender

Historically was showing an increasing trend which is continuing after joining the program

Inconsistent

Fluctuating or random behavior

4.4 Feedback to Consumers To optimize report generation for VidyutRakshaka participants, an MS Excel based automation has been introduced which helps in generating customized reports for each consumer by using specific criteria. A report template fed with metadata is used to generate customized reports. The final report is divided into the following sections: • • • • • •

Profile data including program ID, date of joining, contact details Benchmarking (against the three models described above) Best practices already followed by the participant Recommendations customized for each participant Goal-setting Other details including contact, resource section, disclaimers.

4.5 Report Duration The program is attempting to settle into a quarterly report cycle with dependence on the utility for the data.

4.6 Aggregate-Level Analysis While individual consumer reports are the main focus of the program, the data available at the aggregated level is emerging to be of great value and use to understand the trend at the residential sector level and to assess the impact of the nudges provided.

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4.7 Utility Engagement VidyutRakshaka is aligned with the utility’s DSM goals. While providing the muchvalued consumer outreach, and promoting DSM, VidyutRakshaka has balanced the interest of the consumer and the utility. While the consumer receives reports to save energy, the aggregated data helps utility understand the residential consumption patterns in Bangalore city. The data can inform both DSM program and energy efficiency policies. Savings have been calculated for participants who have been in the program for more than 1 year. 1553 households thus qualified for assessing the savings. In order to ensure that factors like billing errors, non-occupation, etc. do not affect the savings calculation, some data had to be removed resulting in a final set of 1255 households for this calculation as of January 2019. A summary of indicative results as on date is provided below 1. Benchmarking results: Out of the 1255 consumer data available, 444 are categorized as Energy Savers, 94 as Champions, and 707 as Future Champions (Table 3). 2. Per capita electricity consumption across BHKs is calculated by the total electricity consumption and the number of people within the household (Table 4). 3. In Fig. 3, the annual per capita consumption across different BHK categories is plotted against the number of occupants in the house. This shows the large variation in per capita electricity consumption across BHKs and based on occupancy. In the case of the 4+ BHK houses, the per capita electricity consumption without data set is as high as 2001 units per year and this is almost double India’s per capita electricity consumption value of 1149 units per year [16]. At an aggregate-level, our analysis of 1255 households we find 599 households have reduced consumption by an average of 31 units monthly, approximately, 22% of their cumulative monthly consumption. Table 3 Split of consumption trend across BHKs in numbers Consumption Trend

1 BHK

2 BHK

3 BHK

4 BHK

Energy saver

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320

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Fig. 3 Occupancy versus per capita electricity consumption

5 Conclusion Our program “VidyutRakshaka” shows an electricity savings potential of about 26% (or 19 MU per annum) if adopted at Bangalore city level by all residential consumers. The success of this program showcases the potential of such low investment behavior change programs for DSM. This is a good case study for utilities, State electricity regulatory commissions and Forum of Regulators (FoR). In addition, programs like this provide data-based evidence to address many hitherto unanswered research questions in the area of residential electricity consumption. In addition, programs like this provide data-based evidence to address many hitherto unanswered research questions in the area of residential electricity consumption. • Study of the patterns of electricity consumption based on the trends in the ownership of different appliances and equipment and their variation across household sizes. This can give inputs to the Standard and Labeling program by Bureau of Energy Efficiency, India. • Study the demographic impact on residential electricity consumption and socioeconomic inequalities in consumption within a city. This can help in the design of energy efficiency policies and programs catered to the varying needs of different communities. • For Bangalore, seasonal index calculation did not point to clear seasonal consumption changes. However, a better understanding of variations in the seasonal consumption helps in better power purchase planning opportunity for the utility based on the reliable ground-level data. • Government through various schemes has promoted various efficient appliances programs. This kind of consumer-driven behavior change programs addresses the opportunity to neutralize the rebound effect of energy-efficient appliances. To conclude, developing countries like India with diverse demographics need disaggregated field-level data to plan energy policies and programs like VidyutRakshaka fill this gap.

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Acknowledgements Authors are thankful to the Bangalore Electricity Supply Company (BESCOM) for providing data and guidance in support of VidyutRakshaka. Authors would like to acknowledge the Corporate Citizenship support and funding from SocieteGenerale Global Solution Centre, Bangalore.

References 1. Prayas homepage. http://www.prayaspune.org/peg/trends-in-india-sresidentialelectricitycons umption. Last accessed 14 Feb 2019 2. Global buildings performance network homepage. https://www.gbpn.org/newsroom/reportresidential-buildings-indiaenergyuseprojections-and-savings-potentials. Last accessed 11 Feb 2019 3. Forum of regulators. http://www.forumofregulators.gov.in/Data/study/Model%20DSM%20R egulations.pdf 4. Elisha, R.: Household energy use: applying behavioral economics to understand consumer decision-making behavior. Renew Sustain Energy Rev. 1–10 (2015) 5. Anant: Nudges in the marketplace: the response of household electricity consumption to information and monetary incentives. J. Econ. Behav. Organ. 117 (2017) 6. European environment agency (EEA) homepage. https://www.eea.europa.eu/publications/ach ieving-energy-efficiencythroughbehaviour/file. Last accessed 2019/02/11 7. Abrahamse W: The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. J. Environ. Psychol. 1–10 (2007) 8. J-PAL homepage. https://www.povertyactionlab.org/evaluation/opowerevaluatingimpact-hom eenergy-reports-energy-conservation-united-states. Last accessed 09 Feb 2019 9. Khan, I.: Electrical energy conservation through human behavior change: perspective in Bangalore. Int. J. Renew. Energy Res. 1–10 (2015) 10. CPR India news page. http://www.cprindia.org/news/6585. Last accessed 14 Feb 2019 11. EPIC. https://epic.uchicago.in/wp-content/uploads/2017/05/UCH-022117_NudgesInTheM arketplace_final.pdf 12. BESCOM DSM page. https://bescom.org/wexena-project-details. Last accessed 01 Feb 2019 13. IDC. http://www.idconline.com/technical_references/pdfs/electrical_engineering/Side_effe cts_of_energy_efficiency_measures.pdf. Last accessed 14 Feb 2019 14. Google play store. https://play.google.com/store/apps/details?id=in.exuber.vidyutrakshakau ser&hl=en. Last accessed 15 Feb 2019 15. Frederiks, E.R., Stenner, K., Hoban, E.V.: Household energy use: applying behavioural economics to understand consumer decision-making and behaviour. Renew. Sustain Energy Rev. 41, 1385–1394 (2015) 16. Executive summary of power sector, January 2019. http://cea.nic.in/reports/monthly/executive summary/2019/exe_summary-01.pdf. Last accessed 02 Aug 2019

Study of Effects of Water Inlet Temperature and Flow Rate on the Performance of Rotating Packed Bed Saurabh and D. S. Murthy

Nomenclature k [J/kg] P [N/m2 ] Gk [m2 /s2 ] Gb [m2 /s2 ] U [m/s] x [m]

Turbulence kinetic energy Pressure Generation of Turbulence kinetic energy due to mean velocity gradients Generation of Turbulence kinetic energy due to buoyancy Stream-wise velocity Cartesian axis direction

Special characters α [m2 /s] ε [m2 /s3 ] ρ [kg/m3 ] τ [N/m2 ] μ [N.s/m2 ]

Thermal diffusivity Turbulence dissipation rate Physical density Stress tensor Molecular viscosity

Saurabh (B) · D. S. Murthy Department of Mechanical Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India e-mail: [email protected]

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Subscripts b eff i, j

Buoyancy Effective expression Component in Cartesian direction

1 Introduction The evolution of rotating packed beds as gas–liquid contacting device makes use of centrifugal acceleration field to achieve the intensification, which is far greater in magnitude (100–1000 times) than the conventional gravitational acceleration [1, 2]. This intensification facilitates for interaction between multi-phase fluids flowing in the counter-current radial direction. The use of rotating packed bed gained significant appreciation in the replacement of giant distillation towers used in chemical and processing industries, thereby attaining overall volume reduction up to 2–3 order of magnitude. Several other distinctive fields of applications include oil and refinery industries, drug and pharmaceutical industries, post-combustion carbon capture [3], preparation of battery grade lithium bicarbonate [4], etc., wherein rotating packed beds are amicably subjected to a range of processes viz. adsorption, biosorption, dehumidification, degasification, vacuum distillation, stripping, scrubbing, etc. [5–8].

2 Background The preliminary literature on rotating packed bed refers to the work presented as Higee, an acronym for high-gravity [9–12]. The journey started with basic fluid stripping apparatuses, e.g., Podbelniak’s reactor, Chamber’s centrifugal reactor, spinning disc reactor, etc., and drastically evolved with the advent of rotating packed bed which further progressed to rotating zig-zag bed, split bed packing [13–19]. Some prominent visual investigations regarding the fluid flow in rotating packed bed are reported in [20, 21]. The study of rotating packed bed performance, based on number of independent variables viz. fluid inlet conditions, the packing, and rotational parameters is presented in [2, 22]. An appraisal of prominent parameters involved in operation of rotating packed bed has been discussed in [23]. Operating pressure drop characteristics are mentioned elsewhere [24]. However, these explorations solely belong to the mass transfer domain. Furthermore, intruding techniques used in the experimental approach often deviate the flow making it impossible to capture the intricate and original fluid profiles. For this reason, computational fluid dynamics (CFD) simulation for inquisition of water

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Fig. 1 Schematic diagram of rotating packed bed

inlet temperature and flow rate effects on the pressure, velocity, and temperature distribution across the rotating packed bed have been taken up in this segment of communication.

3 Rotating Packed Bed Setup The setup for rotating packed bed consists of packing material duly secured between two perspex discs of inner and outer diameters as 60 mm, 310 mm respectively and separated 25 mm apart. Liquid inlet is provided at the eye of the rotor and provision of liquid outlet is made at casing bottom. Contrarily, air inlet is from top of the casing whereas outlet is drawn from an annular arrangement with respect to the duct for liquid inlet. Casing is an enclosing structure that secures the liquid from splashing and helps in collecting the liquid for easy drain from the bottom. A simple sketch of the same is shown in Fig. 1.

4 Geometry and Method The geometric modeling and meshing corresponding to the packing structure have been performed in the design modeler and ICME meshing modules of ANSYS Fluent code, respectively. The inner diameter 60 mm, outer diameter 310 mm and height 25 mm, packing porosity 0.95, and specific area 4000 m2 /m3 have been considered for the cylindrical packing comprising meshing of 1,000,000 hexahedral elements. The re-normalization group (RNG)-based k-ε model has been retained in solving the instantaneous Navier-Stokes equation because of its ability of incorporating the swirl effect along with the flow turbulence. The governing equations for solution variables viz. pressure, momentum, and turbulent kinetic energy are given below:

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Continuity Eq. ∂ρ + ∇(ρU ) = 0 ∂t

(1)

∂ (ρU ) + ∇(ρU × U ) = ∇ p + ∇.τ + Sm ∂t

(2)

Momentum Eq.

Equation for turbulent kinetic energy   ∂ ∂k ∂ ∂ αk μe f f + G k + G b − ρε − Ym + Sk (ρkU ) = (ρk) + ∂t ∂ xi ∂x j ∂x j

(3)

Eq. for turbulent dissipation   ∂ε ε ∂ ∂ ∂ αε μe f f + C1ε (G k + C3ε G b ) (ρεu i ) = (ρε) + ∂t ∂ xi ∂x j ∂x j k − C2ε ρ

ε2 − Rε + Sε k

(4)

The solution strategy made via pressure–velocity coupling, Green-Gauss cellbased spatial discretization, pressure scheme PRESTO, second-order upwind schemes for momentum and other variables have been adopted. The validation plot has been shown in Fig. 2, showcasing the comparison of current work with the previous literature [22, 25]. 0 rpm Sandilya et al. 0 rpm current work 950 rpm Llerena-Chavez et al. 1420 rpm Sandilya et al. 1420 rpm current work

Fig. 2 Validation plot showing variation of pressure drop versus gas flow rate

0 rpm Llerena-Chavez et al. 950 rpm Sandilya et al. 950 rpm current work 1420 rpm Llerena-Chavez et al.

600 Pressure drop (Pa)

500 400 300 200 100 0

6.5

13.1

19.5

Air flow rate (m3/h)

25.3

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Fig. 3 Pressure contours (a–l) showing combined effect of water inlet temperature and RPM. For 313 K a 0 rpm, b 600 rpm, c 1200 rpm; For 318 K d 0 rpm, e 600 rpm, f 1200 rpm; For 323 K g 0 rpm, h 600 rpm, i 1200 rpm; For 328 K j 0 rpm, k 600 rpm, l 1200 rpm

5 Results and Discussion The effect of fluid flow rates and inlet temperatures through rotating packed bed have been analyzed in this section. The performance of rotating packed bed primarily depends on the pressure distribution and velocity profiles developed inside the packing when subjected to with or without rotation. The thermal performance however includes the study of temperature dissipation throughout the rotating packed bed. For this purpose, the results have been assembled.

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5.1 Effect of Water Inlet Temperature The water inlet temperature has been taken in the range of 313–328 K. Figure 3 shows the combined effect of water inlet temperature and packing rotation on the pressure distribution across the rotating packed bed. It is clearly shown from the contours that the pressure inside the packing increases in the radially outward direction with increase in the water inlet temperature against the overall range 18.5–2210 Pa taken for the plot. For inlet temperatures 313–318 K, the pressure first decreases from stationary condition to 600 rpm and later on increases up to packing rotation 1200 rpm. However, beyond 318 K, a regular increase in the pressure is observed up till 328 K for all the values of rotation from 0 to 1200 rpm. The range of variation in lower limits is very marginal, whereas there is significant variation in the upper limits of pressure with a range of 393.1 Pa. The lower most value for maximum pressure is recorded as 1816.1 Pa corresponding to 313 K, 600 rpm, whereas the highest value turns out to be 2209.3 Pa for 328 K, 1200 rpm under the range of investigation. The effect of water inlet temperature on the velocity plots obtained for rotating packed bed can be viewed vertically along the columns of Fig. 4. Moreover, a horizontal glance presents more interesting insight into the pattern of velocity distribution along the radial direction attributing the effects of rotation from 0–1200 rpm. Under the stationary rotor condition, i.e., N = 0 rpm, the velocity decreases in the radially outward direction for all the inlet temperatures. It holds for the reason that as the flow area increases, the velocity decreases in that direction. Contrary to this, with the onset of rotation, the pattern of velocity completely reverses following the general rule, V = ω × r . For this reason, the velocity is lower at inner radius of packing and gradually increases toward the outer radius of packing. The range of velocity from 1.0 to 18.3 m/s has been selected for comparison of all the profiles. Altogether, the velocity distribution inside the rotating packed bed seems to be independent of water inlet temperature. However, the influence of rotation bears significant discernment to the maximum attainable velocity inside the packing. The value of maximum velocity for 0, 600, 1200 rpm are 7.2, 9.23, and 18.28 m/s, respectively. Figure 5 shows the distribution of temperature across the rotating packed bed under the influence of water inlet temperature ranging from 313 to 328 K. The minimum temperature in the figure corresponds to that of air inlet at 298 K and the maximum value varies showcasing the combined effect of water inlet temperature and packing rotation as a whole. The first column corresponds to stationary rotor condition and reveals incomplete mixing of the two fluids flowing in counter-current direction inside the packing. However, the increase in water inlet temperature can easily be observed while moving down the columns. With the onset of rotation, the intermixing of air–water fluids tunes in smooth contours for packing temperature decreasing radially outwards in direction. This can be assimilated from the fact that, rotation allows the fluid streams to cover helical trajectories providing better time for intermixing and thus facilitating heat transfer, unlike the straight radial path in stationary condition.

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

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(k)

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Fig. 4 Velocity contours (a–l) showing combined effect of water inlet temperature and RPM. For 313 K a 0 rpm, b 600 rpm, c 1200 rpm; For 318 K d 0 rpm, e 600 rpm, f 1200 rpm; For 323 K g 0 rpm, h 600 rpm, i 1200 rpm; For 328 K j 0 rpm, k 600 rpm, l 1200 rpm

5.2 Effect of Water Flow Rate The effect of water flow rate on pressure and temperature distributions across the rotating packed bed has been illustrated in Figs. 6 and 7, respectively. The range of flow rate 0.5–1.5 kg/s has been considered for this purpose. The pressure drop inside the packing increases with increase in fluid flow rate for stationary rotor condition. Nevertheless, with the onset of rotation, the pressure drop first decreases up to 600 rpm and then increases to attain a maximum value of 999.15 Pa at 0.5 kg/s, 1200 rpm. The maximum pressure corresponding to water flow rate 1.0 kg/s is 945.12 Pa and that for 1.5 kg/s is 971.44 Pa, both at 1200 rpm. It is easily inferred that the use of 1.5 kg/s flow rate assisted in compliance with 1200 rpm facilitates lower pressure drop across the rotating packed bed as compared to 0.5 kg/s flow rate. The effect of liquid flow rate, however, remains marginal on the velocity profiles as compared to the effect of rotation, as already been discussed in the previous section. The temperature distribution shows abrupt mixing of the two fluids under 0 rpm

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

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Fig. 5 Temperature contours (a–l) showing combined effect of water inlet temperature and RPM. For 313 K a 0 rpm, b 600 rpm, c 1200 rpm; For 318 K d 0 rpm, e 600 rpm, f 1200 rpm; For 323 K g 0 rpm, h 600 rpm, i 1200 rpm; For 328 K j 0 rpm, k 600 rpm, l 1200 rpm

condition. After the introduction of rotation, the temperature profiles start following continuously uniform pattern along the radial direction for every run of flow rates. However, the consistency of temperature dissipation is highest at 0.5 kg/s flow rate, 1200 rpm.

6 Conclusions From the above discussion, it can be summarized that the use of higher temperatures at water inlet implies higher pressure drop across the packing, although it assists in attainment of maximum value of pressure itself. The use of higher flow rate 1.5 kg/s along with 1200 rpm fairly supports for lower pressure drop. The effect of packing rotation eventually predominates rendering almost negligible effect of water inlet temperature and flow rate on the velocity profile inside the packing. A maximum range of temperature shredding has been reported for the use of higher water inlet

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 6 Pressure contours (a–i) showing combined effect of water flow rate and RPM. For 0.5 kg/s a 0 rpm, b 600 rpm, c 1200 rpm; For 1.0 kg/s d 0 rpm, e 600 rpm, f 1200 rpm; For 1.5 kg/s g 0 rpm, h 600 rpm, i 1200 rpm

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 7 Temperature contours (a–i) showing combined effect of water flow rate and RPM. For 0.5 kg/s a 0 rpm, b 600 rpm, c 1200 rpm; For 1.0 kg/s d 0 rpm, e 600 rpm, f 1200 rpm; For 1.5 kg/s g 0 rpm, h 600 rpm, i 1200 rpm

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temperature 328 K at 600 rpm. On the other hand, lower flow rate 0.5 kg/s and 1200 rpm facilitates consistency in intermixing of fluids thereby providing uniform temperature drop. These results on rotating packed bed can well be customized in the domain of heat transfer for addressing giant and voluminous size of conventional cooling towers [26].

References 1. Ramshaw, C., Mallinson, R.H.: Mass transfer process, U.S. Patent (1981) 2. Kumar, M.P., Rao, D.P.: Studies on a high-gravity gas-liquid contactor. Ind. Eng. Chem. Res. 29, 917–920 (1990). https://doi.org/10.1021/ie00101a031 3. Wang, M., Joel, A.S., Ramshaw, C., Eimer, D., Musa, N.M.: Process intensification for postcombustion CO2 capture with chemical absorption: a critical review. Appl. Energy 158, 275– 291 (2015). https://doi.org/10.1016/j.apenergy.2015.08.083 4. Liu, W., Chu, G., Li, S., Bai, S., Luo, Y., Sun, B.: Preparation of lithium carbonate by thermal decomposition in a rotating packed bed reactor. Chem Eng. J. 1–7 (2018). https://doi.org/10. 1016/j.cej.2018.09.090 5. Liu, Z., Liang, F., Liu, Y.: Artificial neural network modeling of biosorption process using agricultural wastes in a rotating packed bed. Appl. Therm. Eng. 140, 95–101 (2018). https:// doi.org/10.1016/j.applthermaleng.2018.05.029 6. Gu, Y., Zhang, X.: A proposed hyper-gravity liquid desiccant dehumidification system and experimental verification. Appl. Therm. Eng. 2019(113879), 1–9 (2019). https://doi.org/10. 1016/j.applthermaleng.2019.113871 7. Li, W., Song, B., Li, X., Liu, Y.: Modelling of vacuum distillation in a rotating packed bed by Aspen. Appl. Therm. Eng. 117, 322–329 (2017). https://doi.org/10.1016/j.applthermaleng. 2017.01.046 8. Li, W., Yan, J., Yan, Z., Song, Y., Jiao, W., Qi, G., et al.: Adsorption of phenol by activated carbon in rotating packed bed: Experiment and modeling. Appl. Therm. Eng. 142, 760–766 (2018). https://doi.org/10.1016/j.applthermaleng.2018.07.051 9. Tung, H.H., Mah, R.S.H.: Modeling liquid mass transfer in HiGee separation process. Chem. Eng. Commun. 39, 147–153 (1985). https://doi.org/10.1080/00986448508911667 10. Chen, J.: The recent developments in the HiGee technology. In: Presented at the GPE-EPIC Conference, Venice, Italy (2009) 11. Li, Y., Yuli, Y., Xuli, Z., Lili, X., Liu, X., Ji, J.: Rotating zigzag bed as trayed HIGEE and its power consumption. Asia-Pacific J Chem Eng 8, 494–506 (2013). https://doi.org/10.1002/apj. 1688 12. Zhang, D., Zhang, P., Zou, H., Chu, G., Wu, W., Zhu, Z., et al.: Application of HIGEE process intensification technology in synthesis of petroleum sulfonate surfactant. Chem. Eng. Process Process Intensif 49, 508–513 (2010). https://doi.org/10.1016/j.cep.2010.03.018 13. Podbielniak, W.J.: Centrfugal, countercurrent contact apparatus, U.S. Patent (1954) 14. Siptrott, F.M. Chamber’s Centrifugal Reactor, U. S. Patent, 1969 15. Brechtelsbaurer, C., Lewis, N., Oxley, P., Ricard, F., Ramshaw, C.: Evaluation of a spinning disc reactor for continuous processing. Org. Process Res. Dev. 5, 65–68 (2001) 16. Munjal, S., Dudukovic, M.P., Ramachandran, P.: Mass-transfer in rotating packed beds-I: development of gas-liquid and liquid-solid mass-transfer correlations. Chem. Eng. Sci. 44, 2245–56 (1989). https://doi.org/10.1016/0009-2509(89)85159-0 17. Munjal, S., Dudukovic, M.P., Ramachandran, P.: Mass-transfer in rotating packed beds-II: experimental results and comparison with theory and gravity flow. Chem. Eng. Sci. 44, 2257–68 (1989). https://doi.org/10.1016/0009-2509(89)85160-7

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18. Wang, G.Q., Xu, Z.C., Yu, Y.L., Ji, J.B.: Performance of a rotating zigzag bed—a new HIGEE 47, 2131–9 (2008). https://doi.org/10.1016/j.cep.2007.11.001 19. Chandra, A., Goswami, P.S., Rao, D.P.: Characteristics of flow in a rotating packed bed (HIGEE) with split packing. Ind. Eng. Chem. Res. 44, 4051–4060 (2005). https://doi.org/10.1021/ie0 48815u 20. Burns, J.R., Ramshaw, C.: Process intensification: visual study of liquid maldistribution in rotating packed beds. Chem. Eng. Sci. 51, 1347–1352 (1996). https://doi.org/10.1016/00092509(95)00367-3 21. Guo, K., Guo, F., Feng, Y., Chen, J., Zheng, C., Gardner, N.C.: Synchronous visual and RTD study on liquid flow in rotating packed-bed contractor. Chem. Eng. Sci. 55, 1699–1706 (2000). https://doi.org/10.1016/S0009-2509(99)00369-3 22. Sandilya, P., Rao, D.P., Sharma, A., Biswas, G.: Gas-Phase mass transfer in a centrifugal contactor. Ind. Eng. Chem. Res. 40, 384–392 (2001). https://doi.org/10.1021/ie0000818 23. Rao, D.P., Bhowal, A., Goswami, P.S.: Process intensification in rotating packed beds (HIGEE): an appraisal. Ind. Eng. Chem. Res. 43, 1150–1162 (2004). https://doi.org/10.1021/ie030630k 24. Kevyani, M., Gardner, N.C.: Operating characteristics of rotating beds 1989:48 25. Llerena-Chavez, H., Larachi, F.: Analysis of flow in rotating packed beds via CFD simulationsdry pressure drop and gas flow maldistribution. Chem. Eng. Sci. 64, 2113–2126 (2009). https:// doi.org/10.1016/j.ces.2009.01.019 26. Saurabh, Murthy, D.S.: Analysis and optimization of thermal characteristics in a rotating packed bed. Appl. Therm. Eng. 165, 114533 (2020). https://doi.org/10.1016/j.applthermaleng. 2019.114533

Integrated Thermal Analysis of an All-Electric Vehicle Vinayak Kulkarni and Shankar Krishnan

1 Introduction Electrical vehicles are becoming increasingly popular nowadays. Simulating electric vehicles and its components can greatly help to analyze the system. It is important to estimate range, energy consumption of vehicle for the given vehicle specifications and environmental conditions. Thermal issues associated with electric vehicle battery packs can significantly affect performance and life cycle of electric vehicle [1]. There are two main vehicle simulation approaches namely backward-facing approach and forward-facing approach. Vehicle simulators using a backward-facing approach answer the question “Assuming the vehicle met the required trace, how must each component perform?” No model of driver behaviour is required in such models. Instead, the force required to accelerate the vehicle through the time step is calculated directly from the required speed trace. The required force is then translated into a torque (often by assuming some efficiency) that must be provided by the component directly upstream, and the vehicle’s linear speed is likewise translated into a required rotational speed. Component by component, this calculation approach carries backward through the drivetrain, against the tractive power flow direction, until the electrical energy use that would be necessary to meet the trace is computed. Figure 1 shows an overview of the backward faced drivetrain model. Vehicle simulators that use a forward-facing approach include a driver model, which considers the required speed and the present speed to develop appropriate throttle and brake commands (often through a PI controller). The throttle command is then translated into a torque provided by the motor and an energy use rate. The torque provided by the motor is input to the transmission model, which transforms the torque according to the transmission’s efficiency and gear ratio. In turn, the computed torque is passed forward through the drivetrain, in the direction of the physical power flow in the V. Kulkarni · S. Krishnan (B) Department of Mechanical Engineering, IIT Bombay, Mumbai 400076, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_33

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Fig. 1 Overview of drivetrain model [3]

vehicle, until it results in a tractive force at the tire/road interface [2]. In this work, we have used backward-facing model for electric vehicle simulation

2 Electric Drivetrain Model 2.1 Model Assumptions • • • • •

Transmission efficiency of gears is 100%. Regeneration is 100% Battery cells are balanced Cell internal resistance do not change with SOC and temperature. Open-circuit voltage of cell do not vary with SOC and temperature.

2.2 Model Description Figure 2 shows overall model built-in MATLAB/Simulink® . Worldwideharmonized light vehicle test procedure (WLTP) driving cycle is used in the current model. WLTP is global harmonized standard for determining level of energy consumption and electric range from light-duty vehicles. Automotive experts from the European Union, Japan, and India, under guidelines of UNECE World Forum for Harmonization of Vehicle Regulations, developed the standard with a final version released in 2015 [4]. Simulation time for the driving cycle is 1477 s and it covers 14.664 km. The forces acting on a vehicle are namely inertia force due to acceleration of vehicle, aerodynamic force, gradient force due to road gradient and road friction force, which arises due to rolling friction between road and tire. Traction force is the addition of all the above forces. Finertia = m v av

(1)

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341

Fig. 2 Overall electric and thermal model of electric vehicle

Faero =

ρ Acd (vv + vw )2 2

(2)

Fgradient = m v g sin(α)

(3)

Ffriction = μm v g; v = 0 = 0; v = 0

(4)

Ftraction = Finertia + Faero + Fgradient + Ffriction

(5)

Ftraction = m v av +

ρ Acd (vv + vw )2 + m v g sin(α) + μm v g 2

(6)

where vv is vehicle velocity which is obtained from driving cycle and av is vehicle acceleration obtained by differentiating vehicle velocity. Table 1 shows the nomenclature and vehicle parameter values used in the model development. These values approximately describe first-generation Chevy Volt plug-in hybrid electric vehicle Table 1 Vehicle and environmental parameters [5]

Parameters

Units

Description

Value

mv

[kg]

Mass of vehicle

1600

μ

[–]

Rolling friction coefficient

0.01

A

[m2 ]

Frontal area

1.84

cd

[–]

Drag coefficient

0.22

P

[kg/m3 ]

Density of air

1.2

vw

m/s

Wind speed

0

g

m/s2

Standard gravity

9.81

A

[radians]

Road gradient

0

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from General Motors Corporation operating in pure electric mode, based on public information prior to vehicle release. Traction power is the multiplication of traction force and velocity. Total power is the addition of traction power and auxiliary power. Auxiliary power includes power required for complete battery management, cooling of electric motor, cooling of power electronics, cabin cooling, fans, pumps, lighting, and entertainment. We have assumed auxiliary power as constant 2 kW as a first-order worst-case approximation. Ptraction = Ftraction × velocity

(7)

Ptotal = Ptraction + Pauxillary

(8)

Subsequently, Ptotal is fed as input to the electrical motor. Hence, electric motor has to provide power equal to Ptotal /ηmotor where ηmotor is the efficiency of the electric motor. We have assumed efficiency of electric motor to be constant and in worst-case equal to 0.8 in our model. Power Ptotal /ηmotor goes as an input to power electronics. If ηpe is the efficiency of power electronics, then output power from power electronics which will be demanded power from battery will be Ptotal /(ηmotor ηpe ). We have assumed ηpe as 0.95 in our model. Table 2 shows the specifications of the battery that we have used in our simulation. We have assumed that all the cells are balanced and hence total battery power equally divided among the cells. Hence, power demanded from individual cells Pd is Ptotal /(N × ηmotor ηpe ). Figure 3 shows the battery model used in the model. In our model, we have assumed that open-circuit voltage (OCV) is constant and does not vary with time. Terminal voltage v(t) is given by: v(t) = OCV − i(t)R0

(9)

Table 2 Assumed battery parameters [5, 6] Parameter

Unit

Description

Value

Configuration

[–]

Lithium-ion battery-3 cells in parallel, 96 in series

3p96s

N

[–]

Total cells in battery pack

288

OCV

[V]

Constant open-circuit voltage

3.7

R0

[]

Internal cell resistance

0.005

C

[Ah]

Cell capacity

15

ηcoloumb

[–]

Cell columbic efficiency

0.98

SOC0

[%]

Initial state of charge

75%

B

[kwh]

Battery capacity

16

DOD

[%]

Allowable depth of discharge

50

Integrated Thermal Analysis of an All-Electric Vehicle

343

Fig. 3 Schematic of battery model employed [7]

Pd = v(t)i(t) = (OCV − i(t)R0 )i(t)

(10)

Simplifying above expression, we get quadratic expression in i(t) a solution of which can be given as  i(t) =

SOCfinal

OCV −



(OCV2 − 4Pd (t)R0

 (11)

2R0

100 × ηcoloumb = SOC0 − C × 3600

sim  time

i(t)dt

(12)

0

where simtime is simulation time in seconds. This above method to calculate SOCfinal is known as Coulomb counting. Note that battery current is 3 × i(t) in this case as three cells are connected in parallel. We can obtain range of a vehicle, battery energy efficiency and other output parameters like energy consumed in the current cycle and energy consumed per 100 km using the following Eqs. 13–16, respectively. Range(km) =

(SOCfinal − SOC0 ) × Distance covered in current cycle(km) (13) DOD sim  time

Energy consumption(kwh) =

(OCV)i(t)dt

(14)

time ∫sim v(t)i(t)dt 0 simtime ∫0 (OCV)i(t)dt

(15)

0

Battery energy efficiency = Energy consumption/100 km(kwh) =

100 × Energy consumption(kwh) Distance covered in current cycle(km) (16)

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Fig. 4 Schematic of component-level energy balance

3 Integrated Thermal Model 3.1 Model Assumptions • • • •

Lumped capacitance model is used. Electric vehicle components are not in direct contact with the environment. Only cooling requirement of the battery is considered. Liquid cooling system is utilized for cooling and coolant absorbs constant value of heat from components.

3.2 Model Descriptions Using energy balance as shown in Fig. 4, we can arrive at the equation for power loss in a component. Assuming power loss equal to heat generation inside a component, using a lumped capacitance model, the component temperature Tm (t) can be obtained by solving the differential equation. mcpm

dTm = Q rem + Q loss dt

(17)

Q rem = kA.(T∞ − Tm )

(18)

where Q rem is the heat removed by thermal management system. Q loss is heat generated inside the component due to power loss. mcpm is thermal mass of the component [3]. Table 3 provides values of thermal mass assumed for the calculations. Note that there is no ambient temperature term in the differential equation for the component as no direct contact of component and environment is allowed. Table 3 Allowed temperature range, thermal mass and heat generated and removed [3] Battery

Tmin (◦ C) [3] −5

PE

−30

Component

Motor

−30

Tmax (◦ C)[3]

mc p (J/K ) [3]

Q loss (W )

35

130,000

i2 R

85

2000

65

60,000

0

(1−ηpe ) Ptotal ηpe ηmotor

(1−ηmotor )Ptotal ηmotor

kA(W/k) 3 10 1

Integrated Thermal Analysis of an All-Electric Vehicle Table 4 Range and energy consumption per 100 km for different driving cycles

345

Driving cycle

Range (km) Energy consumption per 100 km (kwh)

FTP72

48.31

HWFET

16.88

57.81

14.11

WLTP Class2 52.03

15.67

NEDC

17.49

46.64

4 Model Validation In electric model development, we assumed vehicle parameters (Table 1) and battery parameters (Table 2) similar to series hybrid electric vehicle first-generation Chevrolet Volt. So, we can validate our results by comparing it with full electric range and energy consumption per 100 km for electric-only mode for first-generation Chevrolet Volt. Table 4 shows our results for a range of electric vehicles in electriconly mode for different driving cycles. In model specification of Chevrolet Volt, the company claims 56 km range for only electric mode [8]. As electric vehicle range varies for different driving cycles, our range estimated are approximately close enough to company’s specification. Another important parameter that can be validated from our model is energy consumption per 100 km. We can observe that values estimated by our model are approximately close to specifications [9].

5 Results and Discussion Simulations are carried out for WLTP class2 driving cycle in MATLAB/Simulink® . The results are shown in Fig. 5. Table 5 shows output parameters for WLTP class2 driving cycle. Simulation results for ambient temperature 30 °C are shown in Fig. 6. Initially, it is assumed that all the components are at ambient temperature. We can observe that all the temperature are below maximum temperature limit specified in Table 3. From Fig. 6, we can see that temperature rise is maximum for power electronics and minimum for battery. This is clear from Table 3 that thermal inertia (mc p ) for power electronics is minimum among three-component and battery has maximum value for thermal inertia. So we can conclude that power electronics need more alert controller than the other two components as its temperature can increase/decrease rapidly as compared to other two components for the thermal inertia values specified in Table 3. This analysis helps us to check whether the value taken for Q rem is sufficient to maintain temperature limits provided in Table 3. We can also find out minimum

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Fig. 5 a Variation of SOC during drive cycle and local ups is due to regenerative braking. b Battery current versus time. c Variation of total power with time and current and total power are in phase due to resistive only circuit. d WLTP class2 driving cycle

Table 5 Output parameters for WLTP class 2 driving cycle

Output parameter

Unit

Value

Battery efficiency

[–]

0.9769

Average battery heat

[kw]

0.1293

Average motor heat

[kw]

1.478

Average PE heat

[kw]

0.3889

constant value Q rem in order to keep each component in the prescribed range for the given driving cycle. This analysis also helps us to rank the components based on their cooling requirements. Table 5 shows that average motor heat loss is more than that of battery and power electronics.

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Fig. 6 a Power loss for motor, power electronics and battery. b Variation of battery temperature. c Power electronics temperature. d Variation of motor temperature in driving cycle WLTP class2

6 Conclusions This paper presents a combined electrical and thermal model of an all-electric vehicle. We started with vehicle dynamics to get traction force, traction power, total required power for electric motor, power electronics and battery. We calculated discharge current of battery by using Rint battery model. We used these outputs to get SOC, range, energy consumption of battery. In thermal model, we formulated differential equation for component temperature and formulated heat generated in every component. Finally, we calculated temperature of drivetrain components over the period in the given driving cycle.

References 1. Karimi, G., Li, X.: Thermal management of lithium-ion batteries for electric vehicles. Int. J. Energy Res. 13–24 (2013) 2. Wipke, K., Cuddy, M., Burch, S.: A user-friendly advanced powertrain simulation using a combined backward/forward approach. IEEE Trans. Vehicul Technol. (1999) 3. Weustenfeld, T., Bauer-Kugelmann, W., Menken, J., Straser, K., Koehler, J.: Heat flow rate based thermal management for electric vehicles using a secondary loop heating and cooling system. In: Vehicle Thermal Management Systems Conference and Exhibition (VTMS) (2015) 4. Worldwide harmonized light vehicles test procedure-Wikipedia. https://en.wikipedia.org/wiki/ Worldwide_harmonized_light_vehicles_test_procedure. Last accessed 12 Mar 2019 5. Co-simulating battery and electric-vehicle load week 5-equivalent circuit cell model simulation MOOC offered by University of Colorado—Colorado Springs. https://www.coursera.org/learn/ equivalent-circuit-cell-model-simulation. Last accessed 13 Mar 2019 6. Tamaro, C.: Vehicle powertrain model to predict energy consumption for ecorouting purposes. Virginia Polytechnic Institute and State University, Master of Science Thesis (2016)

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7. How to model voltage polarization? Week1-equivalent circuit cell model simulation MOOC offered by University of Colorado—Colorado Springs. https://www.coursera.org/learn/equiva lent-circuit-cell-model-simulation. Last accessed 12 Mar 2019 8. Green car Congress. https://www.greencarcongress.com/2010/11/volt-20101124.html#more. Last accessed 13 Mar 2019 9. Electric car energy efficiency-Wikipedia. https://en.wikipedia.org/wiki/Electric_car_energy_eff iciency#cite_note-EPA2016MY-1. Last accessed 12 Mar 2019

Computation of Higher Eigenmodes Using Subspace Iteration Scheme and Its Application to Flux Mapping System of AHWR B. Anupreethi, Anurag Gupta, Umasankari Kannan, and Akhilanand Pati Tiwari

1 Introduction India being rich in thorium reserves is designing advanced heavy water reactor (AHWR) to demonstrate the usage of thorium for commercial power production. AHWR is designed as a 920 MW (thermal), 300 MW (electric) heavy water moderated and boiling light water-cooled thermal reactor [1]. AHWR employs pressure tube concept in vertical core orientation, and the heat is removed from the core by boiling light water under natural circulation. The core is neutronically very large and gives rise to high azimuthal neutronic decoupling making it susceptible to slow-induced Xenon oscillations. Hence, reactor monitoring is necessary even during normal operating conditions such as on-power refueling and reactivity device movements [2]. The flux mapping system (FMS) computes the spatial flux distribution online for every 2–5 min and gives the complete picture of the reactor core. The point measurements of neutron flux from the in-core self-powered neutron detectors (SPNDs) B. Anupreethi (B) · A. Gupta · U. Kannan · A. P. Tiwari Homi Bhabha National Institute, Mumbai, India e-mail: [email protected] A. Gupta e-mail: [email protected] U. Kannan e-mail: [email protected] A. P. Tiwari e-mail: [email protected] A. Gupta · U. Kannan Reactor Physics Design Division, Bhabha Atomic Research Centre, Mumbai, India A. P. Tiwari Knowledge Management Group, Bhabha Atomic Research Centre, Mumbai, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_34

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and computations from neutron diffusion theory are combined to provide the threedimensional neutron flux map of the reactor core [3]. This 3-D flux map can be used to derive important information such as zonal powers (to be used by reactor regulating system) and channel powers (to be used by channel monitoring system) which are vital for the reactor operation. In this paper, three methods for the FMS have been described: flux synthesis method (FSM), modified flux synthesis method (MFSM) [3] and a proposed improved MFSM (IMFSM). FSM is the most common method used for FMS. All these methods combine the in-core detector readings and pre-computed flux modes of a reference reactor configuration using least-squares principle to compute the threedimensional flux map. Conventionally, the fundamental mode is computed using power method and the subsequent higher eigenmodes using subtraction method. In this paper, the flux modes are generated using subspace iteration (SSI) technique [4]. This scheme generates a large set of dominant modes simultaneously. The proposed IMFSM uses the fundamental and few higher eigenmodes of the snapshot configuration generated at once by the SSI scheme to estimate the three-dimensional flux distribution of the reactor. For this study, few reactor operational scenarios have been considered. The accuracy of the estimation depends on the number of flux modes used, number, and location of in-core SPNDs in the reactor. AHWR consists of 513 lattice locations in a square lattice of uniform 22.5 cm pitch. The fuel assemblies occupy 452 locations, and the rest is for reactivity control devices and shut-off rods (SORs) [5], and the arrangement can be seen in Fig. 1. The in-core SPNDs are placed in 32 interstitial lattice locations known as neutron flux units (NFU) for point measurement of neutron flux. A maximum of 10 SPNDs (sensitive length 30 cm) can be used in each NFU along the axis, giving a total of 320 SPNDs [6]. In this paper, the effect of variation of number of SPNDs and the set of flux modes on the accuracy of FMS for AHWR have been studied.

2 Flux Mapping Algorithm The basic idea of the flux mapping algorithm (FMA) is that the flux shape at any time in an operating reactor can be represented by a linear combination of fundamental and dominant ‘k’ eigenmodes of the reference configuration of the reactor [3]. The k-eigenvalue equation of the reference state is written as Mφnref =

1 Fφnref , n = 1, 2, 3 . . . kn

(1)

where the operator M accounts for leakage, absorption, and group-to-group transfer and F accounts for fission production. φnref is an eigenmode vector containing flux at all meshes in the reactor, and the superscript ‘ref’ denotes the reference configuration of the reactor. Here, k1 is called the fundamental eigenvalue and k2 , k3 , . . . are called the higher eigenvalues. φ1ref corresponding to k1 is called the fundamental eigenmode

Computation of Higher Eigenmodes Using Subspace Iteration … X\Y 1

2

3

4

5

6

7

8

351

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

A B

S1

C

S2

D

S3

S4

AR1

E

S5

RR1

F

RR2

S6

G

S7

S10

S8

S9

SR1

H

S11

AR2

J

AR3

RR3

K

RR4

S12

SR2

L

S13

SR3

S14

S15

S16

M N

S17

AR4

SR4

S18

S19

S20

SR6

S25

SR7

SR5

AR5

S21

O P Q

S22

S23

S24

R

RR6

S T

S26

RR5 AR6

AR7

S27

U

SR8 S29

S30

V

S28 S31

S32

RR7

W

RR8

S33

X

AR8

S34

S35

Y

S36 S37

Z

Shim Rod S1-37 Absorber Rod RR Regulating Rod SR

Shut off Rod

AR

47500 MWd/te 37500 MWd/te 33500 MWd/te

Fig. 1 AHWR core layout with all reactivity control devices [7]

and the subsequent φ2ref , φ3ref , . . . are the higher eigenmodes of the reference state. The ‘k’ eigenmodes can be pre-computed offline by solving Eq. (1). During reactor operation, the snapshot flux  at any time can be approximated as =

Nm 

an φnref

(2)

n=1

where an is the combining coefficient and Nm denotes the number of ‘k’ eigenmodes. In order to estimate , the combining coefficients have to be calculated. To obtain them, Eq. (2) is assumed to be valid at all points inside the reactor and hence considered valid at detector locations too [2]. This condition leads to the following set of N D (number of in-core detectors) linear equations with Nm (number of eigenmodes) unknowns [3]. φd j ≡ φ(r j ) =

Nm  n=1

  an φnref r j

(3)

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  where r1 , r2 , . . . are the in-core detector locations, φ r j is the computed flux at the jth detector location, and φd j is the measured flux. The above equation can be written in the matrix form as,   [φd ] N D ×1 = φnref N D ×Nm [a] Nm ×1

(4)

As Nm is less than N D , the system becomes overdetermined and such systems can be solved by a least-squares technique,  T    ref T φn Nm ×N D [φd ] N D ×1 = φnref Nm ×N D φnref N D ×Nm [a] Nm ×1

(5)

Equation (5) is similar to system of linear equations (Ax = b) and hence can be solved for combining coefficients using linear solvers. Once the combining coefficients are determined, it can be used in Eq. (2) to compute flux at all points in the reactor. Various flux mapping schemes, as described below, are based on the exploitation of modes from different configurations (such as reference or snapshot) to determine the three-dimensional flux map. FSM is the conventional method for flux mapping. In this method, the fundamental and higher ‘k’ eigenmodes are found a priori for the reference state of the reactor and stored for the use by FMS for online computations during the reactor operation [3]. However, the snapshot configuration of the reactor during operation is different from the reference configuration. To account for this, MFSM method uses the fundamental mode of the snapshot state of the reactor along with the higher ‘k’ eigenmodes of the reference state which are calculated a priori and stored [3]. The flux in the reactor is approximated as  = a 1 φ1 +

Nm 

an φnref

(6)

n=2

With SSI technique, multiple higher eigenmodes can be simultaneously computed for any reactor configuration. Therefore, as an extension to MFSM, IMFSM has been proposed which not only uses the fundamental but also few higher harmonics of the snapshot configuration and the rest from the reference configuration. The neutron flux is approximated for this method as =

NL  n=1

a n φn +

Nm 

an φnref

(7)

n=N L +1

where N L represents the number of higher ‘k’ eigenmodes considered from the snapshot configurations. The above methods follow the same procedure as in Eqs. (3)–(5) to compute the three-dimensional flux map.

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Fig. 2 Eigenmodes of AHWR: a fundamental b first azimuthal c second azimuthal

3 Computation of Flux Modes The set of flux modes is obtained by solving 3D steady-state neutron diffusion equations using subspace iteration technique. This computation is carried out with 3D reactor core analysis code ARCH [8]. The subspace iteration technique is a generalization of power iteration method in which instead of single initial guess vector, multiple initial guess vectors are used and orthonormalized after each iteration. This results in large set of dominant modes simultaneously instead of successive evaluation of higher eigenmodes. The convergence problem associated with degenerate eigenvalues is not faced in this method. The set of flux modes used for FMS of AHWR is fundamental and higher eigenmodes. These eigenmodes (Fig. 2) are generated considering the nominal reactivity device configurations using code ARCH.

4 Simulation of in-Core SPND Readings Since AHWR is in design phase, the in-core detector measurements are not available. The procedure is to compute the fluxes from two-group neutron diffusion equation using ARCH for fine mesh structure. Since the in-core SPNDs are located in the interstitial locations, the readings are obtained by averaging the (diffusion coefficient weighted) neighboring fine mesh thermal fluxes [9]. φd =

Ns 

D dp φ dp /

p=1

φd

thermal neutron flux of dth detector

Ns  p=1

D dp

(8)

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D dp diffusion coefficient corresponding to neighboring mesh ‘p’ of dth detector Ns number of meshes around the dth detector (usually 8) φ dp thermal neutron flux corresponding to neighboring mesh ‘p’ of dth detector.

5 Performance of FMS The root-mean-square (rms) error is used as performance measure for FMS [3]. It is given by   2 N

1  φi − φ¯ i × 100 rms error(% ) = N i=1 φ¯ i

(9)

φ¯ i Reference thermal flux φi Thermal flux estimated from the various FMS methods N Total number of meshes This performance measure is used for comparison of various FMS methods and also for variation of number of in-core SPNDs and set of flux modes.

6 Results and Discussion The nominal configuration of AHWR refers to all eight regulating rods (RRs) 67% IN, all 8 absorber rods (ARs) fully IN and all 8 shim rods (SRs) fully OUT and all SORs being fully OUT. The fundamental and ‘k’ higher modes at all points in the reactor are generated for this reference configuration. Similarly, the developed FMA is applied for various reactivity device configurations, and the variation of eigenmodes and in-core detectors (320 and 168 detectors [2]) are studied. It can be inferred from Figs. 3 and 4 that with increase in number of eigenmodes added to the FMA, the RMS error decreases across all methods. MFSM and IMFSM give better results compared to FSM. Also, it is seen that the range of RMS error remains almost the same with reduced number of in-core SPNDs. It is always preferred to have minimum number of in-core SPNDs to accurately estimate the three-dimensional flux map to save the computational and economic burden. Further, investigation is in progress to conclude on number of eigenmodes and number of in-core detectors to be used in FMS by studying various operational scenarios and transient cases. Figure 5 shows the reconstructed flux at midplane of the reactor for the case of one RR taken fully OUT.

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Fig. 3 Variation of RMS error for different methods with number of ‘k’ eigenmodes considering 320 in-core SPNDs for the test case of one RR taken fully OUT. Here IMFS (1) represents the addition of 1st harmonic to FMA along with fundamental and IMFS (2) represents the addition of 1st and 2nd harmonics to FMA along with fundamental

Fig. 4 Variation of RMS error for different methods with number of ‘k’ eigenmodes considering 168 in-core SPNDs for the test case of one RR taken fully OUT. Here IMFS (1) represents the addition of 1st harmonic to FMA along with fundamental and IMFS (2) represents the addition of 1st and 2nd harmonics to FMA along with fundamental

7 Conclusion The flux mapping system based on eigenmodes can provide information on the detailed power distribution of the reactor. In this paper, subspace iteration technique is applied to generate multiple higher eigenmodes. Several higher eigenmodes can be generated simultaneously by using this technique. Using this advantage, MFSM has been further extended as IMFSM by the addition of fundamental and few higher eigenmodes of snapshot configuration to the set of reference flux modes. A comparative study on various computational schemes for the FMS has been done for AHWR. It is observed that the MFSM and IMFSM provide better results compared to FSM.

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Fig. 5 Reconstructed flux at midplane for the test case of one RR taken fully OUT

The applicability of IMFSM method to FMA has to be studied extensively for various operational configurations. A study on variation of number of in-core SPNDs and set of flux modes have also been carried out. Further tests have to be performed for various reactor configurations and transient situations to conclude on number of modes and number of in-core SPNDs to be used in AHWR.

References 1. Sinha, R.K., Kakodkar, A.: Design and development of AHWR—the Indian thorium fuelled innovative reactor. Nucl. Eng. Des. 236, 683–700 (2006) 2. Naskar, M., Verma, Y., Tiwari, A.P.: Selection of optimum set of modes for flux mapping in AHWR with flux synthesis method. In: Proceedings of National Conference on Virtual Intelligence and Instrumentation (NCVII), 6A.4, BITS Pilani, Rajasthan (2009) 3. Mishra, S., Modak, R.S., Ganesan, S.: Computational schemes for online flux mapping system in a large-sized pressurized heavy water reactor. Nucl. Sci. Eng. 170, 280–289 (2012) 4. Modak, R.S., Jain, V.K.: Subspace iteration scheme for the evaluation of lambda modes of finite-differenced multi-group neutron diffusion equations. Ann. Nucl. Energy 23(3), 229–237 (1996) 5. Thakur, A., Singh, B., Gupta, A., Duggal, V., Bhatt, K., Krishnani, P.D.: Performance of Estimation of distribution algorithm for initial core loading optimization of AHWR-LEU. Annals Nucl. Energy 96, 230–241 (2016) 6. Ananthoju, R., Tiwari, A.P., Belur, M.N.: A two-time-scale approach for discrete-time kalman filter design and application to AHWR Flux Mapping. IEEE Trans. Nucl. Sci. 63(1), 359–370 (2016) 7. Arvind, K., et al.: Safety analysis report—preliminary physics chapter. RPDD/AHWR/130/2008. Rev 1, Dated Nov 10 (2009)

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8. Gupta, A.: A 3D space-time analysis code in cartesian and hexagonal geometries. In: 19th National symposium on radiation physics (NSRP-19), Mamallapuram, TN, India (2012) 9. Ezure, H.: Estimation of most probable power distribution in BWRs by least squares method using in-core measurements. Nucl. Sci. Technol. 25(9), 731–740 (1988)

ESCO Model for Energy-Efficient Pump Installation Scheme: A Case Study Saurabh Khobaragade, Priyanka Bhosale, and Priya Jadhav

1 Introduction Several studies and projects have indicated that ~30% energy savings are costeffectively possible in energized irrigation, through the use of energy-efficient pumpsets and other improvements in the pumping system [1–4]. An analysis of the various technical approaches, challenges, and cost-benefits has been covered in the literature. In this work, we consider the implications of a third-party or ESCO (Energy Service Company), implementing an energy-efficient pumps upgrade and thereafter maintaining the systems too. This is interesting because several of the problems arise due to farmer behavior, and the project’s efficiency gains may be affected by this over time. We investigated such a project conducted in Solapur district in Maharashtra state from 2010 to 2017. The project implementation agency CRI pumps replaced 2209 existing pumps with energy-efficient pumps and were expected to maintain them for 5 years. The payments to the company were expected to be covered by the resultant energy savings. The project was carried out on five agricultural feeders, four in Mangalvedha block and one in Pandharpur block of Solapur. Our study was carried out in Mangalvedha. The following data was used for analysis: first-hand surveys of 22 beneficiaries; consumption and load data of the feeders collected at the substation; data collected from the local implementing pump repair shop; four Monitoring and Verification reports, prepared by the Monitoring and Verification Agency, immediately after installation, 1 year later, 2 years later, and the final one in February 2018; and first-hand observation of the final set of efficiency measurements while they were being conducted by the Project Implementing Agency. We investigated the following factors affecting the efficacy of the scheme: financial feasibility, energy savings, efficiency of pumpsets, effect of voltage, and farmers’ behavior. S. Khobaragade · P. Bhosale · P. Jadhav (B) Centre for Technology Alternatives for Rural Areas, IIT Bombay, Mumbai, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_35

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The paper is divided into the following sections: (1) Introduction (2) Description of the scheme and the geographical area of implementation (3) Analysis of factors affecting the efficacy of the scheme (4) Discussion and conclusion.

2 Description of the Scheme The scheme was launched in 2009 [5]. 2209 old inefficient pumps were replaced with 4 and 5 star rated pumps, over a period of 2 years, on four agricultural feeders in Mangalwedha block: Bhose, Borale, Nandeshwar, Brahmapuri feeders, and one in Pandharpur block: Kharatwadi feeder. The installation, and 5 years of maintenance, was provided free to the farmers who agreed to sign up for the scheme. Maharashtra State Electricity Distribution Company Limited (MSEDCL) played the role of facilitating agency for this project. CRI pumps were the Project Implementing Agency (PIA) which carried out all the work with the help of their authorized local distributor. MITCON Consultancy and Engineering Services Ltd. is the third-party Monitoring &Verification agency appointed by Bureau of Energy Efficiency (BEE) and supervising the Ag DSM project.

2.1 Geographical Location District has dry (arid and semiarid) climate, and most of it, including Mangalvedha block, falls in the Scarcity (Rainfall) Zone. Jowar is a major crop in the district as well as the block, and sugarcane is taken depending upon water availability and dominant in northern region of Mangalwedha along the river side. The average annual rainfall in the block is about 510 mm according to 2011 figure. The average annual fluctuation in groundwater is about 3 m with pre-monsoon average level being 7.85 mbgl and post-monsoon being 4.22 mbgl [6–8]. Fig. 1 shows the location of the district, block and the relevant villages. The feeder in Pandharpur block has not been considered in this study. The irrigation sources in the area are borewells, dugwells, and river water. The scheme covered 805 monoblock pumps (dugwells and river), 640 dugwell pumps, and 764 submersible pumps.

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Fig. 1 Pilot project map—Mangalwedha [9]

3 Analysis of the Factors Affecting Efficacy of the Scheme 3.1 Energy Savings Ideally, energy meters should be used to measure the change in energy consumption after installation of new pumps. For the purposes of this scheme, meters were installed on all pumps, but farmers bypassed meters, or they stopped working. The last point of energy consumption is at the feeder level in the substation. Feeder energy consumption data is recorded in a register at half-hourly intervals at all substations in Maharashtra. We have obtained the data for Borale feeder from the substation for 2016. We have also considered the data for 2006–07 and 2008–09 from the Detailed Project Report of the scheme (DPR). 672 pumps, or 30% of the total number of pumps replaced on all five feeders, are on Borale feeder, out of a total of 682 connections on the feeder. Hence, the Borale feeder data is a good representation. The energy consumption per pump for these three years data is given in Sect. 3.2. In this section, we consider hours of pumping obtained through various methods. A survey of 22 beneficiaries, indicated an average of 1136 h of pumping annually based on memory. Four of these farmers cultivated sugarcane, two had sugarcane, and rabi jowar, and the others all cultivated. The rabbi jowar farmers had an average irrigated area of 5.4 ac with 639 h of pumping. Famers who grew sugarcane were closer to the riverbanks, with an average irrigated area of 9.8 ac, and 2010 h of pumping. This is commensurate with values obtained from other methods as shown in Table 1. Also, according to the farmers, there hasn’t been any change in their hours of pumping with the new pumps. We also tried to estimate the energy consumption based on the water requirement of crops grown in the area, depth of the water sources, and average landholdings. We assume a landholding of 6 ac (average in the region), water table of 16 m, and a pumpset efficiency of 25%, and cropping of 70% area for jowar and 30% for

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Table 1 Annual hours of pumping estimated through different means

Scenario

Annual hrs of pumping

Feeder energy (Borale feeder), substation data

1164

From DPR, substation data

1154

From survey of 22 farmers

1136

sugarcane. The cropping pattern is based on the cropping pattern acquired from the Krishi office of the block. Based on this average configuration, we estimate annual energy consumption of 2638 kWh. There is a fair bit of variation in these estimates, and ideally, energy savings should be based on meters installed on farmers’ pumps.

3.2 Financial Feasibility The proposed project budget for implementation was Rs. 442 lakhs which increased to about Rs. 839 lakh as existing accessories like piping, wiring, panels, etc. were in poor shape and needed to be replaced to maximize the performance of the star rated pumps. An additional Rs. 141 lakh per year was given by MSEDCL to the PIA for operation and maintenance from a special fund called Load Management Charges (LMC). One local pump repair shop in Mangalvedha was designated for all repairs under the maintenance contract. Local mechanics brought in all pumps under this scheme for repair to this shop. Maintenance costs based on information from this shop for pumps being brought in for repair over 2 months, were estimated at an average of Rs. 2218 per repair, and an estimated 0.275 breakdowns per pump per year. Table 2 shows the estimated energy savings based on three different years’ baseline energy consumption data for Borale feeder. The first two years of data was obtained from the Detailed Project Report for the project [9]. The third year’s data, 2016, was Table 2 Estimated energy consumption before replacement for three different years, and the calculated internal rate of return under various conditions Energy consumption without energy-efficient pumps (kWh)

No reduction in efficiency

5% reduction in efficiency/year

Annual Average Hours of pumping

IRR for 10 years project period (%)

IRR for 5 years project period (%)

IRR for 10 years project period (%)

IRR for 5 years project period (%)

2006–07

9195

48

39

38

31

1164

2008–09

7529

38

28

28

20

953

2016

5410

24

11

13

3

807

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obtained directly from the substation where energy consumption data is recorded hourly. The energy consumption without the efficient pumps upgrade for 2016 is adjusted for the fact that the energy-efficient pumps are already in operation. We’ve considered all 3 years in this analysis to show the sensitivity of the financial feasibility of the project to the initial energy consumption or pump usage. Annual energy consumption per pump Feeder energy consumption(1 − loss on feeder) = Number of pumps on feeder The per pump energy consumption was calculated assuming a total feeder loss of 15%, the overall AT&C loss in Maharashtra. Usually, rural feeders are expected to be more lossy, hence this is an optimistic IRR calculation. The savings were calculated based on energy efficiency measurements before and after pump replacement. The energy has been estimated assuming that the total energy output remains the same. The other way to estimate energy savings is to assume that the hours of pumping do not change, and the reduction in power consumption per pump is what makes a difference. But since this is a region with frequent water scarcity, we assume that farmers are limited by the amount of water and not the hours that electricity is available. Unfortunately, agricultural loads are rarely metered reliably, if at all, hence these estimates are used. The IRR calculation has been done using project periods of 5 years and 10 years. This pilot project had a five-year project period, but the pumps could have a lifetime of up to 10 years, and hence we have also done the same calculation for such a project period. We have considered the capital costs incurred by the PIA, and we have also considered the estimated maintenance cost based on the frequency of breakdowns observed, and not the amount given by MSEDCL to the PIA from the LMC fund. We have considered the cost per unit of energy saved to be Rs. 5.51, reached by considering an average cost of supply of Rs. 6 per unit, an average billing rate of Rs. 2.40 per unit, [10] and an estimated collection rate of 20%. We see that for a project period of 5 years, it is barely a good project even for the highest consumption figure which is for 2006–07. For a project period of 10 years, the higher consumption figures are remunerative but the 2016 year consumption data does not indicate a good enough payback for a private company as PIA. In the actual project monetary benefits from estimated energy savings were shared between CRI & MSEDCL on 30:70 ratios, respectively, considering a saving of Rs. 2.70 per unit. Energy savings were then estimated based on the average hours of pumping, which were taken as 1640 h, which is very high considering any of the estimates found in Sect. 3.1. In addition, the company was given a flat fee of Rs. 141 lakhs per year for maintenance. Hence the model has not been tested since the payments to the PIA are not being made on the basis of true energy savings, but projected energy savings which are unrealistic, and additional maintenance charges.

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3.3 Efficiency of Pumpsets Energy efficiency measurements were carried out on all old pumps that were replaced, and all new pumps after installation. Thereafter 10% of the pumps were selected for efficiency measurement each year. Given below is the average efficiency data for the four feeders taken just before and after replacement (Table 3). The change in efficiency for Borale feeder pumps has been used in Sect. 3.1. Bhose and Nandeshwar have similar efficiencies before and after replacement. Brahmapuri has a smaller change in efficiency. There is also the question of how efficiency changes over time, and with breakdowns and repairs. TERI examined electric pumpsets several months after rectification and found an average fall in efficiency of approximately 5% [2]. Under this scheme 30+type of pumpset models were replaced. Openwell 5HP pumpsets are widely used by the farmers in the Mangalwedha area. As it is more accurate to measure head in dug well over borewell, a single pumpset model of category (CRI CSM-3S(5)) is selected from the available data for analysis. We have looked at the efficiency decline in a set of submersible pumpsets in dugwells through the Monitoring and Verification data collected by the PIA. The efficiency measurements were conducted by taking three sets of instantaneous power consumption and flow rate readings. Head was estimated based on water depth and piping system dimensions. The actual water depth in borewell is hard to estimate accurately, hence for our analysis, we decided to only consider dugwell models. In addition, we found that there was much incongruity in the M&V data, because some pumps showed unrealistically high-efficiency numbers, and for some the total head was greater than the cut-off head of the model. Hence, we analyzed the data for just one model, the CRI CSM-3S(5), whose characteristics we could acquire. And within this model, only those pumps were considered with flow rate- head combinations which were within the vendor stated performance characteristic of the model, i.e., the output hydraulic power at a certain head was not more than what was expected. We also left out pumps with efficiency measurements conducted at lower than the operating voltage in the vendor specifications, or pumps that had been repaired. These factors are likely to affect the efficiency adversely, but we wanted to normalize these factors as much as possible to get some minimum level of efficiency reduction.

Table 3 Average efficiency and power consumption measurements collected by CRI pumps [5] Number of pumps replaced

Bhose

Borale

Brahmapuri

Nandeshwar

339

672

266

500

Old avg eff (%)

22.5

22.9

20.5

21.9

Old avg load per pump (kW)

6.39

5.83

7.29

7.16

New avg eff (%)

40.7

41.5

33.6

40.0

New avg load per pump (kW)

4.75

4.37

5.52

5.24

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Table 4 Average variation in efficiency of 13 different pumpsets Approx. age of pumpset (days)

Average efficiency—variation with time 403–410

776–808

1326–1374

No. of IPS considered

5

5

3

Avg. baseline operating eff. (%)

37.3

35

45.6

Standard deviation (baseline eff.)

6.3

10.2

8.7

Average new efficiency at M&V

34.8

29

35.6

Standard deviation (new eff.)

5.8

9.7

8.0

Avg. drop in eff. per year (%)

6.03

7.43

5.95

Table 4 has the resultant pumps’ data, in three different groupings, with the number of days after installation when the measurement was conducted was spaced by about 1, 2, and 4 years. Another factor that would affect efficiency calculations is the operating head. This will vary over time. The vendor specified operating head for the model is 20–35 m, 40 m is the cut-off head. At M&V the total heads—static+dynamic for these pumps varies between 20 and 40 m. The average annual fluctuation in groundwater is about 3 m, hence that should not affect the efficiency significantly [6–8]. Since this is such a small dataset of 13 pumps, and there is so much ambiguity in the M&V data, we cannot depend on the efficiency measurements or calculations by M&V. Sensitivity of efficiency to pipe lengths Another source of ambiguity in the efficiency arose from the pipe lengths and tapping point error in the efficiency measurements. Some of the fields, are quite long and the delivery pipe has several openings or chambers—see Fig. 2. It is not clear if the efficiency measurements have been carried out at the same outlet each time. For instance, in one configuration, with a static head of 21 m, chambers existed between a distance of 10–392 m from the well. A measured flow rate of 7 l/s, input power of 4.7 kW, resulted in calculated efficiency varying between 47 and 77%.

Fig. 2 Schematic showing outlets along the length of a pipe

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Table 5 Sample voltage measurements Voltage

No of pumps

V > 240

1

200 < V < 220

11

180 < V < 200

8

V < 150

1

Table 6 Average sanctioned HP versus connected HP for replaced pumps on the four feeders [5] Feeder

No. of pumps replaced

Sanctioned HP

Old Pump HP

New Pump HP

Bhose

339

4.4

5.1

4.6

Borale

672

4.2

4.9

4.4

Brahmapuri

266

5.9

6

5.8

Nandeshwar

500

4.7

5.9

5.2

3.4 Effect of Voltage Voltage levels were low but were very low only at the tail end of the feeder in Bhose village which had low voltage issues in general. Table 5 has sample measurements taken at 21 beneficiaries’ pumpsets. According to the M&V reports, the pumps being used before the replacement were oversized, and farmers did not allow downsizing of pumps even if the flow rate matched their old pumps. Hence the system was overloaded, resulting in lower voltages. Besides resulting in ineffective operation of the pumps, this would affect the efficiencies adversely overall. In addition to causing deterioration in the pumps. Table 6 shows that the average HP of the replaced pumps is greater than the sanctioned HP on three out of four feeders, and barely less than, on the fourth feeder.

3.5 Effect of Farmer Behavior 33 farmers and 30 mechanics were interviewed to find out about farmer behavior and causes for pumps failures. The reasons for the problems and can be categorized into three main categories. Figure 3a shows how much each category of problem accounts for the breakdown. Some of the farmer misuse of technology are as follows: Increasing the relay setting on the starter, using wire with higher current capacity for fuse, putting a mechanical obstruction so that the starter does not trip—all of these allow the motor to fail at low voltages, thus resulting in large currents through the motor. 50% of the surveyed 33 farmers, had bypassed the capacitors installed. This alone could have reduced the current significantly, possibly leading to better voltages. Some of the

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Fig. 3 Categorization of all the problems

connections were loose. The pumps were moved—for instance when there was not enough water in the well, a farmer might move the pump to the river. This new head may not correspond to the pump characteristics and so that the pump runs at a low efficiency. The technical problems mainly include the wear and tear of the equipment due to usage. The scheme did not have any provision for regular maintenance, and farmers did not do it either. The supply-side problems are low and/or unbalanced voltage, and it is possible that these could also be aggravated or caused by farmers’ usage behavior. Figure 3b shows farmers’ behavior towards maintenance of pumping system, observed through survey.

4 Discussion and Conclusion The model of this pilot project wherein the PIA installs as well as maintains the pump and gets paid on the basis of energy savings if successfully implemented, could get around all the problems that encumber Agricultural Demand Side Management initiatives. Unfortunately, in this pilot project, the payments to the PIA are not based on realistic energy savings. The two main reasons being, error in efficiency measurements (verified with data analysis), and consideration of inappropriate hours of pumping. The estimated energy savings were based on 1640 h of pumping annually, an approximate reduction of load of 1.2 kW per pump, a savings of Rs. 2.70 per unit of energy saved, divided up in a ratio of 30:70 between the PIA and MSEDCL achieving breakeven at end of 5th year, resulting in an IRR of 2%. The additional benefit to the PIA not considered here is the sale and brand recognition through the scheme. From our calculations, we get an IRR of 39–11% based on 1164–807 h of pumping, which reduces to 31–3% considering a reduction in operational efficiency of 5% annually.

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The average efficiency of EEPS was found to be 32.34% (n = 21, SD = 13.6%), at the end of 5 years, which is less than the targeted value of 34.5%. But the baseline efficiency was 22% (from M&V report, SD not available), hence energy savings are going to continue for a few more years after the project period resulting in a gain for the utility. Unfortunately, there is no reliable account of the energy savings actually achieved through this scheme. Hence this pilot project is not implemented in a way that seriously tests the ESCO model. But the analysis of this data, and field conditions, throw up several questions about a rational design for an ESCO model. For instance, seemingly, the efficiency difference between an old and a new pump, and the energy consumption or hours of pumping should be enough to calculate the energy savings due to the replacement. However, efficiency is not easy to calculate in the case of borewells, since head measurement is hard. Also, many conditions that affect the operational efficiency of the pump vary in the field, such as voltage and water table level. Drought conditions, as seen in this case, will reduce hours of pumping and hence energy savings. More useful than measuring instantaneous power consumption and flow rate measurement would be a measurement of water and energy consumption over a period of a year, of a sample set of pumps. This would give the utility as well as the ESCO the required information to make a decision that leads to optimum performance and gains for both parties. This measurement should be carried out after replacement too, to determine the energy savings that have occurred due to efficiency improvement. Many effects of farmer behavior, like oversized pumps or removal of capacitors, are felt at the low tensions network level. Hence, an ESCO model at the Distribution Transformer level may also be a good possibility. There may be more uniformity in water and energy consumption, total energy consumption is easily measured at the DT, and farmers connected to a DT can be sensitized to using the right sized pump, and to following certain practices like use of capacitors, regular maintenance, and appropriate current limiting devices to ensure any energy-efficient pumps to function at their maximum potential thus improving the probability of success of such schemes. Also, smaller interventions may provide opportunities for rural entrepreneurs.

References 1. CORE: Best practices for agricultural pumpsets and rural Demand Side Management (DSM). Core International, Inc (2005) 2. Reidhead, W.: Achieving agricultural pumpset efficiency in rural India. J. Int. Dev. 13(2), 135–151 (2001) 3. Planning commission: report of high level panel on financial position of distribution utilities. Planning Commission, Government of India (2011) 4. WB: India: Power supply to: agriculture, Report No. 22171-IN. World Bank (2001) 5. MITCON: Monitoring and verification report for 4th year. AgDSM Pilot project in Solapur Circle, BEE (2017) 6. CGWB: Report on ground water information Solapur District Maharashtra. Central Groundwater Board, Government of India (2013)

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7. Feedback ventures: Report on estimation and segregation of distribution loss in Solapur circle electricity distribution network. MERC (2010) 8. GSDA: A report on dynamic ground water resources of Maharashtra 2011–12. Groundwater surveys and development agency Pune, Government of India (2012) 9. DPR: pilot AgDSM project at Solapur Maharashtra, detailed project report. Bureau of energy efficiency (2009) 10. MERC: Order, Maharashtra electricity regulatory commission, Case No.121 of 2014

Transient Numerical Model for Natural Convection Flow in Flat Plate Solar Collector Nagesh B. Balam , Tabish Alam , and Akhilesh Gupta

Nomenclature Ar Bi Cp g Gr h H L K K n N Nu p Pr q” Q R Ra S t T

Aspect ratio (L/H) Biot number Specific heat capacity Gravitational force Grashof number Convective heat transfer coefficient Height of respective domain Length of enclosure Thermal conductivity Diffusion coefficient Time step Number of tubes Nusselt number Pressure Prandtl number Heat flux (W/m2 ) Non-dimensional Heat flux Residual Rayleigh number Transport equation source term Time Temperature

N. B. Balam (B) · T. Alam CSIR-Central Building Research Institute, Roorkee 247667, India e-mail: [email protected] A. Gupta Indian Institute of Technology Roorkee, Roorkee 247667, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_36

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u u, v U U, V x, y X, Y α β γ ε θ ν ρ τ ψ ω   ∇2 

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Velocity vector Velocity components Non-dimensional velocity Non-dimensional velocity components Coordinates Non-dimensional coordinates Thermal diffusivity Thermal expansion coefficient Angle of inclination of enclosure Numerical tolerance limit Non-dimensional temperature Kinematic viscosity Density Non-dimensional time Stream function Non-dimensional stream function Vorticity Non-dimensional vorticity General conservation variable Laplacian operator Order of discretization

Subscripts/Superscripts o amb f fp fg g p S t w

Reference condition Ambient Fluid Fluid to plate Fluid to glass Glass Plate Solar Tube Water

1 Introduction Natural convection losses in flat plate solar collectors (FPSC) are the major heat losses constituting above 70% of the overall heat losses in the solar collector. So, natural convection flow optimization offers a maximum potential to reduce the heat

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losses and improve the overall efficiency of the FPSC. Several numerical models have been developed to describe overall heat transfer process in the solar collectors. Duffie and Beckman [1] developed a one-dimensional model to evaluate the overall heat transfer coefficient and collector efficiency as a function of fluid inlet temperature to characterize the steady-state performance of FPSC. However, the requirement of steady-state conditions makes experimental comparison tests more complicated, inaccurate and expensive. The behaviour of FPSC can be more accurately characterized by its transient response to input parameters such as solar radiation intensity, weather variation, shadow factors, inlet fluid temperature which are transient in nature. Lumped-capacitance models are developed to model the transient behaviour of FPSC [2, 3]. Lumped capacitance assumes there are no significant temperature spatial gradients along the spatial dimensions of the solid body. A 1-node model was first attempted by close [4] by developing an energy balance equation at a node on the absorber surface. The absorber plate, collector fluid temperature, and cover temperatures are assumed uniform and equal making the study completely steady state at each time step. Further, Klien [5] developed a 2-node model by creating two nodes at absorber plate and cover glass. The lumped capacitance of the absorber plate and collector fluid is differentiated from the lumped capacitance of the glass covers. Thus, two energy balance equations at absorber plate and glass cover are developed. Assumptions in the study include a perfect thermal coupling between absorber plate and collector fluid giving a uniform temperature. A three-node model is developed by Morrison and Ranatunga [6] separating the thermal capacitance of the absorber plate and collector fluid resulting in a three equation model. It is assumed that the temperature gradient from the inlet to outlet of the tube is varying linearly. Later multinode models were developed that contain multiple nodes in the collector fluid regime, absorber plate and glass covers. All of these models assumed evenly distributed incident solar radiation, negligible edge effects and negligible receiver conductivity. One of the major disadvantages of these lumped-capacitance models is neglecting the distribution of temperature gradients in the annulus air gap between absorber plate and solar collector. Natural convection in the air gap is taken into account by empirical expressions only [7] without actually simulating the fluid flow behaviour. But, as pointed out previously more than 70% of the overall heat loss occurs through natural convection between absorber plate and ambient air via the glass cover. So modelling the fluid flow phenomenon inside the annular air gap is necessary to optimize the natural convection losses. The partial differential equations that arise in modelling this fluid behaviour are governed by Navier–Stokes equations in addition to energy conservation equation. These are nonlinear in nature and highly computer-intensive. So very few attempts have been carried out to rigorously model the temperature distribution in the annulus air gap. Recently with the advances of digital computation techniques, these studies have become possible. Steady-state simulations of 2D and 3D FPSC models to simulate the fluid flow behaviour are studied by fluent and other commercial softwares [8–16]. These studies have primarily focussed on various structural and operating parameter optimization to increase the FPSC efficiency.

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A transient model that characterizes the natural convection flow behaviour within the annulus gap of the FPSC is not available in the literature. Here we develop a 2D model to optimize various structural and operating parameters.

2 Governing Equations 2.1 Assumption (i) (ii) (iii) (iv) (v)

The flow and temperature changes are uniform along Z-direction limiting the flow to 2D. Since the flow is 2D, the temperature of heat transfer fluid in the pipe is constant. Radiation heat transfer is neglected and, however, could be easily be incorporated in the present model. Fluid and material properties are evaluated as a function of constant temperature throughout the transient simulation. Side and bottom heat transfer losses are neglected by considering perfectly insulated.

FPSC is divided into three zones as shown in Fig. 1. The plate domain (p) which designates the absorber plate section of the FPSC, the fluid domain (f ) which is filled

Fig. 1 Schematic diagram of FPSC with boundary conditions

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375

with air and the glass domain (g). The governing equations which define the heat transfer behaviour in each section are presented below.

2.2 Governing Equations The governing equations are non-dimensionalized using the following dimensionless quantities. y L αt ωH 2 x ; Y = ; AR = ; τ = 2 ;  = ; H H H H α gβq Smax H 4 ψ ν ∂ ∂ = ; Pr = ; Ra = ;U = + ;V = − ; α α αν K ∂Y ∂X  2  ∂ ∂ 2 (T − Tamb )   ; Ra = Gr ∗ Pr; =− ;θ =  + ∂ X2 ∂Y 2 q Smax H K q  K1 hH − → ; Q S =  S ; r K 12 = ; U = (U, V ); Nu = Bi = K q Smax K2 X=

By applying the above-defined non-dimensional quantities, the governing Eqs. 15 and its associated BC’s are defined. For the sake of clarity, the suffixes are dropped in the governing equations as shown in Eq. 1.

2.3 Plate Domain (P) ∂ 2θ p ∂θ p ∂ 2θ p ∂ 2θ ∂θ ∂ 2θ = = + ⇔ + ∂τ ∂ X2 ∂Y 2 ∂τ ∂ X2 ∂Y 2 Boundary conditions: Plate bottom ∂θ = 0, ∀(X, Y ) ∈ (0 → L − n Dt , 0) ∂Y Tube ∂θ = −Bi t (θ − θw ), ∀(X, Y ) ∈ (N Dt , 0) ∂Y Plate sides     ∂θ = 0, ∀(X, Y ) ∈ 0, 0 → H p and L , 0 → H p ∂X

(1)

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Plate top   ∂θ ∂θ = −r K f p + Q S , ∀(X, Y ) ∈ 0 → L , H p ∂Y ∂Y

2.4 Fluid Domain (F) ∇ 2 = −

 2  ∂θ ∂ − ∂θ →  + U .∇  = Pr ∇  + Ra . Pr + cos γ . − sin γ . ∂τ ∂x ∂y   ∂θ − → + U .∇ θ = ∇ 2 θ ∂τ

(2) (3) (4)

Boundary conditions: Fluid bottom   θ f = θ p , U , V , = 0∀(X, Y ) ∈ 0 → L , H p Fluid sides     ∂θ   , U , V , = 0, ∀(X, Y ) ∈ 0, H p → H f and L , H p → H f ∂X Fluid top θ f = θg , U , V , Ψ = 0 ∀(X, Y ) ∈ (0 → L , H f )

2.5 Glass Domain ∂ 2θ ∂θ ∂ 2θ = + ; 2 ∂τ ∂X ∂Y 2 Boundary conditions:   ∂θ ∂θ Glass bottom ∂Y = −r K f g ∂Y , ∀(X, Y ) ∈ 0 → L , H f     Glass sides ∂∂θX = 0, ∀(X, Y ) ∈ 0, H f → Hg and L , H f → Hg   ∂θ Glass top ∂Y = −Biamb (θ − θamb ), ∀(X, Y ) ∈ 0 → L , Hg

(5)

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3 Numerical Implementation 3.1 Algorithm Step 1: Initialize all the dependant variables θ p , θ f , θg , U , V , ,  at time step ‘n’. Step 2: Update the dependant variables to time step ‘n + 1’. Plate domain: Evaluate θ pn+1 including boundaries fluid domain: Evaluate θ n+1 , n+1 excluding boundaries f f glass domain: Evaluate θgn+1 including boundaries Step 3: Evaluate boundary conditions for θ n+1 , n+1 at time step ‘n + 1’. f f Step 4: Repeat steps 2 and 3 until the solutions are converged

3.2 Validation Two case studies are selected to validate the model developed to simulate the natural convection flow, Case1: Differentially heated square cavity described in Vahl Davis et al., [17] Case 2: Top heat loss coefficient in solar collector by Samdarshi et al., Subiantaro et al. [18, 19], Case1: The results for simulation of natural convection flow in a square domain of case described in Vahl Davis et al. are compared in Table 1. We present the results for Rayleigh numbers in the range of 103 –106 . The quantities considered here are the maximum horizontal velocity u max on the vertical midplane Table 1 Comparision of natural convection flow results in square domain with benchmark results of Davis et al. [17] Ra

Reference

103

Benchmark Present study

104

vmax

max

mid

Nu y

3.649

3.697

–

1.174

1.117 1.119

3.635

3.692

1.180

1.180

0.3

0.1

–

0.5

0.1

Benchmark

16.17

19.61

–

5.071

2.238 2.260

41 × 41

16.10

19.48

5.080

5.080

|%| Difference

0.4

0.6

–

0.1

1

Benchmark

34.73

68.59

9.612

9.111

4.509 4.645

Present study 106

41 × 41

umax

|%| Difference Present study 105

Mesh

41 × 41

34.44

66.81

9.675

9.153

|%| Difference

0.84

2.6

0.66

0.46

3.0

Benchmark

64.63

219.36

16.750

16.32

8.817

64.70

215.42

17.064

16.62

9.184

0.1

1.8

1.87

1.84

4.16

Present study |%| Difference

61 × 61

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Table 2 Top heat loss coefficient comparison (U t : W/m2 /K) with Samdarshi et al. [18] Rayleigh no.

1

100

1000

2500

Samdarshi et al.

13

6.5

5.5

5.4

Present study

11.5

6.0

5.3

5.2

|%| Difference

13

8.3

3.8

3.85

of the cavity, the maximum vertical velocity vmax on the horizontal midplane, the maximum absolute value of the stream function |ψ|max , the absolute value of the stream function at the midpoint of the cavity |ψmid |, the average Nusselt number Nu0 on the hot wall, and the maximum and minimum values Numax and Numin of the local Nusselt number on the hot wall. Case 2: Samdarshi and Mullick have proposed an empirical correlation to evaluate the top heat loss coefficient of a solar collector. We compare the evaluated heat transfer coefficient from the present model with Samdarshi et al. [18] to validate the present model (Table 2).

4 Results The transient simulation results are presented for the following non-dimensional parameters. Since the flow is symmetric along the X-axis direction, we simulate the flow using 2 pipes only, but the study can be extended to any number of pipes. For the above-defined non-dimensional parameters, the simulations were carried out using finite difference programming in MATLAB. The isotherm contours are shown in Figs. 2 and 3. Initial conditions of the transient simulation were all the temperature profiles in three domains which are equal to ambient temperature and a

Fig. 2 Isothermal contours of absorber plate, annulus gap and glass cover at time 0.5 s (top), 1.5 s (middle) and 2.5 s (bottom)

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Fig. 3 Isothermal contours of absorber plate, annulus gap and glass cover at time 3.5 s (top), and 4.5 s (bottom)

solar radiation intensity of 500 W/m2 that corresponds to QS = 0.5 is given as the initial condition. It can be observed from Figs. 2 and 3 that the copper plate temperature reaches to a maximum non-dimensional temperature of 20 with time. As expected, the temperature is maximum in the copper plate as seen in Fig. 3 at the mid-portion between both the fluid tubes. Initially, at time = 0.5 s heat flow starts raising up at 4 different locations. As the steady state is reached the central contours collapse into single strong rotating contour with bulk of the heat transfer happening at this location. By varying various non-dimensional parameters in the given model, it is possible to predict the time required to reach the steady-state limits in all the three regions. One can observe many interesting phenomena that are occurring in the annulus gap of FPSC which are not possible in either lumped-capacitance modelling or steady-state simulations.

5 Conclusion A transient 2D numerical model is developed to simulate the natural convection flow in the annulus gap of the solar collector. The model is validated against the standard results available in the literature. It has been found that the present model could simulate the steady-state response of the solar collector within 13% error limit when compared with Samdarshi et. al. [18] The error could be further reduced by including the radiation heat transfer effects also. The developed model is tested for a sample problem with parameters discussed in Table 3. With this model, the transient behaviour of the FPSC can be estimated, time taken for the flow to achieve steady state in each of the domains. The study can be further extended to double and triple glazing as well. A variety of parameters can be varied with the present study and its influence on the overall performance of the solar collector. Parameters such as the effect of tube water temperature, bond conductance, heat transfer coefficient of tube and glass, solar radiation intensity, plate, tube and glass conductivity, plate and tube wall thickness, tube diameter, no of tubes, gap between the tubes, glass thickness,

380 Table 3 Non-dimensional (ND) parameters of FPSC for simulation

N. B. Balam et al. ND parameter

Value

Collector length (L/H)

8

Annulus air gap (H f –H p )/H

1

Absorber plate width (H p /H)

0.1

Cover glass width (H g– H f )/H

0.1

ND tube diameter (DT /H)

1.2

Number of tubes (N)

2

Rayleigh number (Ra)

105

Prandtl number of fluid (Pr)

0.71

ND solar heat flux (QS )

0.5

ND HTF temperature (θ w )

0.0

ND ambient temperature (θ amb )

0.0

Biot number of glass (Biglass )

10

Biot number of tube (Bitube )

7.5

annulus air gap, overall aspect ratio of FPSC, wind heat transfer coefficient, ambient air temperature, fluid and material thermal properties. Presently, the model is limited to convection heat transfer only, but it can be easily extended to include radiation heat transfer by modifying the governing equations to include radiation heat transfer also. The water temperature is considered constant which also can be assumed varying across the cross section of the pipe. Side and bottom heat losses can also be included in the present simulation by modifying the boundary conditions.

References 1. Duffie, J.A., Beckman, W.A.: Solar engineering of thermal processes. Wiley, Hoboken, NJ (2013) 2. Smith, J.G.: Comparison of transient models for flat-plates and trough concentrators. J. Sol.Energy Eng. 108(4), 341 (1986). https://doi.org/10.1115/1.3268117 3. Tagliafico, L.A., Scarpa, F., De Rosa, M.: Dynamic thermal models and CFD analysis for flat-plate thermal solar collectors—a review. Renew. Sustain. Energy Rev. 30, 526–537 (2014). https://doi.org/10.1016/j.rser.2013.10.023 4. Close, D.: A design approach for solar processes. Sol. Energy 11(2), 112–122 (1967). https:// doi.org/10.1016/0038-092x(67)90051-5 5. Klein, S.: Calculation of flat-plate collector loss coefficients. Sol. Energy 17(1), 79–80 (1975). https://doi.org/10.1016/0038-092x(75)90020-1 6. Morrison, G., Ranatunga, D.: Transient response of thermosyphon solar collectors. Sol. Energy 24(1), 55–61 (1980). https://doi.org/10.1016/0038-092x(80)90020-1 7. Oliva, A., Costa, M., Segarra, C.: Numerical simulation of solar collectors: the effect of nonuniform and nonsteady state of the boundary conditions. Sol. Energy 47(5), 359–373 (1991). https://doi.org/10.1016/0038-092x(91)90030-z

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8. Ouzzane, M., Galanis, N.: Numerical Analysis Of Mixed Convection In Inclined Tubes With External Longitudinal Fins. Sol. Energy 71(3), 199–211 (2001). https://doi.org/10.1016/s0038092x(01)00030-5 9. Ramírez-Minguela, J., Alfaro-Ayala, J., Rangel-Hernández, V., Uribe-Ramírez, A., MendozaMiranda, J., Pérez-García, V., Belman-Flores, J.: Comparison of the thermo-hydraulic performance and the entropy generation rate for two types of low temperature solar collectors using CFD. Sol. Energy 166, 123–137 (2018). https://doi.org/10.1016/j.solener.2018.03.050 10. Allan, J., Dehouche, Z., Stankovice, S., Harries, A.: Computational fluid dynamics simulation and experimental study of key design parameters of solar thermal collectors. J. Sol. Energy Eng. 139(5), 051001 (2017). https://doi.org/10.1115/1.4037090 11. Cerón, J., Pérez-García, J., Solano, J., García, A., Herrero-Martín, R.: A coupled numerical model for tube-on-sheet flat-plate solar liquid collectors. Analysis and validation of the heat transfer mechanisms. Appl. Energy 140, 275–287 (2015). https://doi.org/10.1016/j.apenergy. 2014.11.069 12. Jiandong, Z., Hanzhong, T., Susu, C.: Numerical simulation for structural parameters of flatplate solar collector. Sol. Energy 117, 192–202 (2015). https://doi.org/10.1016/j.solener.2015. 04.027 13. Dovi´c, D., Andrassy, M.: Numerically assisted analysis of flat and corrugated plate solar collector thermal performance. Sol. Energy, 86(9), 2416–2431 (2012). https://doi.org/10.1016/ j.solener.2012.05.016 14. Allan, J., Shah, L.J., Furbo, S.: Flow distribution in a solar collector panel with horizontally inclined absorber strips. Sol. Energy 81(12), 1501–1511 (2007). https://doi.org/10.1016/j.sol ener.2007.02.001 15. Martinopoulos, G., Missirlis, D., Tsilingiridis, G., Yakinthos, K., Kyriakis, N.: CFD modeling of a polymer solar collector. Renew. Energy 35(7), 1499–1508 (2010). https://doi.org/10.1016/ j.renene.2010.01.004 16. Selmi, M., Al-Khawaja, M.J., Marafia, A.: Validation of CFD simulation for flat plate solar energy collector. Renew. Energy 33(3), 383–387 (2008). https://doi.org/10.1016/j.renene.2007. 02.003 17. De Vahl Davis, G.: Natural convection of air in a square cavity: a bench mark numerical solution. Int. J. Numer. Meth. Fluids 3(3), 249–264 (1983). https://doi.org/10.1002/fld.165003 0305 18. Samdarshi, S.K., Mullick, S.C.: Analysis of the top heat loss factor of flat plate solar collectors with single and double glazing. Int. J. Energy Res. 14(9), 975–990 (1990). https://doi.org/10. 1002/er.4440140908 19. Subiantoro, A., Ooi, K.T.: Analytical models for the computation and optimization of single and double glazing flat plate solar collectors with normal and small air gap spacing. Appl. Energy 104, 392–399 (2013). https://doi.org/10.1016/j.apenergy.2012.11.009

Rice Paddy as a Source of Sustainable Energy in India Mohnish Borker and T. V. Suchithra

1 Introduction India is a densely populated country with a population of about 1.25 billion people. Of all, a large margin of 300 million people still lives without electricity. Thus, it makes India as one of the largest un-electrified populations in the world. Only 55% of households in rural areas have electricity compared to 93% in urban areas. As seen from Fig. 1, almost 70% of India’s electricity comes from coal-based power plants, 16% from hydro power, and another 4% from nuclear. The remaining 10% comes from the renewables depending on daily conditions [1]. GDP of India is growing at a larger pace, and to sustain this, it is important that corresponding growth in demand of primary energy as well as electricity and plans to meet the demands are considered [2]. The grid-tied renewable energy capacity in India has reached 42 GW, wherein wind energy contributes to 66 and 15% from solar PV. The rest is by biomass and small hydro power plants. Biomass power has attained a total installed capacity of 4.5 GW [3]. Agriculture and other allied sectors account for 14% of India’s GDP. The economic contribution of agriculture to India’s GDP is steadily decreasing due to the broadbased economic growth of the country. Over 58% of the rural household depends on agriculture as their principle means of livelihood. India achieved a record high rice production of 104.32 million tons in 2017. Incorporating crop cultivation with sustainable energy generation can lead to a breakthrough in solving the demand crisis [4]. Bioelectricity generation without competing with crop production is advantageous to meet the demand of the rising population. This breakthrough is possible by using M. Borker (B) Padre Conceicao College of Engineering, Verna, Goa, India e-mail: [email protected] T. V. Suchithra National Institute of Technology Calicut, Calicut, Kerala, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_37

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Fig. 1 Energy scenario of India

Indian Energy Scenario 2017 Coal & Natural Gas Hydro power

Nuclear Renewables Oil

a plant microbial fuel cell. Unlike a conventional fuel cell, a microbial fuel cell exploits microorganism by using waste organic matter as a source of substrate. Plants release a considerable number of organic compounds (C6 H12 O6 ) during the process of photosynthesis. Bacteria which are electrochemically active present around the roots breakdown this organic matter, releasing electrons. Electricity is generated when an electron acceptor or an electrode is placed in the vicinity of these bacteria. The one with a higher potential is used as an anode. The plant is placed in the anode region where the anodic environment is made favorable for the plant growth. Figure 2 provides the basic design of a plant microbial fuel cell. Electricity generation takes place in two steps. (i) In the anode section, electrons are released wherein they are taken up by the anode and transferred to the cathode by an eternal circuit through a load. (ii) The electrons combine with the protons passing through the membrane forming water in the cathode region. The reactions at the anode and cathode are as follows: Anode: 2C6 H12 O6 → 2C6 H10 O6 + 4H+ + 4e− Fig. 2 Basic design of a plant microbial fuel cell

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Cathode: O2 + 4H+ + 4e− → 2H2 O Net Reaction: 2C6 H12 O6 + O2 → 2C6 H10 O6 + 2H2 O There are basically three designs of plant MFC (a) sediment-type microbial fuel cell [5], (b) plant microbial fuel cell [6], and (c) flat-plate plant microbial fuel cell [7]. In general, a plant MFC is classified in two categories, single chamber and dual chamber. The classification is based on the incorporation of a proton exchange membrane which is absent in a sediment MFC. In this study, rice paddy (Oryza sativa) is used in three different PMFC models; sediment PMFC, rooftop PMFC, and dual-chamber PMFC. The compatibility of the plant in the three models was tested, and its performance parameters were measured. Performance parameters like the open-circuit voltage (V oc ), generated voltage (V ), current density (I), and power density (P) w.r.t. the anode geometric area and the internal resistance are considered. Based on the different working conditions, the proof of claim tests was carried out. To determine the performance of the electrogenic bacteria in the PMFC system, microbial isolation was carried out by repeated striking on an agar-based petri plate media.

2 Experimental Setup The performance evaluation of rice paddy in a plant microbial fuel cell was carried out using three different PMFC models. The rice paddy was sown in the three PMFC models at the end of July 2017. The three models were placed in the natural environment of National Institute of Technology, located at Kozhikode, Kerala (11.3217° N, 75.9342° E). Kozhikode experiences hot humid summers with temperatures ranging from 34 °C maximum to 23 °C minimum. The rainfall is mostly from the southwest monsoons starting from June till September, while the northeast rains arrive in the second half of October through November.

2.1 Sediment PMFC Sediment PMFC is a single-chamber module with only anode chamber. The cell lags a proton exchange membrane and a cathode chamber, where the cathode is exposed to the atmosphere. The plants are cultivated in the anode region, and the electrodes are separated by maintaining a suitable potential difference.

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Fig. 3 Sediment PMFC with rice paddy

The sediment PMFC considered in this study is a simple plant in beaker sediment MFC. Carbon cloth (90 × 70 mm) is used as the electrode material. The anode is placed at the base of the beaker, while the cathode is placed at the top, exposed to the atmosphere. A 10 cm gap is maintained between both the electrodes by placing a thermo-foam sheet around the plants which also maintains the stability of the plant. The two electrodes are connected to an external circuit consisting of a multimeter and load by using crocodile clips. Figure 3 depicts the plant in beaker sediment PMFC model.

2.2 Rooftop PMFC An innovative technique of combining green roofs with electricity generation is a rooftop PMFC. The system incorporates large covers of land or combination of small modules for electricity generation. It is considered to be an advanced form of sediment PMFC made up of a single chamber with no proton exchange membrane and a cathode chamber. The rooftop PMFC system in this study consisted of a single module in form of a box-shape container (300 × 200 × 150 mm). The cathode and anode consisted of graphite sheets, wherein suitable slots were provided on the cathode sheet to accommodate the plants. Cocopeat as growth media is used in the anode. Using crocodile clips both the electrodes are connected to the external circuit. Figure 4 displays the rooftop PMFC system with and without the plants.

Rice Paddy as a Source of Sustainable Energy in India

a)

387

b)

Fig. 4 Rooftop PMFC a blank module b with rice paddy

2.3 Dual-Chamber PMFC A dual-chamber PMFC consists of two different anode and cathode chambers separated by a proton exchange membrane. The rate of contamination is high in singlechamber sediment PMFCs due to no proper channeling of the electrons and the protons. In the dual-chamber system, the electrons and protons are filtered due to the PEM. Thus, the output of a dual-chamber PMFC is high. In this study, two graphite sheets (150 × 150 mm) were placed in the anode and cathode chambers, where the plants are cultivated on the sheet in the anode chamber. The anode and cathode are separated using Nafion 117, a proton exchange membrane (100 × 100 mm). The electrons having affinity toward the anode will be absorbed at the anode, while the protons will pass through the PEM to the cathode chamber, consisting of still water. Fig. 5 Dual-chamber PMFC model

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The electrodes are connected to the external circuit by using gold wire. Figure 5 elaborates the dual-chamber PMFC model.

3 Results and Discussion 3.1 Electron Cycle Determination Grass species like rice paddy that can survive in waterlogged conditions are more suitable for a PMFC; the claims have been made by research scholars at Wageningen University, Netherlands [8]. Grass species have fibrous roots which enables them to evenly disperse the rhizodeposits in the lower soil. Determining the electron cycle is the primary step in selecting a plant on trial and error basis for a plant MFC. Exudates released by the plants in the rhizosphere are broken down by electrochemically active bacteria (EAB) to release electrons. Theses electrons are attracted to the anode which acts as an electron acceptor. The electrons are then directed through an external circuit consisting of a load and are released near the cathode, where it combines with the protons forming water with O2 from the atmosphere. During this process, the electrons travel from one chamber to another in form of a cycle, and during this cycle, it produces electricity. Rice paddy was tested in a plant in beaker sediment type PMFC to determine the electron cycle. Carbon cloth (90 × 70 mm) is used as an electrode material for both anode and cathode, maintaining a distance of 70 mm between the two. The plant is plotted with loam soil, wherein the anode is placed at the bottom of the beaker and the cathode is at the top of the coil cover, exposed to the atmosphere. Both the electrodes are connected using crocodile clips to the external circuit. The electron cycle is generally obtained by measuring the open-circuit voltage (V oc ) and the short-circuit current (I sc ) manually using a multimeter (DT830D Digital Multimeter). An initial incubation period of 50 days or unless the V oc reading reaches 200 mV (whichever approaches first) is strictly maintained [6]. After an initial incubation period, the systems show considerable increase in the open-circuit voltage value. According to Schamphelaire et al. the possible reasons such as life cycle dependency of the rhizodeposits release, omission of the nutrients into the lower soil, release of oxygen through the aerenchyma, scavenging the electrons already collected at the anode, and lack of an adapted anodic microbial consortium are necessary for the initial incubation period [3].

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3.2 Polarization Curves The different plant MFC models were tested to evaluate the performance parameters like current density, power density, and internal resistance. Polarization curves were obtained for different values of resistances like 50, 100, 500, 1000, 1500, and 2000 . V oc : Open-circuit voltage (V) I sc : Short circuit current (mA) V: Generated voltage (V) Rext : External resistance () I: Current density (mA/m2 GA). I =

V Aanode RExt

(3.1)

Ri: Internal Resistance (/m2 GA). Rint =

(Voc − V ) I

(3.2)

P: Power density (mW/m2 GA). P=V×I

(3.3)

6.00

1.2

5.00

1

4.00

0.8

3.00

0.6

2.00

0.4

1.00

0.2

0.00

0

200

400

600

800

0

Power density (W/m2)

Fig. 6 Polarization curve of rice paddy in dual-chamber PMFC

Current density (A/m2)

The polarization curves of all the three different models, dual-chamber, sediment, and rooftop PMFC are depicted in Figs. 6, 7, and 8, respectively. Table 1 displayed below gives the overall performance parameters like max current density, max power density, and the internal resistance of the three PMFC models.

Voltage (mV)

I (A/m2) P (W/m2)

M. Borker and T. V. Suchithra 400 350 300 250 200 150 100 50 0 600

6.00

Fig. 7 Polarization curve of rice paddy in sediment PMFC

I (A/m2)

5.00 4.00 3.00 2.00 1.00 0.00

0

200 400 Voltage (mV)

P (mW/m2)

390

I (A/m2)

Current density (mA/m2)

Fig. 8 Polarization curve of rice paddy in rooftop PMFC

2000

60 50

1500 40 30

1000

20 500 10 0

0 0

200

400

Voltage (mV)

Table 1 PMFC polarization details

Power density (mW/m2)

P (mW/m2)

600

I (mA/m2) P (mW/m2)

Model

Max current density (A/m2 )

Max power density (W/m2 )

Internal resistance ()

Sediment PMFC

3.59

0.364

1500

Rooftop PMFC 0.51

0.053

500

Dual-chamber PMFC

1.042

1500

3.74

It depicts the high-power density of a dual-chamber PMFC in comparison with the lower internal resistance of the rooftop PMFC.

3.3 Dependence on Solar Radiation Plant microbial fuel cell utilizes the organic compounds produced by the plant during photosynthesis as the organic substrate. The rate at which these organic compounds are released into the system depends on the rate of photosynthesis, which in turn depends on the incident radiation. PMFCs are indirectly dependent on the incident

Fig. 9 Voltage variation of PMFC

Open circuit voltage (mV)

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800 700 600 500 400 300 200 100 0 0

5

10

15

20

25

Time (hours) Dual Chamber PMFC Sediment MFC

Rooop PMFC

solar radiation, and to prove this claim, the open-circuit voltage (V oc ) was measured on daily basis at different intervals of time. Figure 9 depicts the voltage variation on daily basis for the three PMFC models. It is been observed that the V oc values of the three modules have seen a high increase post-noon. The value decreases by nightfall and remains low during night.

4 Conclusion Incorporating living plants in microbial fuel cell to reduce the rising demand for energy has laid a large impetus on bioenergy research. Three different PMFC models were tested for electricity generation using rice paddy (Oryza sativa). From the polarization details, the dual-chamber PMFC generated high-power density (1.042 W/m2 ). The models were tested for their performance at different intervals of time in a day, and their electron cycles were also evaluated. For large-scale application, the effect of growth enhancers was studied and found to be effective. The practical application of the PMFC depends mostly on the solar insolation and life cycle of the plant. Due to in situ power generation, the dualchamber system cannot be implemented for large-scale applications. This is mainly a concern during harvesting of the crop and incorporation of the expensive Nafion 117 a membrane. Rooftop PMFCs can be exploited with various other grass species as they have high potential for power generation.

References 1. Richard, M.: India’s Energy Crisis. MIT Technology Review (2015) 2. Mahapatra, G.: Renewable Energy in India. In: Conference Proceedings of Economics for Ecology (2016)

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3. Grover, R.B., Chandra, S.: Scenario for growth of electricity in India. J. Energ. Policy 34, 2834–2847 (2006) 4. Havliek, P, Schneider, U.A.: Global land-use implications of first-generation and secondgeneration biofuel targets. Energ. Policy 39, 5690–5702 (2011) 5. Schamphelaire, L.D., Bossche, L.V.D., Dand, H.S.: Microbial fuel cell generating electricity from Rhizodeposits of rice plants. J. Environ. Sci. Technol. 42, 3053–3058 (2008) 6. Strik, D., Hamelers, H.: Green electricity production with living plants and bacteria in a fuel cell. Int. J. Energ. Res. 32, 870–876 (2008) 7. Helder, M., Strik, D.: Year-round performance of the flat plate plant microbial fuel cell. J. Appl. Biol. Sci. 76, 55–57 (2012) 8. Helder, M., Strik, D., Hamelers, H.: Concurrent bioelectricity and biomass production in three plant microbial fuel cells. J. Biores. Technol. 101, 3541–3547 (2010)

Cost and Emission Trade-Offs in Electricity Supply for the State of Maharashtra Pankaj Kumar, Trupti Mishra, and Rangan Banerjee

1 Introduction The Indian power sector is coal dominated and responsible for the largest share of India’s energy-related emissions. In the year 2017, coal thermal power plants generated 1133 TWh of electricity which was 74% of total production [1]. In the year 2014, nearly 63% of India’s energy-related emissions were from electricity generation [2]. However, India’s per capita electricity consumption in 2015 stood at 1 MWh/capita in 2017 in comparison to the global average of 3.2 MWh/capita. With the growing population and increasing electricity consumption, India’s power sector needs to be decarbonized to mitigate climate change. In this context, India’s Paris Agreement targets aimed at 40% non-fossil share of cumulative power generation and 33–35% reduction in emission intensity by 2030 [3]. Emission reduction has also been a priority of the Indian government as reflected in recent policies [4, 5]. The average plant load factor for Indian electricity sector had fallen to 63% as of March 2015 [6]. Hence, large operational flexibility can be utilized at national and sub-national scales to reduce emissions. In this study, we analyze trade-offs between cost and emissions for the state of Maharashtra for one recent day of operation.

P. Kumar (B) Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India e-mail: [email protected] T. Mishra SJM School of Management, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India R. Banerjee Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_38

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2 Literature Review Many studies were conducted in past to provide insights on emission reduction costs using optimization models. Anandarajah and Gambhir [7] used TIMES integrated assessment model to explore India’s emission reduction potential with renewables using CCS and non-CCS scenarios. Gambhir et al. [8] used TIMES to explore fossil fuel impacts and benefits of mitigation in a carbon permit trading scheme. Phadke et al. [9] used a capacity expansion and production cost model (PLEXOS) to analyze the implications of India’s ambitious 175 GW renewable target for Paris Agreement. Palchak et al. [10] created a production cost model to analyze the implications of high penetration of renewables. Both Phadke et al. [9] and Palchak et al. [10] used models with detailed load curves for India along with operational constraints. All these studies conducted in the past used technology interventions to achieve decarbonization. However, there is a need for insights on emission reduction potential of existing power sector infrastructure using a low carbon operating strategy. This study attempts to address this research gap using a dispatch model in TIMES framework with unit commitment and dispatch features. We created the power sector model with hourly temporal resolution and unit-wise representation of power plants allocated to Maharashtra. Further, one recent day of operations (January 15, 2019) were analyzed for minimizing cost and emissions.

3 Methodology 3.1 Overview of Electricity Generation for Maharashtra The total electricity supply for India stood at 12,06,306 MU in 2018–19 [11]. As of March 2018, India had 620 coal thermal (197.1 GW) and 239 gas thermal (24.8 GW) power plants operational with a total power sector capacity of 344 GW. Here, the power generation capacity available for Maharashtra on January 15, 2019, included 21.67 GW of coal thermal power capacity out of 34.735 and 3.03 GW out of 4.45 GW gas thermal capacity [12]. Within this, the coal and gas power generation capacity allocated under power purchase agreements was 24.70 GW. The thermal power plant capacity allocated to Maharashtra is shown in Fig. 1 and summarized in Table 1. The average heat rate and emission factor of coal thermal units were 2468 kcal/kWh (author compiled from various tariff notifications) and 0.99 tCO2 /MWh [13]. The electricity demand for January 15, 2019, stood at 365,189 MWh [12]. The average age of coal thermal power plants available for generation on the day of analysis was 15.4 years (refer Table 2).

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Fig. 1 Maharashtra map with allocated power plant capacity Table 1 Electricity generation capacity available for January 15, 2019 Technology type

Total capacity available (GW)

Allocated capacity under PPAs (GW)

Capacity available on January 15, 2019 (excluding maintenance schedule)

Coal

34.81

21.67

34.31

Gas

3.952

3.033

1.985

Solar

0.1

0.04

0

Nuclear

0.544

1.52

0

Hydro

0.391

1.45

0

Table 2 Descriptive statistics of coal power plants available for Maharashtra (age, emission factor, cost of electricity, heat rate) (author compiled from MERIT India database [12], CEA emission factors [13] and various tariff notifications) Plant unit characteristics

Mean

Stdev

Max

Min

Capacity (MW)

413.3

190.9

800

200

Net efficiency (%)

32.3

3.1

39.9

22.2

Emission factor (tCO2 /MWh)

0.99

0.09

1.2

0.8

Cost of electricity (Rs./kWh)

3.38

1.01

5.98

1.89

Age (years)

15.4

12.14

40

2

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3.2 Reference Energy System The reference energy system for this study represents all the available technologies for electricity generation and commodity flows in the system. For this analysis, all the available coal and gas thermal power plants were modeled unit-wise. All the available power plants for Maharashtra (fully or partially allocated) were considered for electricity generation. Nuclear, solar and hydro were not scheduled for electricity generation on 15 January and hence are not included for analysis (in line with MERIT India [12]). The model resolution was set at hourly temporal scale. The average load curve shape for January was taken for Maharashtra [14] and represented with actual demand for January 15, 2019. The typical load duration curve for Maharashtra has twin peaks which arise due to commercial electricity demand in daytime and evening due to high residential electricity demand. The electricity demand was exogenously specified in line with actual demand for the day. The fixed and variable cost of power plants were taken from MERIT India website. The reference energy system for Maharashtra is modeled on The Integrated MARKAL-EFOM System (TIMES) framework. TIMES is a bottom-up, linear programming model generator with a partial equilibrium framework which minimizes the total cost of electricity production. The model used for this study uses unit commitment and dispatch features in TIMES with operational constraints. The framework is used for this study due to its technology explicit features and linear programming framework which suits well for minimizing cost and emissions. Objective Function a. Cost minimization The objective function of TIMES minimizes the total cost of electricity production in the planning horizon. Here, the cost of electricity includes fixed and variable costs. PRODCOST(t) = Min

t= p 

((FIXOM(t, k) + VAROM(t, k))

t=1

 ∗ (ACTL(t, k, s)

 (1)

s

where PRODCOST (t) = total cost of electricity production on average day in year t FIXOM (t, k) = fixed cost component of tariff for electricity production from technology k VAROM (t, k) = variable cost component of tariff for electricity production from technology k ACTL (t, k, s) = activity level of technology in time period t, technology k and timeslice s.

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b. Emission minimization The objective function here minimizes the total carbon tax from electricity sector. (PRODCOST(t)) = Min

t= p 

(CT(t, k))

(2)

t=1

where CT(t, k) = carbon tax from electricity production for technology k, in time period t. Here, unit carbon tax is imposed for electricity sector and all fixed and variable costs are ignored. Thus, this objective function minimizes total carbon emissions. Constraints The constraints are to be satisfied by the model while minimizing total system costs. The constraints in the model are as follows a. Demand satisfaction 

ACTL(t, k, s) ≥ D(t, d, s)

(3)

k

The total supply ACT (t, k) at every given timeslice should be at least equal to total electricity demand D(t, d). b. Capacity utilization ACTL(t, k, s) ≤ AF(t, k, s) ∗ CAPUNIT ∗ CAP(t, k)

(4)

The total activity ACTL(t, k, s) of a particular technology k under operation at a given timeslice level s should not exceed its availability factor AF(t, k, s). The detailed equations for ramp rates, and minimum online and offline times can be found at Loulou and Labriet [15] and Loulou [16]. The ramp rates of all power plants were assumed in line with CERC norms.

4 Results As of January 15, 2019, there were 84 coal thermal and 25 gas thermal power plants allocated to the state of Maharashtra for electricity generation. Within the given set, we consider all power plants available for electricity production (83 coal thermal and 16 gas thermal) on the given day. Out of these coal thermal power plants, 26 units had emission factors above 1 tCO2 /MWh. Figure 2 summarizes the cost of electricity, emission factor and heat rate distribution of coal thermal power plants of the reference energy system.

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Fig. 2 Cost of electricity, heat rate and emission factor distribution of thermal power plant units

4.1 Electricity Dispatch As represented in Fig. 3, coal plays a dominant part in electricity supply in both cost and emission minimization scenarios (CM and EM) for Maharashtra. In cost minimization scenario, coal thermal power plants supply 95.5% of electricity amounting to 349.06 GWh, while in emission minimization scenario coal-based electricity

Fig. 3 Electricity dispatch: a Cost minimization and b Emission minimization

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reduces to 86.9% of the total at 317.53 GWh due to higher emission factor associated with coal. The cost minimization scenario prioritizes older power plant units for electricity generation, while emission minimization scenario prioritizes power plant units with lesser emission factors.

4.2 Active Power Plants in Cost Minimization and Emission Minimization Scenarios There are 37 coal power plant units of 17.14 GW active in cost minimization scenario. All of these units have cost of electricity lower than 2.93 Rs./kWh. In contrast, there are 33 coal units of 16.49 GW and all gas units are active in emission minimization scenario. Highest emission factor of power plant units is 0.96 tCO2 /MWh in emission minimization. The distribution of active coal thermal units is summarized in Figs. 4, 5 and 6. There are 20 coal units of 11.2 GW and 6 gas units of 0.112 GW operating in both cost and emission minimization scenarios and providing a win-win strategy for emission reduction. The weighted average age of thermal power plants reduces from 15.07 years in cost minimization scenario to 10.25 years in emission minimization scenario. The youngest coal unit active in cost minimization scenario is of 6 years, while the oldest unit active in emission minimization scenario is of 12 years. It can be observed from Fig. 5 that power plant units with lower emission factors are working in emission minimizing scenario while units with lower costs are operating in cost minimizing scenario. Thus, the cost minimizing scenario results in operation of a large number of older units with higher emission factors. The older units get prioritized in cost minimization because they are more economical to operate and have recovered most of their fixed cost component. In contrast, the newer plants are cleaner and have lesser emission factors than older plants. However, they have higher cost of electricity since their fixed cost needs to be recovered.

Fig. 4 Operating units in scenarios: a Cost minimization and b Emission minimization

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Fig. 5 Cost of electricity and emission factor of units in a Cost Minimization b Emission Minimization

Fig. 6 Operating capacity in a Cost minimization and b Emission minimization

4.3 Emissions and Cost of Electricity The emissions from power plants amount to 331.14 kt for cost minimization scenario. The cost of electricity in this scenario is 2.40 Rs/kWh. The emissions in emission minimization scenario reduce by 9.2% to 300.67 kt, while the cost of electricity increases by 30.8% to 3.14 Rs/kWh. This emission reduction is attributed to higher operations of cleaner gas and coal units. The cost of electricity is higher in emission minimization scenario due to higher cost of gas thermal electricity and higher cost of electricity from newer coal thermal units. The total cost of power generation per day increases from 876.4 million INR in cost minimization scenario to 1145.41 million INR in emission minimization scenario. The cost of carbon abatement in emission minimization scenario amounts to 8827 Rs/ton. This cost of carbon abatement is comparatively higher than carbon prices currently in developed countries [17].

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5 Conclusion We created a power sector model with unit commitment and dispatch features consisting of all available power plant units for Maharashtra. This model was used to analyze cost and emission minimizing generation for one recent day with existing power plant performance characteristics. In our results, we found that the emissions from thermal power can be reduced by 9.2% in emission minimization as compared to cost minimization scenario. The cost minimization scenario has the cost of electricity at 2.40 Rs/kWh and emissions at 331.14 kt. In contrast, the cost of electricity in emission minimization scenario increases by 30.8% to 3.14 Rs./kWh while emissions decrease to 300.67 kt. The cost minimization scenario has all plants active below cost of electricity of 2.93 Rs/kWh, while emission minimization scenario has all plants below 0.96 tCO2 /MWh. There are 20 coal thermal units of 11.2 GW and 6 gas units of 0.112 GW operating in both scenarios providing a strategy to reduce emissions with lower costs. The cost of carbon abatement for Maharashtra is 8827 Rs/ton which is much higher than the cost of carbon abatement in most of the developed countries. The insights of this study can be helpful in mitigating emissions from the power sector irrespective of additional investments in retrofits or new capacity. There is a trade-off in cost and emission minimizing generation strategies in terms of cost and emissions. This trade-off in cost and emission at national and subnational scales can be useful in prioritizing power plants for electricity dispatch. The additional cost of mitigation here can be financed by Green Climate Fund. Acknowledgements The authors would like to thank the Department of Science and Technology for supporting fellowships of students at the Interdisciplinary Programme in Climate Studies. We would also like to thank the Industrial Research and Consultancy Centre (IRCC) at Indian Institute of Technology Bombay for their funding support.

References 1. International Energy Agency. https://www.iea.org/countries/India. Last accessed 2019/3/10 2. Ananthakumar, M., Rachel, R., Lakshmi, A., Malik, Y.: Energy Emissions. Version 2.0 dated 2017/9/28 from GHG platform India: GHG platform India-2005–2013 National Estimates2017. Available at http://ghgplatform-india.org/data-and-emissions/energy.html. Last accessed 2019/3/10 3. UNFCCC Homepage. https://www4.unfccc.int. Last accessed 2019/3/10 4. CERC: CERC RE Tariff Order 2017–18. New Delhi (2017) 5. CEA: National Electricity Plan. Central Electricity Authority, New Delhi (2018) 6. CEA: Executive Summary Power Sector March-15. Central Electricity Authority, New Delhi (2016) 7. Anandarajah, G., Gambhir, A.: India’s CO2 emission pathways to 2050: what role can renewables play? Appl. Energ. 131, 79–86 (2014) 8. Gambhir, A., Napp, T., Emmott, C., Anandarajah, G.: India’s CO2 emissions pathways to 2050: energy system, economic and fossil fuel impacts with and without carbon permit trading. Energy 77, 791–801 (2014)

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9. Phadke, A., Abhyankar, N., Deshmukh, R.: Techno-economic assessment of integrating 175 GW of renewable energy into the Indian Grid by 2022. Ernest Orlando Lawrence Berkeley National Laboratory (2016) 10. Palchak, D., Cochran, J., Deshmukh, R., Ehlen, A., Soonee, S., McBennett, B., Milligan, M., Chernyakhovskiy, I., Narasimhan, S., Joshi, M., Sreedharan, P.: Greening the grid: pathways to integrate 175 GW of renewable energy into India’s electric grid, vol. 1. National Study (2019) 11. Ministry of Power.: Power Sector at a Glance ALL INDIA. https://powermin.nic.in/en/content/ power-sector-glance-all-india. Last accessed 2019/3/14 12. MERIT-Merit Order Dispatch of Electricity for Rejuvenation of Income and Transparency. http://meritindia.in/. Last accessed 2019/3/10 13. CEA: Central Electricity Authority: CO2 baseline database. New Delhi (2016) 14. POSOCO: Electricity Demand Pattern Analysis, vol. 1. Power System Operation Corporation Ltd (2016) 15. Loulou, R., Labriet, M.: ETSAP-TIAM: the TIMES integrated assessment model part I: model structure. Comput. Manag. Sci. 5(1–2), 7–40 (2007) 16. Loulou, R.: ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation. Comput. Manag. Sci. 5(1–2), 41–66 (2007) 17. Ernst & Young LLP: Discussion Paper on Carbon Tax Structure for India. Shakti Sustainable Energy Foundation (2015)

Technological Interventions in Sun Drying of Grapes in Tropical Climate for Enhanced and Hygienic Drying Mallikarjun Pujari , P. G. Tewari , M. B. Gorawar , Ajitkumar P. Madival , Rakesh Tapaskar , V. G. Balikai , and P. P. Revankar

1 Introduction Agriculture supports about 58% of rural India for livelihood and contributes 4th largest export commodity equivalent to 10% monetary share [1]. The food safety and security ensures prevention of hygiene loss through industry-scale preservation. Of all preservation methods, drying has better features to reduce post-harvest loss with sun drying as widely adopted practice [2]. The solar and hot air drying of agrifood products has extensively reported literature [3] on grape-drying kinetics in solar dryer using pre-treatment [4–8]. Indian grape-grown area includes subtropical, hot tropical and mild tropical agro-climatic zones. The hot tropical agro-climatic zone has 70% of grape cultivation covering parts of the states of Maharashtra, Andhra Pradesh and Karnataka in region between 15 and 20° N latitudes. In India, grape is mainly dried in shed with 5–10 racks, each 50–100 m long, 2.5 m high and 1.5 m width [9] in N–S orientation.

2 Literature Review The literary reviews have extensively reported on OSD and SDA grape drying by pre-treatment with K2 CO3 solution and dipping oil for improved quality of grape. Pangavhane et al. reported that fresh hand-harvested Thompson seedless grape has an average sugar level of 23 on Brix scale (1 Bx is equivalent to 1 g of sucrose per 100 g of solution). The sorted grapes need water wash to remove dust and dirt due to cultivation and reaping for subsequent drying using air at 60 °C with M. Pujari · P. G. Tewari · M. B. Gorawar · A. P. Madival · R. Tapaskar · V. G. Balikai · P. P. Revankar (B) School of Mechanical Engineering, K.L.E. Technological University, Hubballi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_39

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0.5 m/s flow rate [2]. Rathnayake et al. developed a compact drying cabinet for 50–100 kg range of produce, housed in 1.25 × 0.92 m space with trays of 0.73 × 0.6 × 0.025 m size adjusted at 5 cm inter-tray space. The device dried pepper at 57 °C in 23 h time with even airflow to limit temperature variation within 3 °C [3]. Fadhel et al. investigated sultana grape drying in natural convection dryer, tunnel greenhouse and open sun. The grapes soaked in 1% NaOH solution were heated to 90 °C for a 2–3 s soak time, twice or thrice before cleaning with distilled water [4]. Doymaz studied black grape pre-treatment in solutions of ethyl oleate plus potassium carbonate, potassium carbonate plus olive oil, ethyl oleate–potassium hydroxide and ethyl oleate–sodium carbonate. It exhibited multi-level drying time with ethyl oleate–potassium carbonate pretreated sample showing lowest dry time of 25 h. The lower specific energy for moisture removal in agricultural produce makes it fit for long-term storage [5]. Akoy et al. designed 16.8 m2 natural convection solar dryer for 100 kg of sliced mango. The 20 h drying attained 10% moisture from initial 81.4% on wet basis with emphasis on sustainability [6]. Vania et al. studied twolevel factorial designs for pretreated sample of Rubi grape in K2 CO3 solution at 50 °C [7]. Forson et al. developed mixed-mode natural convection solar crop dryer (MNCSCD) with air heater, drying chamber and chimney for 160 kg of cassava and other crops. The drying time was 30–36 h for solar irradiance of 400 W/m2 , 25 °C, and 77.8% RH [8]. Ehiem et al. developed 258.64 kg/batch industry-scale fruit/vegetable dryer and tested tomato sample to be 84% efficient. The airflow of 18.3 m/s, 18.8 m/s and 19.5 m/s was selected, respectively, for small-size, medium-size and large-size tomato. The larger-size tomato needed larger heat and mass transfer-driven moisture removal possible at high velocity [9]. Stephen and Emmanuel reported on proper selection of power source, material and alternate design strategies for faster drying. This mode had a provision to regulate drying with controlled heat input and airflow. The large-scale implementation of solar energy drastically reduces carbon footprint of excessive fossil fuel usage [10]. Babagana et al. developed a forced/natural convection solar dryer for vegetables and food with black corrugated aluminum plate. The collector (0.72 × 0.6 × 0.25 m) supplied heated air to gmelina wood drying cabinet (1.2 × 0.6 × 1.8 m). The experiments indicated 45% collector efficiency with sensible heat storage of 48.9 W/m2 K sufficient for 6 h drying during night [11]. Bhuiyan et al. utilized effect of both heat and mass diffusions in combined solar-mechanical dryer. The diffusion coefficient (De ) gave maximum activation energy in kcal/g-mole for potato as 7.656 and 8.252, respectively, for mechanical and solar conditions. The drying is a combined process of heat and mass transfer, and hence, dryer design should account both these effects [12]. Askari et al. reported on Rabat–Casablanca raisin quality on basis of Enterobacteriaceae stain samples as indicators of microbial flora with standard counts of plate, coliform, fecal coliform, yeast and mold averaged as 2.8 × 107 , 3 × 103 , 2.3 × 103 , 3 × 103 and 4.6 × 104 CFU/g, respectively [13]. Basumatary et al. developed low-cost wood flank dryer with cubical and triangular prism geometry and drying chamber with non-corrosive GI sheet. The 77 °C maximum was attained in drying chamber at noon with 64–66 °C as day-average. The local materials for solar dryer established sustainability into construction and

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operation of solar dryers [14]. Adiletta et al. studied white and red grape variety for abrasive pre-treatment in sheet-coated shaker using 50 °C air current at 2.3 m/s. The drying kinetics of treated samples was better as pre-treatment aids to dislodge bounded moisture [15]. Ubale et al. have reported on forced convection that utilizes both mass and heat transfers in moisture removal, but needs additional power to drive heated air. The crop energy requirements justify improvement in drying rate on account of forced convection driven by a SPV system [16]. Singh et al. experimented on Thompson seedless grape at 60 °C with a 0.82 m/s flow in laboratory-scale hot air dryer. Pre-treatment with 25 g of potassium carbonate and 15 mL ethyl oleate per liter of distilled water gave 3 min dip time. The dry time for 40 °C dipping solution was 19 h, against 28 and 25 h, respectively, for solutions at 20 and 30° [17]. Abay et al. adopted indirect passive conventional solar dryer for a batch of 10 kg tomato. The collector used corrugated absorber to create turbulence in airflow, and good insulation reduced heat losses. The absorber temperature was 77 °C at 12:00 am with 1021 W/m2 solar radiation. The payback and benefit-to-cost ratio (BCR) were 8 months and BCR of 11.8 [18]. Ubale et al. have reported on dryer parameters like air temperature, relative humidity, drying chamber humidity, air velocity and mass of grapes to present that indicated average evacuated tube efficiency of 24.5% against insulated dryer with 37.1% efficiency [19]. Sekyere et al. investigated hybrid dryer suitable for uniform drying coupled with an accelerated drying rate for the product. The mixed-mode natural convection solar crop dryer with backup heater dried pineapple in four different modes to get desired moisture between 106 and 184% (d.b) [20].

3 Drying Methods and Materials in Experimental Investigations The details of materials and methodology in Open sun drying (OSD) and Shed drying (SD) for grape samples namely untreated (UT) and chemically treated (CT) is presented in this section. The study investigated four options of grape drying to figure out best option to yield quality raisins. The OSD used clean polythene sheet spread on unshaded open ground with adequate sunlight to dry the grape sample laid on it as bunches. The shed accommodated about 5 tons of fresh grapes layered with adequate spacing in between over 11 tiers that extended to a width of 2 m and running over a length of 60 m. The shed preferably constructed on open land ensured no obstruction to airflow owing to properly selected spacing and configuration that were important for better air circulation through grape-loaded racks. The top region of the shed, built in steel sheets, formed the plane cover that facilitated protection from excessive sunbeam and occasional rains. Figure 1 shows details of both OSD and SD for large-scale drying of grapes with wire mesh netted rack having 2–3 cm mesh spacing for loading pretreated grapes.

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Fig. 1 Details of shed drying and open sun drying along with grape samples used in study

The shed had plastic or metallic roof with side overhang on top racks to protect grapes from rain and excessive sunlight. The rack spacing in shed dryer ensured adequate direct solar radiation during entire day, except at noon when sun was at zenith. The side curtains were occasionally used to prevent contamination by dust/rain.

3.1 Materials Used for Pre-treatment of Grapes The Thompson seedless grapes grown in a local farm were used to test on mass basis. The pre-treatment with 25 g of potassium carbonate and 15 ml of ethyl oleate (dipping oil) provided attractive golden brown color and accelerated drying. The cold-dipping pre-treatment was preferred to hot-dipping process [9, 10].

3.2 Measuring Equipment and Methodology The experiments conducted as per specified standards used equipment in Table 1 for sunshine data from 8:00 am to 6:00 pm. The macro-climatic factors are air temperature, wind speed, solar radiation and rainfall-affected moisture removal rate. The drying rate was co-related with air temperature and relative humidity as they accelerated grape drying. The adequate sunlight and temperature influenced dried grape quality. Table 1 Specifications of measuring equipment used for the study Name of equipment

Specification (range, least count, model and make)

Pyranometer

0–1999 W/m2 , ±10 W/m2 or ±5%, Megger Irradiance Meter, PVM210

Temperature indicator (°C)

0–250, 2.2 above 0, Samson Automation, Bangalore

Humidity sensor

0–100, 0.1, EQTRH304W, EOUINOX

Anemometer

0.4–45, ± (2% + 0.1 m/s), MEXTECH AM-4208

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The solar insolation, wind speed, air temperature and relative humidity were recorded using pyranometer, anemometer, probe thermometer and humidity sensor, respectively, at regular time ranges between 30 s and 10 min.

4 Results and Discussion The experimental observations are made to grape drying by two alternate methodologies to evolve better grape drying practices for enhanced revenue in raisin production.

4.1 Variation of Macro-climatic and Micro-climatic Grape Drying Parameters The grapefruit temperature in solar drying was directly proportional to intensity of solar insolation and duration of exposure. The site solar radiation intensity exhibits diurnal and seasonal variations; hence, dryer design should be based on data of climatic factors at the test site under investigation. The macro-climatic site parameters (maximum, minimum and average values) of wind speed, solar insolation and air temperature as indicated, respectively, in Figs. 2, 3 and 4 reveal their variable nature during the test duration. The wind speed averaged between 0.7 and 0.8 m/s influenced drying characteristics by assisting movement of humid air from berry surface. The observations pointed in Fig. 5 revealed shed temperature to be lower by 3–4 °C compared to ambient air temperature on account of shading effect produced inside the shed. The reduction in shed temperature prevented grape berry overheating and thereby its deterioration in quality. The micro-climatic parameter of relative humidity as indicated in Figs. 6 and 7, respectively, showed similar variations as recorded on a typical day and averaged values. The maximum berry temperature was 60 °C for grape drying when air temperature was 45 °C that Fig. 2 Variation of wind speed

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Fig. 3 Variation of solar insolation

Fig. 4 Variation of air temperature

Fig. 5 Variation of shed and air temperature

revealed lower wind speed due to which air circulation led to rapid heat loss from berry to air. The drying indicated in Fig. 8 is summarized in the form of drying curve generalized by M(t) = Mo e−kt

(1)

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Fig. 6 Variation of relative humidity (day 1)

Fig. 7 Variation of relative humidity

Fig. 8 Drying curves

where ‘k’ represents drying constant as shown in Table 2. The coefficient of determination (R2 ) for given experimental data is close to unity, and hence, time dependence of drying curve for four modes resembles reported trend of agricultural products. The comparison of drying constants in OSD and SD reveals that SD has ‘drying constant-k’ to be lower by 49.35% (untreated sample—UT) and 9.86% (chemically treated sample—CT).

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Table 2 Drying equation for grape in different modes S. No.

Drying mode

Drying equations

Constant M o

k, day−1

R2

1

OSD-UT

M(t) = 1378.7 Exp (−0.233 t)

1378.7

0.233

0.9862

2

OSD-CT

M(t) = 1339.2 Exp (−0.152 t)

1339.2

0.152

0.9685

3

SD-CT

M(t) = 1338.2 Exp (−0.137 t)

1338.2

0.137

0.9653

4

SD-UT

M(t) = 1312.2 Exp (−0.118 t)

1312.2

0.118

0.9532

The data also signifies role of chemical treatment to have a positive influence in case of shed drying with improved quality of raisins. The drying rate in OSD was higher than SD owing to the presence of greater beam radiation; however, it is associated with dust and other contamination.

4.2 Mechanism of Grape Drying and Grape Quality This section deals with experimental observations on preparing raisins from harvested raw grapes through four different approaches identified as OSD-UT, OSD-CT, SDUT and SD-CT. The details of weight reduction in the grapes observed for the four methods were compared to arrive at a suitable methodology for harvested grapes. Figures 8 and 9 represent drying in four different samples investigated in terms of weight reduction of sample and percentage moisture removal rate. The drying rate in OSD-CT was fastest with moisture removal rate also higher, particularly during the early phase of drying. The moisture removal decreased during last two days, and grapes dried to a final weight of 243 g within 60 h (spread over about six days). The direct radiation on berry in OSD mode leads to improper coloration and poor taste of grapes. The chemically treated grape was better compared to OSD-UT grape samples in terms of attributes in color and flavor. The moisture removal rate for different modes of drying revealed that moisture removal rate was faster in OSD-CT as compared to OSD-UT samples. Among all investigated four processes, OSD-UT Fig. 9 Moisture removal rate curve

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fared too poor in raisin quality, while SD-CT mode fared to produce best raisin quality. The OSD was faster in SD mode with CT exhibited accelerated drying on account of ‘cuticle layer opening’ or ‘blooming’ phenomenon in the grape berry. The OSDUT clocked 95 h compared to 60 h for OSD-CT, while SD-CT took 100 h to attain final dry weight of 0.251 kg that was 16.66% lower than 120 h observed in case of SD-UT mode. The external structure of dried products also referred to as morphological features plays a vital role in the final product value. The study recorded the drying cycle exhibited in the four modes through visual inspection of the samples at regular intervals during the drying cycle. The dynamic changes occur on account of physicochemical processes that occur on account of heat and convective flowdominated drying process. The summary of changes noted during study has been recorded as Table 3 to get comparative merits and demerits of the alternate drying strategies. Table 3 Morphological changes in the harvested grapes Test Day

1

2

3

4

Grape Drying Method OSD-UT

OSD-CT

SD-UT

SD-CT

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4.3 Raisin Sensory Evaluation on Color, Texture, Weight and Taste The qualitative sensory evaluation on four different dried raisins through experiments indicated that field dried grape and ‘market available fine-quality raisin’ were comparable in terms of color, texture, taste and grape weight per unit of initial grape berry. The qualitative study was performed through the defined 0–5 judgment scale: with poor quality indicated by digit zero and superior quality in coherence assigned as a five-point score. The sensory evaluation was conducted through trained observers who evaluated independent dried raisin samples with respect to grape quality parameters at defined intervals of the drying cycle. Figures 10, 11, 12 and 13 indicate 5

Fig. 10 Variation of color of grapes

OSD-UT

OSD-CT

SD-UT

SD-CT

Color scale

4

3

2

1

0

3

5

7

9

11

Days

Fig. 11 Variation of texture of grapes

5

Standard Raisin Texture Scale

OSD-UT

OSD-CT

SD-UT

SD-CT

4

3

2

1

0

3

5

7

Days

9

11

Technological Interventions in Sun Drying of Grapes … Fig. 12 Variation of grape weight reduction

OSD-UT

5

413 OSD-CT

SD-CT

SD- UT

Weight scale

4 3 2 1 0

1

3

5

7

9

11

Days

Fig. 13 Variation of grape taste

OSD-UT

5

OSD-CT

SD-UT

SD-CT

Taste standard

4 3 2 1 0

3

5

7

9

11

Days

sensory responses collected for raisins produced by four different drying methods with respect to raisin attributes of color, texture, weight and taste. The sensory evaluation for grape color performed on a 0–5 scale assigned 5-point value to best-quality attractive golden brown color, and 0-point value reflected poorquality black-colored raisins. Figure 10 indicates that OSD-UT gave poor-quality black color raisin and SD-CT had good-quality attractive golden-colored raisin. The second sensory evaluation parameter investigated was texture, defined by roundness and surface wrinkles resembling standard raisins. Figure 11 depicts the periodic variation of grape texture on 0–5-point scale with 0 score indicative for distorted raisin, while 5 on the scale stands for better-quality raisin texture. The third sensory parameter for evaluation was ‘dry weight’—an indicator of proper drying revealing either over-drying or under-drying of raisin. As revealed in Fig. 12, CT yielded better quality for both OSD and SD. The sensory evaluation based on taste as indicated in Fig. 13 identified SD-CT to be best among all the four methods investigated.

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5 Conclusions • Experimental location had adequate solar insolation, wind speed and ambient temperature making it suitable for Thompson seedless grape drying. The overall observations of macro-climatic and micro-climatic conditions indicated that location was economically viable to produce marketable quality of raisin. • OSD-CT provided faster drying rate, but some important physical qualities of dried product were not favorable on account of poor color, texture, taste and improper shrinkage owing to excessive exposure to beam solar radiation, dust and insects. • OSD-CT drying mode produced better-quality raisins with 58% faster drying than OSD-UT. Similarly, SD-CT was 40% slower than OSD-CT as potassium carbonate provided attractive golden brown color to raisins while dipping oil accelerated drying rate on account of grape cuticle blooming. The overall effect leads to better quality of dried product through adoption of shed drying over the OSD method. • The OSD-UT sample required a 95 h drying period to yield 0.234 kg of dry raisin, as against OSD-CT grape sample that took 60 h drying time. The simultaneously performed tests took 100 h and 120 h, respectively, for SD-CT and SD-UT samples to yield 0.251 and 0.310 kg of dry raisin. The raisin quality was better for shade drying on account of transparent plastic covering isolation that prevented dust contamination. • The SD-CT samples with attractive golden coloration strongly suggested its implementation for large-scale grape drying by an additional drying technique augmentation through use of solar air heater to improve drying rate.

References 1. http://www.ibef.org/industry/agriculture-india.aspx. Last accessed on 2019/08/20 2. Pangavhane, D.R., Sawhney, R.L., Sarsavadia, P.N.: Effect of various dipping pretreatment on drying kinetics of Thompson seedless grapes. J. Food Eng. 39(1), 211–216 (1999) 3. Rathnayake, R.M.S.P., Ariyartane, A.R., Prematilake, S.P.: Design and fabrication of engineering model of a crop dryer. Trop. Agric. Res. 18(1) (2008) 4. Fadhel, A., Kooli, S., Farhat, A., Bellghith, A.: Study of the solar drying of grapes by three different processes. Desalination 185(1), 535–541 (2005) 5. Doymaz, I.: Drying kinetics of black grapes treated with different solutions. J. Food Eng. 76(1), 212–217 (2006) 6. Akoy, E.-A.O.M., Ismail, M.A., Ahmed, E.-F.A., Luecke, W.: Design and construction of a solar dryer for mango slices. In: The Annual Conference on Tropical and Subtropical Agricultural and Natural Resource Management (TROPENTAG), 11–13 October 2006. University of Bonn, Institute of Crop Science and Resource Conservation (2006) 7. Telis, V.R.N., Lourençon, V.A., Gabas, L., Telis-Romero, J.: Drying rates of Rubi grapes submitted to chemical pretreatments for raisin production. Pesq. Agropuc. Bras. 41, 503–509 (2006). Brasilia

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8. Forson, F.K., Nazha, M.A.A., Akuffo, F.O., Rajakaruna, H.: Design of mixed-mode natural convection solar crop dryers: application of principles and rules of thumb. Renew. Energ. 32(1), 2306–2319 (2007) 9. Ehiem, J.C., Irtwange, S.V., Obetta, S.E.: Design and development of an industrial fruit and vegetable dryer. Res. J. Appl. Sci. Eng. Technol. 1(2), 44–53 (2009) 10. Stephen, A.K., Emmanuel, S.: Improvement on the design of a cabinet grain dryer. Am. J. Eng. Appl. Sci. 2(1), 217–228 (2009) 11. Babagana, G., Silas, K., Mustafa, B.: Design and construction of forced/natural convection solar vegetable dryer with heat storage. ARPN J. Eng. Appl. Sci. 7(1), 1213–1217 (2012) 12. Bhuiyan, M.H.R., Alam, M.M., Islam, M.N.: The construction and testing of a combined solar and mechanical cabinet dryer. J. Environ. Sci. Nat. Res. 4(2), 35–40 (2011) 13. Askari, G.A., Kahouadji, A., Khedid, K., Charof, R., Mennane, Z.: Physicochemical and microbiological study of raisin, local and imported (Morocco). Middle East J. Sci. Res. 11(1), 1–6 (2012) 14. Basumatary, B., Roy, M., Basumatary, D., Narzary, S., Deuri, U., Nayak, P.K., Kumar, N.: Design, construction and calibration of low cost solar cabinet dryer. Int. J. Environ. Eng. Manag. 4, 351–358 (2013) 15. Adiletta, G., Senadeera, W., Di Matteo, M., Paola, R.: Drying kinetics of two grape varieties of Italy. In: Proceedings of the 6th Nordic Drying Conference (2013) 16. Ubale, A., Pangavhane, D.R., Warke, A.: Performance improvisation of conventional grape drying method by introducing forced air exhaust. Am. Int. J. Res. Sci. Technol. Eng. Math. 1–5 (2014) 17. Singh, S.P., Jairaj, K.S., Srikanth, K.: Influence of variation in temperature of dipping solution on drying time and color parameters of Thompson seedless grapes. Int. J. Agric. Food Sci. 4(2), 36–42 (2015) 18. Tesfamariam, D.A., Bayray, M., Tesfay, M., Hagos, F.Y.: Modeling and experiment of solar crop dryer for rural application. J. Chem. Pharm. Sci. 109–118 (2015) 19. Ubale, A.B., Pangavhane, D., Auti, A., Warke, : Experimental and theoretical study of Thompson seedless grapes drying using solar evacuated tube collector with force convection method. Int. J. Eng. 28(12), 1796–1801 (2015) 20. Sekyere, C.K.K., Forson, F.K., Adam, F.W.: Experimental investigation of the drying characteristics of a mixed mode natural convection solar crop dryer with back up heater. Renew. Energ. 92(1), 532–542 (2016)

Effect of Temperature on the Hydrodynamics of Steam Reactor in a Chemical Looping Reforming System Agnideep Baidya, Saptashwa Biswas, Avinash Singh, Debodipta Moitra, Pooja Chaubdar, and Atal Bihari Harichandan

1 Introduction Immense increase in challenges for developing new technologies which would produce clean and an alternating form of energy due to exhausting petroleum resources and unwarranted utilization of fossil fuels that results in global warming and air pollution has been a crucial concern for researchers [1, 2]. However, the environmental concerns due to greenhouse gases can be very well addressed by considering hydrogen as fuel which would produce only water as a result of combustion with no other detrimental effect to the environment. But, every ton of hydrogen generation from natural gas, oil, coal and electrolysis process can lead to production of 9–10 tons of CO2 [3, 4]. However, chemical looping reforming (CLR) is a process that produces hydrogen in an efficient way with 100% CO2 capture being extensively analyzed [5–7]. A CLR system constitutes (i) Air Reactor (AR), (ii) Fuel Reactor (FR) and (iii) Steam Reactor (SR). First the oxidation of metal oxide takes place in the AR, and then, it passes to the FR where metal oxide reduction takes place with water vapor and CO2 is being produced as by-products. At the outlet, 100% CO2 is apprehended with condensation of water vapor. The reduced metal oxide then reacts with the oxygen from water vapor in the steam reactor and produces metal oxide along with hydrogen as products. Figure 1 depicts the working principle of CLR system. A fluidized bed reactor (FBR) is analyzed to simulate steam reactor considering highly complex fluid dynamics and reaction kinetics among solidus and gaseous particles which takes place inside the steam reactor. The real-time flow physics explicated with conservation of mass, momentum, energy and species transport A. Baidya · S. Biswas · A. Singh · D. Moitra · P. Chaubdar (B) · A. B. Harichandan KIIT Deemed to be University, Bhubaneswar, Odisha 751024, India e-mail: [email protected] A. B. Harichandan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_40

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Fig. 1 Chemical looping reforming system

is discussed explicitly using commercial CFD Software [3, 8–15]. However, the temporal features of bubble generation, up-surging, expanding and bursting in the steam reactor have not been reported by many researchers and can be broadly attempted with due consideration of various operating constraints. Present study reports the successive progress of bubbles in the SR of a chemical looping reforming system that is important specifically at start of the process and subsequently for further time-period before accomplishing quasi-steady state along with fuel conversion rate for varied fuel reactor temperatures in a CLR process. Two oxygen carriers (iron oxide and manganese oxide) with water vapor as fuel are used for the current study of CLR process in a stream reactor. The bubble hydrodynamics and the effect of temperature on the conversion of fuel into hydrogen are investigated. Following are the chemical reactions in the CLR process in which iron oxide and manganese oxide are considered to be an oxygen carrier independently: (i)

low temperature (700–1100 K) exothermic reaction in AR 4Fe3 O4 + O2 → 6Fe2 O3 8 2 Mn3 O4 + O2 → 4Mn2 O3 3 3

(ii) high temperature (800–1600 K) endothermic reaction in FR 4Fe2 O3 + CH4 → 8FeO + CO2 + 2H2 O

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4Mn2 O3 + CH4 → 8MnO + CO2 + 2H2 O (iii) low temperature (700–1200 K) exothermic reaction in SR 3FeO + H2 O(g) → Fe3 O4 + H2 3MnO + H2 O(g) → Mn3 O4 + H2 .

2 Numerical Considerations The unsteady multi-phase flow physics has been addressed by using Ansys FLUENT with phase coupled (PC-SIMPLE) finite volume method and second-order upwind scheme to solve the coupled equations. To solve the convective part of the equations, second-order QUICK scheme has been considered. Figure 2 shows the 2-D computational domain generated for the steam reactor having reactor height as 1 m and width as 0.25 m. Boundary conditions and numerical parameters for unsteady numerical simulations are shown in Tables 1 and 2, respectively. In order to study bubble hydrodynamics in circulating fluidized bed reactor precisely, authors have kept the grid size 10 times more than oxygen carrier particles’ size [4] and did not carry out any grid independence test explicitly. The steam reactor is parted into two regions: (i) static bed region, which is up to 0.4 m in the reactor and (ii) free bed Fig. 2 Computational domain for steam reactor

420 Table 1 Boundary conditions

Table 2 Numerical Parameters

A. Baidya et al. Computational face

Boundary condition

Reactor inlet

Velocity inlet

Reactor outlet

Pressure outlet

Reactor surface

Wall (no-slip)

Numerical parameters

Details

Convergence criteria

10−6

Time step

10−4 s

Number of rectangular cells

2500

region which is rest part of the steam reactor, i.e., 0.6 m. Initially, the static bed region is patched with metal oxide granules with volume fraction of 0.48 having no trace of metal oxide particles in free bed height. The heat transfer coefficient among the gaseous and solidus phase was described by Gunn [16]. For solid particles, collision coefficient was taken as a constant value of 0.88. The solid particles in the static bed height regime are provided with certain fluidization velocity for better mixing of gaseous and solidus phase. The model parameters are alike to that considered by Deng et al. [8] and are used for the base case in the current investigation.

3 Results and Discussions The hydrodynamics of chemical reaction in the SR has been modeled using a multiphase CFD model according to kinetic theory of granular flow. The kinetic reaction that occurs between fuel (steam) and oxygen carrier (iron oxide or manganese oxide) has been customized by properly incorporating a user-defined function (UDF) in Ansys FLUENT. The shape of the granules is considered spherical with smooth, regular, inelastic and mono-dispersed spheres. Temperature range for the reaction, which is happening between gaseous and solidus phase, was assumed to be 873– 1273 K. 100% w.t. of the water vapor (fuel) is supplied to the SR with constant inlet velocity from the distributor plate placed at the bottom of the reactor. The oxygen carrier was stored in static bed region of the reactor with some initial fluidization velocity. When the fuel supply is initiated from the bottom of the reactor, transfer of momentum occurs to the oxygen carrier, and at reaction temperature, nucleation reaction or activation sites start in the reactor [17]. Hydrogen (H2 ) is produced as the product of reaction occurring between oxygen present in the steam and reduced oxygen carrier. The irregular formation of activation sites, due to reactor temperature variation, strengthens the study of transient bubble dynamics in the reactor. Figure 3 depicts results of numerical study performed with similar parameters that was considered by Deng et al. [8] with alike geometry and numerical domain

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Fig. 3 Mole fraction in gaseous phase along centerline of the steam reactor (x = 0 cm) with iron oxide as oxygen carrier

by using H2 as fuel and CaSO4 as oxygen carrier. It displays the variance of gaseous phase in mole fraction at height of y = 30 cm, along the reactor center line, from the inlet in static bed height (Fig. 3a) and at exit of the reactor (Fig. 3b). The discrepancy between current findings and the results reported by Deng et al. [8] are expected to be because of second-order discretization scheme used in present case in contrast to first-order scheme used by Deng et al. [8] and the pressure outlet boundary condition at reactor outlet in present case against the outflow condition used by Deng et al. [8]. An abrupt drop in mole fraction of reactant from unity has been noticed, and it oscillates about 0.68 initially for time up to 1.0 s. But, the characteristic is noticed to be reversed for gaseous product mole fraction that increases from 0 to oscillate about 0.32. A quasi-steady state has been attained after 1.0 s, and mole fraction of H2 and H2 O is obtained to be 0.7 and 0.32, respectively. After 3.8 s, the quasi-steady state is attained at the outlet with mole fraction of H2 and H2 O being 0.65 and 0.35, respectively. The good concurrence between the results obtained from current study with Deng et al. [8] necessitates further simulations by assuming steam and iron oxide or manganese oxide as fuel and oxygen carrier, respectively, for analyzing transient phenomena and the aspect of different operating temperature in a chemical looping reforming technology.

3.1 Transient and Quasi-Steady Bubble Dynamics Figure 4 depicts the volume fraction contour for solidus phase inside the steam reactor over time period from 0 to 2 s. The shrinking core model has been used to estimate chemical kinetics for this reactor [18]. Steam is supplied to distributor plate with uniform velocity. Iron oxide granules were initially patched with fluidization

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Fig. 4 Unsteady phenomena of bubble development in steam reactor with iron oxide as oxygen carrier

velocity which is less than that of steam velocity in static bed region. Contours in Fig. 4 show generation, upsurge, expand and burst phenomena of bubbles. Reaction starts from static bed region where small bubbles generate near distributor plate in the bed region for initial 5 s. Then, these smaller bubbles rise up creating larger bubbles. These larger bubbles create low pressure zone in wake region and thus followed by newly generated smaller bubbles. Smaller bubbles get accelerated toward the large bubbles due to pressure difference and coalesce with large bubbles, which results in the acceleration of large bubble also. Consequently, size of the bubble enlarges in the limited flow passage with slug formation in steam reactor that forms two vertically off-set columns. The solid granules are continuously forced in upward direction by surging slug, but due to gravitational effect and difference in densities of gas–solid particles, solid particles descend on the centerline and on the wall between time period of 0.5–1.5 s. This phenomenon is also observed and addressed by Clift and Grace [19] experimentally. Figure 5 depicts the physical process of solid volume fraction profile from t = 1.1 to 10 s. This provides a fair explanation of transient and quasi-steady bubble dynamics in the SR. It displays that rates reaction are different for different regimes inside the reactor which is mainly because the fluidization velocity of solid granules. One more reason for different reaction rate is that there is variation in gas velocity in slug and the bubble. The overall inter-mixing of gaseous and solidus phase is accomplished with constant delivery of the fed-stream through distributor. The transient phenomena of reaction are examined in the time between 0 and 1.5 s, and the beyond 1.5 s reaction advances close to the quasi-steady state. Consequently, the center annulus region

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Fig. 5 Solid volume fraction contours of transient and quasi-steady process with iron oxide as oxygen carrier

starts diminishing, and overall inter-mixing between gaseous and solidus granules achieves quasi-steady state.

3.2 Effect of Temperature on the Fuel Conversion Rate Contour lines for the solid volume fraction inside the SR are depicted by Fig. 6 for the temperature range 873–1273 K with oxygen carrier for MnO (Fig. 6a) and FeO (Fig. 6b) at the quasi-steady state up to time period of 40 s. The sizes 0.4, 0.6 and 0.25 m are kept constant for dense bed height, free bed height and width of steam reactor, respectively, for the present case. The temperature values for the current simulations are 873, 973, 1073, 1173 and 1273 K. Contours show that the conversion rate is high at higher temperatures which is because of the reason that mole fraction of steam decreases at reactor outlet in proportion to temperature increase in the reactor. The reaction temperature plays a key role for the successful completion of the any fluidized bed reactors as described by Hossain [18]. Induction period inside steam reactor decreases as the reactor temperature increases. As the temperature increases in the reactor, it results in the decrement in the induction time which ultimately causes more activation sites to form in the same duration. Because of this, it is concluded that increase in operating temperature will result in increased conversion rate. It is also noticed that fuel conversion rate for CLR process with manganese oxide as oxygen carrier is higher than the process with iron oxide as oxygen carrier.

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Fig. 6 Temperature effect on transient and quasi-steady process in CLR system with a manganese oxide b iron oxide as oxygen carrier

4 Conclusion An Eulerian multi-phase model is implemented to interpret the continuum principle of dual fluid model for gaseous and solidus phase model. The bubble hydrodynamics in terms of developing of bubbles, rising, growing and bursting in the steam reactor has been studied for transient and quasi-steady behavior. The conversion rate and mole fraction of steam have also been considered for various oxygen carrier particles with varying range of operating temperatures. In the current study, it is remarkable that transient bubble dynamics lasts for 0–2 s. Beyond this time period, bubble columns diminish because of differential density between gaseous and solidus phase. The conversion rate in the SR is noticed to increase with the increase in temperature that causes proper inter-mixing of gaseous and solidus phases with the temperature variation between 873 and 1273 K. The fuel conversion rate for CLR process with manganese oxide as oxygen carrier was found to be higher than the process with iron oxide as oxygen carrier.

References 1. Heidary, H., Abbassi, A., Kermani, M.J.: Enhanced heat transfer with corrugated flow channel in anode side of direct methanol fuel cells. Energ. Convers. Manag. 75, 748–760 (2013) 2. Thanaa, F., Eskander, M.N., El-Hagry, M.T.: Energy flow and management of a hybrid wind/PV/fuel cell generation system. Energ. Convers. Manag. 47, 164–180 (2006) 3. Momirlan, M., Veziroglu, T.: Recent directions in world hydrogen production. Renew. Sustain. Rev. 3, 219–231 (1999) 4. Gelderbloom, S.J., Gidaspow, D., Lyczkowski, R.W.: CFD simulations of bubbling/collapsing fluidized beds for three geldart groups. AlChE J. 49, 844–858 (2003)

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5. Richter, H.J., Knoche, K.F.: Reversibility of combustion processes. ACS Symp. Ser. 23, 571– 585 (1983) 6. Harichandan, A.B., Shamim, T.: CFD analysis of bubble hydrodynamics in a fuel reactor for a hydrogen-fuled chemical looping combustion system. Energ. Conserv. Manag. 86, 1010–1022 (2014) 7. Rydén, M., Lyngfelt, A., Mattisson, T.: Production of H2 and synthesis gas by chemical-looping reforming. In: The Eight International Conference on Greenhouse Gas Control Technologies, Trondheim, Norway (2006) 8. Deng, Z., Xiao, R., Jin, B., Song, Q.: Numerical simulation of chemical looping combustion process with CaSO4 oxygen carrier. Int. J. Greenhouse Gas Contr. 3, 368–375 (2009) 9. Khan, M.N., Shamim, T.: Investigation of hydrogen in a three reactor chemical looping reforming process. Appl. Energ. (2015) 10. Fan, L.S., Zeng, L., Wang, W., Luo, S.: Chemical looping processes for CO2 capture and carbonaceous fuel conversion—prospect and opportunity. Energ. Environ. Sci. 5, 7254–7280 (2012) 11. Tang, M., Xu, L., Fan, M.: Progress in oxygen carrier development of methane-based chemicallooping reforming: a review. Appl. Energ. 151, 143–56 (2015) 12. Protasova, L., Snijkers, F.: Recent developments in oxygen carrier materials for hydrogen production via chemical looping processes. Fuel 181, 75–93 (2016) 13. Mattisson, T., Lyngfelt, A., Cho, P.: The use of iron oxide as an oxygen carrier in chemicallooping combustion of methane with inherent separation of CO2 . Fuel 80, 1953–1962 (2003) 14. Ryu, H.J., Gin, G.T.: Chemical-looping hydrogen generation system: performance estimation and process selection. Korean J. Chem. Eng. 24(3), 527–531 (2007) 15. Mahalatkar, K., Kuhlman, J., Huckaby, E.D., O’Brien, T.: Computational fluid dynamic simulation of chemical looping fuel reactors utilizing gaseous fuels. Chem. Eng. Sci. 66, 469–479 (2011) 16. Gunn, D.J.: Transfer of heat or mass to particles in fixed and fluidized beds. Int. J. Heat Mass Transf. 21, 467–476 (1978) 17. Cho, W.C., SeO, M.W., Kim, S.D., Kang, K.S., Bae, K.K., Kim, S.H.: Reactivity of iron oxide as an oxygen carrier for chemical-looping hydrogen production. Int. J. Hydrogen Energ. 37, 16852–16863 (2012) 18. Hossain, M.M., Lasa, H.I.: Chemical-looping combustion (CLC) for inherent CO2 separation— a review. Chem. Eng. Sci. 63, 4433–4451 (2008) 19. Clift, R., Grace, J.R.: Continuous Bubbling and Slugging. London: Academic Press (1985)

Enhancement in Product Value of Potato Through Chemical Pre-treatment and Drying Process M. B. Gorawar , S. V. Desai , V. G. Balikai , and P. P. Revankar

1 Introduction The agricultural product preservation plays a key role as a post-harvesting strategy to enhance shelf life without loss of nutrients. The agricultural sector is reeling under major losses due to poor post-harvest techniques that have led to every fifth portion of harvested product lost either to rodents or microbial decay due to high moisture in food products. The moisture content, air flow rate, drying air temperature, relative humidity and pre-treatment process are important parameters governing drying process. Agricultural products are made preservation-ready using methods like drying, chemical processing and heat treatment. The open sun drying is most common and widely used drying method across the world. It involves spreading crop on open ground for sun drying through exposure to natural convective air until moisture reduces to a level that inhibits growth of microorganisms like microbial infestation. This mode of drying has unregulated solar insolation, intermittency of solar energy and contaminations due to direct exposure. The well-designed preservation techniques based on electric drying and indirect type solar drying help store food products for longer time, minimize the wastages and transport for longer distance without spoilage. The characterization of drying process is important to obtain parameters that yield hygienic products in lowest possible drying time and drying cost. The microbiological studies indicate hygiene level of dried product that is essential to ascertain suitability and safety aspects of dried product.

M. B. Gorawar · V. G. Balikai · P. P. Revankar (B) School of Mechanical Engineering, KLE Technological University, Hubballi, India e-mail: [email protected] S. V. Desai Department of Biotechnology, KLE Technological University, Hubballi, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_41

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2 Literature Review The pre-treatment process reduced enzymatic activities in potato slice that resulted in surface discoloration of potato slices due to melanin formation. The 2% ethanol produced darker color, while texture was lost on account of acetic acid and ethanol that rehydrated potato slice [1]. Tripathy and Kumar analyzed dehydration rate in potato slice of cylindrical geometry with thickness 0.01 m and diameter 0.05 m under unsteady state that led to enhanced convective heat transfer coefficient due to larger surface area exposed to convective fluid. The specific energy consumption was higher for initial hours of drying and gradually decreased as it progressed owing to resistance for initial moisture removal on account of unbroken surface that limited moisture migration [2]. Bacelos and Almeida developed finite control volume model to analyze shrinkage and internal resistance to mass transfer in spherical potato of 10 mm diameter. The drying model that neglected shrinkage factor exhibited a good agreement with experimental data during early drying period, this trend however overestimated drying parameters for the later stage of drying process against observed experimental data [3]. Chayjan investigated drying of potato slice using different modes that included fixed thin layer, semi-fluidized and fluidized bed under laboratory conditions in the range of 40–70 °C. The moisture diffusivity directly related to temperature with activation energy between 15.88 and 24.95 kJ/mol [4]. Shekofteh et al. experimented on operating parameters for shrinkage of potato slices through indoor tests at 60, 70 and 80 °C of drying air temperature obtained using variable heat source (electric lamp) for air velocity in range of 0.5–1 m/s. The drying air temperature significantly influenced drying rate and shrinkage of potato slice as compared to changes in airflow velocity [5]. Tesfamichael et al. designed and developed natural convective solar crop dryer to analyze drying characteristics of potato slices. The clear sky days witnessed heated air at 14–29 °C temperature above ambient air temperature. The drying yielded better quality on account of avoidance of insects and contamination apart from 30% lower drying time than open sun drying system at similar climatic conditions [6]. Darvishi et al. discussed effect of heat supplied by microwave dryer in potato drying with respect to shape, energy consumption and energy efficiency. It was observed that with increase in input heat, the drying rate in cylindrical and rectangular geometry potato slices increased by up to 56% and 42%, respectively. However, cylindrical slices required 25% higher specific energy compared to rectangular slices [7]. Dagde and Nmegbu analyzed effect of temperature on drying of potato slices in batch tray dryer on basis of energy balance equation and appropriate boundary conditions. The slice surface temperature increased, while moisture content decreased as drying progressed in good agreement with published literature [8]. Naderinezhad et al. analyzed potato slice drying in hot convective air dryer at varied air temperatures between 45 and 70 °C and air velocities of 1.60 and 1.81 m/s. The air temperature significantly influenced drying rate of potato slices to the tune of about 25% lower drying time for increase by 10 °C. The drying rate for square slices was more than that for other shapes of potato slices

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[9]. Bundalevski et al. investigated kinematics of potato slice drying in vacuum farinfrared air temperature of 120–200 °C in the pressure range between 20 and 80 kPa. The results compared well with reported drying models that indicated direct relation of drying time with operating temperature and inversely to air pressure. The performance index, coefficient correlation, low chi-squared and RMSE values were reported in the study [10]. Amjad et al. assessed energy utilization, energy utilization ratio and exergy of single layer and split layer potato slice (thickness 5 and 8 mm) dried in temperature range of 55–65 °C using diagonal batch type electric dryer [11]. Adeniyi et al. investigated microbiological factors of processed potato fermented in 2% brine solution. The nutritive qualities of brine fermented samples were analyzed, and organoleptic parameter was accessed through trained panelist [12]. Amany et al. have investigated dried samples in the form of slices of common potato varieties like sponta, glactica, valor and leady through sensory evaluation for assessment of their food value. The dried potato samples were processed into edible food product after frying in sun flower oil at 180 ± 5 °C. The test samples were obtained every day for five consecutive days for conduct of organoleptic tests on fried chips. The samples of sunflower oil used in frying of dried samples of potato chips were also subjected to physicochemical assessment. Duran et al. tested microbiological quality of retail products of frozen hash brown, dried hash brown with onions, frozen French fried, dried instant mashed and potato salad. The colony counts per gram of dried product were, respectively, 270, 580 and 78 for hash brown potato, frozen hash brown potato with onion and frozen French fried potato. The instant mashed potatoes and potato salad had geometric mean values for aerobic plate counts per gram in cfu/ml as 3 log10 and 3.6 log10 , respectively [13].

3 Material and Pre-treatment Process The potato slices were washed in water and peeled with clean steel knife before immersion into solution containing NaCl (2%) for 5 min. The peeled potatoes sliced to 4–5 mm thickness are subjected to blanching process (immersed in boiled water for 5–10 min) and cooled before being treated in solution of NaCl (2%), Citric acid (0.2%) and Potassium meta-bisulphate (0.2%). The flowchart in Fig. 1 reflects the steps in potato slice pre-treatment. The chemical pre-treatment of potato induces desirable properties that make moisture removal better without any deterioration in product hygiene. Two identical control samples of sliced potato were prepared for study on effect of chemical pre-treatment on drying parameters in electric cabinet dryer. The study investigates choice of appropriate air temperature, air flow rate and chemical pretreatment in order to gain suitable product properties at accelerated drying of potato.

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Fig. 1 Flowchart of chemical pre-treatment for potato drying

4 Details of Experimental Setup for Electrical Drying The experimental setup used for study on crop drying as indicated in Figs. 2 and 3 consists of an electric blower, orifice, and electric air heater along with a drying chamber (cabinet) for removal of moisture from the product to be dried.

Fig. 2 Line diagram of experimental setup of cabinet drying unit

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Fig. 3 Experimental setup installed for investigation

The experimentation investigation involved two stages of sample preparation and drying. The primary stage had water washing of potato that removed impurities before peeling operation using clean steel knife. The peeled potatoes were soaked in cold water containing 2% NaCl solution (by volume) for about 5 min followed by their sizing to circular shape. The subsequent blanching in hot water (80 °C) for about 5–10 min was followed by its cooling to ambient temperature. The blanched potato slices were immersed in water containing metered quantity of NaCl (2%), Citric acid (0.2%) and Potassium meta-bisulphate (0.2%) (KMS) under controlled condition. The pretreated test samples were washed in clean water and spread in single layer on stainless steel tray with nylon mesh for drying as shown in Fig. 4a–d. The experimental investigations used electric crop dryer to predict drying characteristics of potato slice in terms of moisture ratio, drying rate and drying time for regulated drying air temperatures of 60, 80 and 100 °C, and three airflow rates of

a. Fresh potato

b. Peeling and NaCl treatment

c. Slicing and Blanching

d. Spreading after treatment

e. Final dried sample

f. Packed sample

Fig. 4 Experimental stages of drying potato

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0.015, 0.02 and 0.025 kg/s delivered through the variable speed air blower. The initial cabinet moisture was eliminated before each set of observation through the no-load test conducted at specified air temperature, and airflow rate for 30 min duration with an empty stain steel tray (known weight) was placed in drying cabinet.

5 Results and Discussion This section discusses important experimental observations made with respect to drying characteristics of potato slice and microbiological analysis of dried potato slice. The study includes variation of relative humidity, drying cabinet outlet temperature, drying time and weight reduction. The tests were conducted at constant drying chamber inlet temperature for various air mass flow rate (0.015, 0.02 and 0.025 kg/s) and vice versa for constant air mass flow rate at varying drying chamber inlet temperature (60, 80 and 100 °C).

5.1 Drying Characteristics of Potato Slice Figure 5 depicts variation in % moisture reduction with drying time for the air flow rate of 0.015 kg/s. It was observed that the % moisture reduction was higher during initial drying period for all air temperatures, due to the presence of higher loose moisture content on surface skin of potato slice. After removal, this surface moisture drying rate momentarily decreased and later increased during later phase of drying cycle. The drying rate strongly depends on molecular diffusivity of potato slice. The higher drying air temperature increased moisture reduction due to higher dispersion Fig. 5 Temperature dependence of % moisture reduction in potato slice drying

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in air molecules; however, relative change was more for rise in air temperature from 60 to 80 °C as compared air temperature rise from 80 to 100 °C. The potato slices attained desired moisture content within drying time durations of 7 h, 8 h and 10 h, respectively, for air temperatures 100, 80 and 60 °C that indicated strong influence drying air temperature on drying rate. The general trend indicated that weight reduction of potato slice was accelerated with increase in the mass flow rate of heated air on account of faster heat removal and better movement of the moisture particles after release from the potato surface. Results suggested that inlet temperature of 100 °C with a mass flow rate of 0.025 kg/s yielded fastest drying rate of 4.5 h for the 0.4 kg sample of potato. The drying time in case of 60 and 80 °C inlet air temperature was, respectively, higher by 22 and 11% for the identical mass flow rate of 0.025 kg/s. Similarly, in case of lower mass flow rate of air (0.015 kg/s), the drying time was higher by 50% and 28%, respectively, for 60 and 80 °C inlet air temperature as compared to 100 °C inlet air temperature that had a drying time of 6 h. The experiments were conducted on chemically treated potato slice and untreated slice of same geometry under similar operating conditions, 0.02 kg/s and at the air temperature of 60 °C to investigate the effect of chemical treatment on drying time, and results are presented in Fig. 6. However, quality of chemical treated slice in terms of color and texture was remarkably better that untreated slices as shown in Fig. 6 due to higher enzymatic activities and larger shrinkages. The nature of drying any agricultural product is explained by the generalized exponential drying curve with time-dependent moisture removal M(t) to be a function of drying constant ‘k’ as represented by Eq. 1. M(t) = Mo exp[−kt]

(1)

The drying constant is specific to nature of dried product and the drying conditions of temperature, humidity and air flow rate. Fig. 6 Weight reduction in CT/UT potato slice

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Table 1 Temperature dependence on drying characteristics of potato slice S. No. 1

Temperature (°C) 60

2

80

3

100

Mo

k day−1

R2

M(t) = 28.451

[exp−0.274 t ]

28.451

0.274

0.7454

M(t) = 40.932

[exp−0.371 t ]

40.932

0.371

0.6513

M(t) = 25.723 [exp−0.172 t ]

25.723

0.172

0.7909

Drying equation

Table 2 Influence of chemical treatment on drying characteristics of potato slice S.No.

Drying mode

Drying equation

Mo

k, h−1

R2

1

CT

M(t) = 404.58 [exp−0.203 t ]

404.58

0.203

0.9838

UT

[exp−0.201 t ]

415.78

0.201

0.9797

2

M(t) = 415.78

Fig. 7 Comparison of dried potato slices

a. UT sample

b. CT sample

Table 1 indicates the influence of temperature on the drying characteristics of potato as depicted through the drying constant that suggest accelerated drying at higher temperature. Similarly, the influence of chemical treatment on moisture removal as depicted in Table 2 strongly favors chemical treatment that has marginally higher rate along with hygienically acceptable end product as shown in Fig. 7a, b.

5.2 Microbiological Analysis of Dried Products The microbiological analysis of potato drying for bacterial and fungal (yeast and mold) count was performed and compared with different inlet temperatures and mass flow rate. The nutrient agar (NA) and potato dextrose agar (PDA) were used to culture bacteria and fungi, respectively.

5.2.1

Total Plate Count Method for Bacterial Infestation

The plate count technique used routinely measured bacteria containing samples that were cultured on nutrient agar medium visible in the form of clustered colony making

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Table 3 Bacterial count for potato slice Dried product Potato slice

Temperature (°C)

Colony count, (log10 colony forming units/g) 0.015 kg/s

0.02 kg/s

0.025 kg/s

60

3.9

3.2

2.9

80

2.7

2.3

2.1

100

1.7

1.5

1.2

it possible to obtain their quantitative measure. The physical count on the number of colonies developed directly indicated the number of organisms in the sample. The serial dilution was executed cumulatively by transferring the known volume of first dilution to second dilution blank. The process was successively proceeded on to third, fourth, fifth and sixth dilution blanks. Once diluted, the specified volume of dilution sample (1 or 0.1 ml) from various dilutions was added to sterile petri plates (in duplicate for each dilution) to which molten and cooled (45–50 °C) agar medium was added. The colonies were counted on a colony counter.

5.2.2

Procedure for Microbiological Analysis

Microbiological analysis of cabinet dried potato slice for total viable count was performed with all experiments were carried out in duplicates. Analysis of bacteria and yeasts and molds was performed by pour plate and spread plate method on nutrient agar and potato dextrose agar, respectively. The nutrient agar plates were incubated at 37 °C for 24–48 h, and potato dextrose agar plates were incubated at ambient room temperature for 3–4 days (Table 3).

5.2.3

Sensory Evaluation of Fried Potato Chips for Organoleptic Properties

The final stage to ensure the acceptance of dried product termed as sensory evaluation was carried out on chemically treated dried potato slice with the help of expert panelist to analyze effect of drying parameters on product quality. For sensory evaluation, the potato slice was fried at 180 ± 5 °C with sunflower oil. Each sample was randomly numbered and presented to the panel which consists of twelve inexperienced members to gauge its utility as a food item. The response of the participants on sensory quality of product was collected through the questionnaire designed on a ‘one to four scale’ to assess the attributes of taste, texture, color odor and overall appearance. The designed scale was rated as (1) Dislike, (2) Acceptable, (3) Like and (4) Like very much.

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Table 4 Results of sensory evaluation (on a scale of 4) CT

Taste

Texture

Color

Odor

Overall appearance

Sample 1

3

3.5

3.8

4

3.9

Sample 2

3

3.5

3.7

4

3.7

Sample 3

3

3.5

3.6

4

3.6

Sample 1

Sample 2

Sample 3

Table 4 summarizes sensory evaluation results for three CT samples (indicated 1, 2 and 3) of potato dried at a temperature of 80 °C with flow rates of 0.015, 0.020 and 0.025 kg/s that showed positive sign in terms of all the sensory parameters considered. Hence, it was concluded that drying process devoid of chemical pre-treatment was not suitable owing to discoloration and poor hygiene value of final product.

6 Conclusions • The air inlet temperature and mass flow rate strongly influenced drying time as per the experimental observations. The drying air temperature change from 60 to 100 °C had drying time reduced from 9 to 6 h for 0.015 kg/s airflow rate as against reduction in drying time from 7 to 4.5 h for 0.02 kg/s air flow. • As mass flow rate of air was increased from 0.015 to 0.025 kg/s, the drying time is reduced from 9 h, 8 h, and 6 h to 7 h, 6 h and 4.5 h, respectively, for 0.02 kg/s. • The microbiological analysis of dried product favored adoption of appropriate preservation strategy on the basis of drying characteristics like air inlet temperature and mass flow rate. The increase in drying temperature decreased the microbial activity, but however, the upper limit was fixed on the basis of safe drying temperature of the product to ensure retention of its nutritional value and equilibrium moisture content. • The lowest temperature studied with respect to drying (60 °C) indicated a high bacterial count of 2.9–3.1 log10 cfu/ml which was not considered as good microbiological quality. However, since these are intermediate products, which need to be processed by frying before their consumption, it is of less concern in regard to health. The counts tend to further decrease upon subjecting to frying.

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• The test on food quality of finished product with respect to CT samples of potato slice revealed the absence of yeast and mold in the final product; however, the dried versions of UT potato samples showed growth of mold leading to discoloration as against the CT samples that exhibited bright color. • The intangible results in terms of improvement in the farming sector have not been quantitatively evaluated in this study, but however, the impact is significant owing to the crisis like situation faced due to successive failure of good monsoon in India.

References 1. Ezekiel, R., Rani, M.: Effect of pre-dehydration chemical treatment on enzymatic discoloration and rehydration of solar dehydrated potato slices and cubes. Potato J. 33(3–4), 104–109 (2006) 2. Tripathy, P.P., Kumar, S.: Modeling of heat transfer and energy analysis of potato slices and cylinders during solar drying. Appl. Therm. Eng. 29, 884–891 (2009) 3. Bacelos, M.S., Almeida, P.I.F.: Modelling of drying kinetic of potatoes taking into account shrinkage. Procedia Food Sci. 1, 713–721 (2011) 4. Chayjan, R.A.: Modeling some drying characteristics of high moisture potato slices in fixed, semi fluidized and fluidized bed conditions. J. Agric. Sci. Technol. 14, 1229–1241 (2012) 5. Mohammad, S., Eskandari, C.F., Soheila, K., Yasin, H.: Study of shrinkage of potato sheets during drying in thin-layer dryer. Res. J. Appl. Sci. Eng. Technol. 4(16), 2677–2681 (2012) 6. Aklilu, T., Abebayehu, A.: Experimental analysis of potato slices drying characteristics using solar dryer. J. Appl. Sci. 13(6), 939–943 (2013) 7. Hosain, D., Hamid, K., Ahmad, B., Mehdi, L.: Effect of shape potato chips on drying characteristics. Int. J. Agric. Crop Sci. (2013). ISSN 2227-670X 8. Dagde, K.K., Nmegbu, C.G.J.: Mathematical modeling of a tray dryer for the drying of potato chips using hot air medium. Int. J. Adv. Res. Technol. 3(7) (2014). ISSN 2278-7763 9. Naderinezhad, S., Etesami, N., Najafabady, A.P., Falavarjani, M.G.: Mathematical modeling of drying of potato slices in a forced convective dryer based on important parameters. Food Sci. Nutr. 4(1), 110–118 (2016) 10. Bundalevski, S., Mitrevski, V., Lutovska, M., Geramitcioski, T., Mijakovski, V.: Experimental investigation of vacuum far-infrared drying of potato slices. J. Process. Energ. Agric. 19(2), 71–75 (2015) 11. Amjad, W., Hensel, O., Munir, A., Esper, A., Sturm, B.: Thermodynamic analysis of drying process in a diagonal-batch dryer developed for batch uniformity using potato slices. J. Food Eng. 169, 238–249 (2016) 12. Basuny, A.M.M., Mostafa, D.M.M., Shaker, A.M.: Relationship between chemical composition and sensory evaluation of potato chips made from six potato varieties with emphasis on the quality of fried sunflower oil. World J. Dairy Food Sci. 4(2), 193–200 (2009) 13. Oliveira J.V., Alves M.M., Costa J.C.: Optimization of biogas production from Sargassum sp. using a design of experiments to assess the co-digestion with glycerol and waste frying oil. Biores. Technol. (14), 01553–3 (2010). ISSN 0960-8524

Desalination Using Waste Heat Recovery with Active Solar Still Sandeep Kumar Singh, S. K. Tyagi, and S. C. Kaushik

Nomenclature Gr hcw hew md Pr pg pw qew Tw Tg CPCB ICMR ISI Nu USEPA WHO NR

Grash of number Convective heat transfer coefficient (W/m2 K) Evaporative heat transfer coefficient (W/m2 K) Distillate output (kg/s) Prandtl number Partial saturated vapor pressure at glass temperature (N/m2 ) Partial saturated vapor pressure at water temperature (N/m2 ) Rate of evaporative heat transfer (W/m2 ) Water temperature (K) Glass temperature (K) Central Pollution Control Board Indian Council of Medical Research Indian Standard Institution Nusselt number United States Environmental Protection Agency World Health Organization No relaxation

1 Introduction Nearly 71% area of the total earth’s surface is covered with water, though it is hard to complete the demand of all human and habitats. Approximately, 2.5% of freshwater S. K. Singh · S. K. Tyagi (B) · S. C. Kaushik Indian Institute of Technology Delhi, New Delhi, Delhi 110016, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_42

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is available, mainly in the form of groundwater, glaciers, and ice caps, and in that, accessible freshwater surface is only 0.008% [1]. Water scarcity counts when the supply of water supply goes below 1000 m3 /person per year [2]. Currently, one-third of total world resident’s population experience drastic water problem and anticipated to an escalation in percentage [3]. Not only India but also in many areas of the USA, increase in municipal contamination and private well for drinking water, primarily due to extensive use of fertilizers, as well as waste from human and animal, has been documented. Many people suffered from cholera, jaundice, and water-borne diseases. One of the primary approaches to reduce the water shortage is a desalination process. Through this process, potable water from saline or brackish water can be produced [4]. Conventionally, seawater desalination requires lots of energy intensive, is more expensive, and discharges an enormous amount of high salinity brine [5]. Salinity depends upon the total dissolved solids (TDS); for brackish water and seawater, TDS is up to 10,000 ppm and 45,000 ppm, respectively [6]. Permissible limit of drinking water quality is expressed in Table 1 [7]. By desalination, 1000 m3 of water can be produced in a day, but it requires extensive energy, about 10,000 tons of fossil fuel per year [8]. To decrease the carbon footprint and the emission of greenhouse gases, the use of renewable and sustainable energy resources is crucial because these gases are the main reasons for climate change and global warming. Solar energy is unable to provide continuous Table 1 Potable water quality range [7] Parameters pH

(kg/m3 )

WHO ×

10−3

Turbidity NTU Fluoride

(kg/m3 )

×

10−3

CPCB

ISI

USEPA

ICMR

(6.5–8.5)

(6.5–8.5)

(6.5–8.5)

(6.5–8.5)

(6.5–9.2)

−

10

10

−

25 1.5

1.5

1.5

0.6–1.2

4.0

Alkalinity (kg/m3 )

−

0.6

−

−

−

Total hardness (kg/m3 )

0.5

0.6

0.3

−

0.6

Calcium (kg/m3 ) × 10−3

75

0.2

75

−

0.2

Chlorides (kg/m3 )

0.2

1

0.250

0.250

1

Lead (kg/m3 ) × 10−3

0. 05

NR

0.10

−

0.05

−

−

0.2

−

−

Chromium (kg/m3 )

−

NR

0.05

0.1

−

Magnesium (kg/m3 ) × 10−3

50

0.1

30

−

−

Residual

(kg/m3 )

free

−

NR

−

−

−

Sulfate (kg/m3 )

−

0.4

0.15

−

0.4

Iron (kg/m3 ) × 10−3

1.0

E. coli (MPN/ 0.1

m3 )

0.1

1.0

0. 3

−

(kg/m3 )

−

0.1

45

−

0.1

Copper (kg/m3 ) × 10−3

1.0

1.5

0.05

1.3

1.5

Conductivity (kg/m3 )

−

2000

−

−

−

5.0

15.0

5.0

−

0.10

Nitrate

Zinc

(kg/m3 )

×

10−3

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operation of systems due to its intermittent nature. Agricultural wastes such as rice husk and bagasse are a potential source of energy which if unutilized is liable to cause severe environmental pollution crisis. Biomass being a CO2 neutral fuel helps in reducing the carbon footprints if combustion is clean and hence is an emerging field in the area of solarbiomass hybrid desalination systems. With the combination of solar field, biomass fired boiler at a temperature less than 90 °C can operate desalination plant after the sunshine hour.

2 Desalination Technologies Desalination is a procedure to take away the important mineral form the brackish or saline water. Nearly, 1% of the world’s total population depends upon the water obtained from desalination, but it is expected from the United Nation that almost 14% of the world population will face scarcity of water by 2025 [9]. The process of desalination consumes lots of energy and also has some adverse effect on the atmosphere. Conventional desalination units run on fossil fuel which contributes to greenhouse gas (GHG) emissions. Use of renewable energy can prevent depletion of fossil fuel that is non-renewable in nature, which has motivated the researcher to search for an option to operate the desalination plant by using the energy from a renewable source [10], and different solar distillation systems shown in Fig. 1. It is estimated that approximately, 80% of the total world’s desalination capacity is delivered by two technologies: Reverse osmosis (RO) and multi-stage flash (MSF). Almost 40% of the total world’s desalination capacity is covered by the Middle East, and they widely use MSF (particularly in Kuwait, UAE, and Saudi Arabia) [11]. MSF and multi-effect desalination (MED) procedures comprise a set of stages at continuously decreasing pressure and temperature. In MSF, reduction of pressure takes place suddenly when saline water enters into the evacuated chamber followed by vapor generation, and with the decreasing pressure, this process occurs repetitively. The steam nearly at a temperature of 100 °C is supplied externally, which is primarily required for the process to occur. In MED, the generation of vapor takes place by thermal energy

Fig. 1 Classification of solar distillation systems

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absorption from seawater. This generated steam in the first stage will heat the salt solution in the succeeding stage because the following stage is at lower pressure and temperature. Process performance depends upon the number of stages and effects. In mechanical vapor compression (MVC) and thermal vapor compression (TVC), the production of vapor can be increased by compressing the vapor which is generated from the initial saline solution either mechanically or thermally. Reverse osmosis has no limitation; it can desalt seawaters or brackish water, whereas electrodialysis has some limitation; usually, it is only used for brackish feed water [12]. Different desalination technologies are as follows: For selecting the particular desalination process, several factors [13] have to be considered, such as: a. b. c. d. e. f. g. h. i.

Saline water treatment requirements. The simplicity of operation and robust criteria. Compact size and low maintenance. The capital cost of the equipment and material. The effectiveness versus energy consumption of the selected process. Required land area for the equipment installation. Interest, approval, and local support with the least changes to the societal sphere. The relevance of the process with the solar energy application. The required quantity of potable water with the application of several desalination processes.

3 Experimental Setup The proposed experimental setup shown in Fig. 2 consists of a solar still, pelletbased cookstove, and a tank situated at an altitude. Tank filled with brackish water

Fig. 2 Solar desalination system with waste heat recovery from cookstove

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Fig. 3 Solar still mechanism

has a certain level of salinity, total dissolved solids, and impurities of that specific area. Water is supplied by gravitation to the water jacket surrounding biomass pelletbased cookstove, and after gaining sensible heat, it is supplied to the solar still. Solar still during daytime works as a desalinated unit, and at nighttime, it works as a condensing unit. The biomass cookstove has an outer and inner diameter of 0.180 m and 0.125 m, respectively, having height 0.350 m and made of mild steel. The feed water is naturally circulated to the water jacket under a controlled condition which is situated above the height of cookstove. In Fig. 3 a solar still with darkened basin filled with saline water or brackish water at a confined depth. It is roofed by an inclined transparent glass for solar radiation transmission and condensation which is due to the temperature difference between glass and basin temperature. The blackened liner is heated up by the solar energy which is entering the basin, and evaporation of water takes place. Due to partial difference in pressure and temperature, condensation of the water vapor takes place on inclined glass cover and collected at the bottom by providing the appropriate provision. In case of conventional still basin, the condensate of high quality obtained which is 2–3 m3 per unit area (m2 ) per day [14], this is the daily minimum requirement of an adult person as mentioned by the WHO [15, 16]. Solar desalination technology is one of the most suitable technologies for remote area dwellers because its construction is economical and has very low maintenance. It is used for evaporating brackish water to obtain potable water. Evaporation takes place by using solar heat from the sun, leaving behind the residue of salt and other contamination. The vapor from the evaporated water condenses on the surface of the cover and is collected as a fresh distilled water. Solar still basin type’s yield is given [12] by: md =

qew × Aw h fg

qew = h ew × (Tw − Tg )

(1) (2)

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h ew = 16.273 × 10−3 × h cw ×

( pw − p g ) (Tw − Tg )

(3)

The convective heat transfer coefficient is given by Duncle’s relation as 

h cw

( pw − pg ) × (Tw − Tg ) = 0.884 × (268.9 × 103 − pg )

 13 (4)

Among the 1.21 billion Indians, 0.833 billion live in rural areas, while 0.377 billion stay in urban areas, and approximately 75.1% household still use solid fuel for biomass cookstove [17], made from sawdust, coconut husk, agro waste, and palm waste, etc. The thermal imaging test has been carried out as shown in Fig. 4, using infrared camera (FLIR A325sc, spectral range 7.5–13.0 µm, standard temperature range 0–350 °C) for real-time assessment of outer surface temperature [18]. It has been found that a significant amount of heat is released from cookstove wall, which can be utilized in the water jacket to fulfill the heating requirement. During off-sunshine hours, the solar still acts as a condenser unit with the use of the biomass cookstove unit. A water jacket is to be fabricated around the cookstove as per the simulation results obtained from SOLIDWORKS 2017 premium [19]. Simulation is carried out at a variable mass flow rate and the diameter of the water jacket (Table 2). Total heat available/waste heat at the wall of the cookstove is given as: For all value of Gr and Pr at a constant heat flux ⎤2

⎡ 0.167

0.387(Gr Pr) ⎥ ⎢ Nu = ⎣0.82 +   0.492 0.5625 0.296 ⎦ 1 + Pr

Fig. 4 Thermal imaging test of cookstove during cooking

(5)

Desalination Using Waste Heat Recovery with Active Solar Still Table 2 Biomass cookstove wall temperature range

S. No.

Height range (m)

445 Temperature (°C)

1.

Above 0.30

350

2.

Between 0.25 and 0.30

340

3.

Between 0.15 and 0.25

220–315

4.

Between 0.00 and 0.15

105–115

Gr =

g β(Tmax − Tmin )L 3 υ3

(6)

Total heat available can be calculated as: Q = U AT

(7)

4 Results and Discussion The results have been retrieved from the simulation for various mass flow rates such as 0.0400, 0.0500, and 0.0600 kg/s, and the thickness of the water jacket considered is 0.006 and 0.008 m from the outer diameter of the cookstove for the same mass flow rate, so the total diameter with different thickness is 0.192 m and 0.196 m, respectively. The mean average temperature (T mean ) is 187.5 °C. At this mean average temperature and atmospheric pressure, the properties of the gases have been taken and the total heat available at the wall of the cookstove is 344 W. The following results obtained are shown in Fig. 5. 0.006 m

400

0.008 m

Temperature (°C)

350 300 250 200 150 100 50 0 0.004 kg/s

0.005 kg/s

Mass flow rate of water

Fig. 5 Gain in temperature at a different mass flow rate and thickness

0.006 kg/s

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The thickness of water jacket should be 0.008 m with the mass flow rate of 0.05– 0.06 kg/s, to fulfill the temperature requirement of the solar still which is around 50–70 °C. With this hybrid system, the productivity of solar still will be enhanced.

5 Conclusions In the proposed system, the effect of varying mass flow rate and inlet feed water temperature which is supply to the still was analyzed, and the following conclusion can be drawn from the present study: a. Waste energy utilized by the flowing water in the hybrid system for evaporation. b. When the mass flow rate is minimum, the heat transfer linearly increases and vice versa, so the average value of the mass flow rate is 0.055 kg/s. c. Increase in water jacket thickness will decrease the temperature of water which supplies to the still, so it should not go beyond 0.008 m. d. A system is able to run during off-sunshine hours. e. Able to fulfill the water requirement of a small family. For the people living below the poverty line, if the government helps by some initial investment, so this section of the population may get access to drinkable water at almost zero cost.

References 1. Wong, K.V., Pecora, C.: Recommendations for energy–water–food nexus problems. J. Energ. Res. Technol. 137(7), 32002 (2015) 2. Rijsberman, F.R.: Water scarcity: fact or fiction? Agric. Water Manag. 80(1–3), 5–22 (2006) 3. Jimenez-Cisneros, B.: Responding to the challenges of water security: the eighth phase of the international hydrological programme, 2014–2021. In: Proceedings of the 11th Kovacs Colloquium, Paris, France, vol. 366, pp. 10–19. International Association of Hydrological Science (2015) 4. Elimelech, M., Phillip, W.A.: The future of seawater desalination: energy, technology, and the environment. Science 333, 712–717 (2011) 5. Miller, S., Shemer, H., Semiat, R.: Energy and environmental issues in desalination. Desalination 336, 2–8 (2015) 6. Micale, G., Cipollina, A., Rizzuti, L.: Seawater Desalination: Conventional and Renewable Energy Processes, 1st edn. Springer, Berlin, Heidelberg (2009) 7. Kumar, M., Puri, A.: A review of permissible limits of drinking water, Indian. J. Occup. Environ. Med. 16(1), 40–44 (2012) 8. Methnani, M.: Influence of fuel costs on seawater desalination options. Desalination 205(1–3), 332–339 (2007) 9. Water Security. https://www.globalwaterintel.com/. Last accessed on 15/02/2019 10. Subramani, A., Badruzzaman, M., Oppenheimer, J., Jacangelo, J.G.: Energy minimization strategies and renewable energy utilization for desalination: a review. Water Res. 45, 1907–1920 (2011)

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11. Rao, S.M., Mamatha, P.: Water quality in sustainable water management. Curr. Sci. 87(7), 942–947 (2004) 12. Ettouney, H., El-Dessouky, H., Alatiqi, I.: Process control in water desalination industry: an overview. Desalination 126(1–3), 15–32 (1999) 13. Oldach, R.: Matching Renewable Energy with Desalination Plants. Project Report MEDRC Project: 97-AS-006a (2001) 14. Qiblawey, H.M., Banat, F.: Solar thermal desalination technologies. Desalination 220(1–3), 633–644 (2008) 15. World Health Organization: Guidelines for Drinking-water Quality, vol. 1, 3 edn. (2006) 16. Bhardwaj, R., Tenkortenaar, M.V., Mudde, R.F.: Inflatable plastic solar still with the passive condenser for single family use. Desalination 398, 151–156 (2016) 17. Census of India 2011. http://censusindia.gov.in/Census_And_You/area_and_population.aspx. Last accessed 2019/02/09 18. FLIR A325sc. https://www.flir.in/products/a325sc/. Last accessed 2018/12/03 19. SOLIDWORKS 2017 Premium, Flow Simulation 2017 SP3.0. Build: 3794

Incorporating Battery Degradation in Stand-alone PV Microgrid with Hybrid Energy Storage Ammu Susanna Jacob, Rangan Banerjee, and Prakash C. Ghosh

1 Introduction In a stand-alone renewable microgrid, the supply and demand variability is found in different time scales, i.e. instantaneous, diurnal, and seasonal. A single energy storage device cannot cater to these varied fluctuations. Therefore, we combine two or more energy storage to provide a reliable supply to the load forming a hybrid energy storage. An optimal method to combine different energy storage units is based on its nominal discharge duration as it can be easily correlated with the supply demand variability. It is important to analyse the performance of these complex energy storage systems in a microgrid context. The analysis of hybrid energy storage system in a microgrid context with varying lifetimes for battery storage is not found in literature. The motive of this paper is to model and simulate a microgrid with hybrid energy storage system (battery, supercapacitor, and hydrogen storage) by taking into account the battery degradation and analyse the system reliability and economics. Two off-grid microgrid case studies where the use of hybrid storage can be justified are examined—telecom tower and a welding shop.

A. S. Jacob · R. Banerjee · P. C. Ghosh (B) Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India e-mail: [email protected] A. S. Jacob Center for Study of Science, Technology and Policy, Bengaluru 560094, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_43

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2 System Design and Evaluation The block diagram for the generic system with hybrid storage is shown in Fig. 1. The system consists of PV, DC-DC converter with maximum power point tracking, supercapacitor as short-term storage, battery as mid-term storage, and hydrogen storage with fuel cell and electrolyser as long-term storage. The hybrid storage system is connected to the DC bus through controllers (depicted as C 1 , C 2 , and C 3 ). The AC load is coupled to the DC bus through an inverter. The microgrid with hybrid energy storage system is sized using pinch analysis and design space approach. Here cumulative supply should always be greater than the cumulative demand. For the given supply, there is a pinch point, where supply meets the demand and storage is minimum. The minimum storage points are plotted for different generator ratings to obtain the sizing curves. The region above the sizing curve is the feasible region (where demand is met by the supply) or called as, the design space. The sizing curves for short-term, mid-term, and long-term storage are obtained, by repeating the analysis for different time scales (minutes to hour, hour to days, and week to year), thereby correlating the supply demand variability with the nominal discharge duration of various storage devices. Hence, for a PV rating we get one set of hybrid storage (short-term, mid-term, and long-term) for the given supply and demand. The resulting design curves are approximated to quadratic equations,

Fig. 1 Block diagram of the generic microgrid system with hybrid storage

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and the correlations are used as constraints to determine the optimal mix of supply and storage that minimise the life cycle cost. This sizing methodology for hybrid energy storage in PV microgrids is discussed in detail in our previous work by the authors [1].

2.1 Component Modelling The different components in the block diagram of Fig. 1 are modelled using its electrical equivalent circuit or its characteristic curves. The component modelling equations and its validation are shown in Table 1. The DC/DC converters are modelled as a lumped parameter model with a constant efficiency. Three controllers—C 1 , C 2 , and C 3 —supervise the system based on the availability of solar irradiance and load. The main controller C 1 controls the subcontroller C 2 and C 3 . C 2 controls the power flow between PV, storage, and load. C 3 selects the different storage based on rulebased energy management strategy and system requirements. The PV, battery, and battery degradation model are discussed in detail in our previous work by authors [2]. The different component models described above in Table 1 are compiled together in MATLAB/Simulink to simulate the entire system behaviour. The information flow diagram of the generalised system is shown in Fig. 2. The solar insolation, temperature, and the loads are given as input to the system model. The component blocks show the input, output, and the parameters governing each blocks.

2.2 Energy Management Strategy We follow a rule-based energy management strategy for the simulation. Solar PV is the primary source of power to the loads. When PV generation is insufficient, stored energy from battery supplies the load. If this energy does not satisfy the load, fuel cell operates and converts the hydrogen to electricity. Similarly, when excess energy from PV is available battery is charged. Once battery is fully charged and still excess energy is available; then, the electrolyser converts the excess electrical energy to hydrogen.

3 Case Study Two practical microgrid contexts with respect to Indian conditions are considered— off-grid telecom tower and a welding shop. Three metrics are used to evaluate the microgrid contexts—loss of load probability (LOLP), annualised life cycle costing (ALCC), and battery degradation rate. LOLP measures the probability that in a given

PV model validation of ECO 250 W using specification sheet [4]

Output current of PV1 ,     s −1 − Io_PV = IPV − I0 exp VPVV+IR ta VPV +IRs Rp

(continued)

Validation

PV [3]

Single-diode equivalent circuit of PV model

Component and references Modelling equations

Table 1 Modelling of different microgrid components and its validation

452 A. S. Jacob et al.

Experimental validation of a 2 V, 500 Ah VRLA, gel type battery for a 50 A constant current discharge

K c C 0∗ K t 1+(K c −1)(I /I ∗ )δ Qe C ( Iavg ,θ )

0

θb (t) = ∫

t



Ct

t

a) Ps − (θ−θ R



Battery electrolyte temperature,

State of Charge, SOC = 1 −

C(I, θ ) =

Capacity dependence on current and temperature,

Terminal voltage of battery2 ,   1 + Em Vb = Ib R0 + Im R2 + τ1Rs+1

(continued)

Validation

Battery [5–7]

Lead acid battery equivalent circuit

Component and references Modelling equations

Table 1 (continued)

Incorporating Battery Degradation in Stand-alone PV Microgrid … 453

Supercapacitor [10]

Id (k)∅DOD (k)t Cλ,max

k=ti

⎣⎝ Ae

⎡⎛ −E a R



λfloat

1 − 1 θb (k) θref

⎤

⎠φDOD (k)⎦t

⎞

SOC =

VSC VSCfull

Voltage of the supercapacitor,  VSC = ISC RSC + C1SC i dt State of Charge of the supercapacitor,

RC model of a supercapacitor cell

Total degradation, δ(Id , DOD, θb k) = δ(Id , DOD, k) + δ(θb , k)

δ(θb , k) =

t f

Degradation of battery w.r.t. temperature,

k=ti

k=t f

(continued)

Supercapacitor model validation of 2.7 V, 350 F using specification sheet [11]

Battery degradation model is validated with respect to End of Life (EoL) given in technical specification [9] for the given conditions. The battery is cycled for 1800 cycles at C/10 charge and discharge at a temperature of 25 °C. The EoL in technical specification = 80% of initial capacity (i.e. 20% battery degradation). The Simulink model gives a battery degradation of 21.78%

Battery degradation model Weighted Ampere hour approach degradation of battery w.r.t. [8] discharge current and DOD3 ,

δ(Id , DOD, k) =

Validation

Component and references Modelling equations

Table 1 (continued)

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Since the characteristic curves of fuel cell and electrolyser are taken as look-up table, the fuel cell and electrolyser system do not require validation

U H2 = NFC ∗

IFC 2F

=

PFCstack 2F∗VFC

Utilisation rate of hydrogen (in mol/s),

(continued)

Validation

Fuel cell [12]

Typical characteristic curves of PEM fuel cell

Component and references Modelling equations

Table 1 (continued)

Incorporating Battery Degradation in Stand-alone PV Microgrid … 455

Hydrogen produced from the electrolyser is stored in a compressed tank. It is assumed that the electrolyser produces hydrogen at this pressure

PH2 = NEC ∗

IEC 2F

=

PECstack 2F∗VEC

Production rate of hydrogen (in mol/s),

P V —PV current due to solar radiation, I 0 —reverse saturation current of the diode, V PV —PV output voltage V t —thermal voltage of PV, a—ideality factor, Rp —PV shunt resistance, Rs —PV series resistance 2 V —battery voltage, I —battery current, R , R , R —equivalent circuit resistances, E —main branch voltage, K —parameter relating to battery equivalent 0 1 2 m c b b circuit, Qe —extracted charge, Ps —losses in battery, θ a —ambient temperature, Rt —thermal resistance, C t —thermal capacitance 3 δ(I , DoD, θ )—battery degradation fraction, I —discharge current,  DOD —depth of discharge stress factor, k—simulation step, A—Arrhenius constant, d b d E a —activation energy, C λ,max —maximal lifetime capacity, λfloat —float life of battery

1I

Validation

Electrolyser [13]

Typical characteristic curves of an electrolyser

Component and references Modelling equations

Table 1 (continued)

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Fig. 2 Information flow diagram of a generic PV stand-alone microgrid with hybrid storage

time period, the supply is less than the demand. ALCC accounts for all expenses incurred during the system lifetime and battery degradation rate is calculated based on degradation modelling given in Table 1. The hourly variations of solar insolation and temperature variations for a year of New Delhi climate are used as inputs for the simulation.

3.1 Off-Grid Telecom Tower For any telecom tower, a dependable and continuous power supply is a crucial requirement. Conventional off-grid towers powered by PV have a diesel generator with battery backup [14]. The advantage of replacing diesel generator with hydrogen technologies (electrolyser, fuel cell, and H2 tank) includes elimination of transportation cost of diesel fuel to the remote location, CO2 emission, and uncertainty in diesel price. Telecom tower is more or less characterised by constant load throughout the year. An average load of 72 kWh/day is assumed for the given context. The input solar radiation has daily and seasonal fluctuations that are met by the hybrid storage. The optimal PV, battery, and hydrogen storage that will meet the daily and seasonal fluctuations are 37 kWp , 96 kWh, and 5.2 m3 (at 200 bar), respectively. The DC distribution side voltage where PV and hybrid storage are connected is selected as 48 V. Hence, DC-DC converters are required to boost the PV, fuel cell, and electrolyser voltage to the bus voltage. 2 V 500 Ah batteries (with 50% DoD) are connected directly to the DC bus as 48 V strings. The battery and hydrogen SOC variation for the entire year is shown in Fig. 3. The fuel cell and electrolyser sizes are iteratively

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Fig. 3 Battery and hydrogen SOC variation for telecom tower

selected from the simulation. The p.u. cost of the system is 27 |/kWh (with DOE target cost for hydrogen storage and its subsystems). During the summer months, battery and hydrogen SOC are near its upper limit (0.8 and 1, respectively) and during winter months, their SOC is near its lower limit (0.3 and 0, respectively). The battery degrades due to time and temperature variation to 3.78% of its initial capacity in the first year. From the simulation, it is observed that the fuel cell and electrolyser are operated in their nominal regime and hence have rated lifespan. The above system is compared by incorporating battery degradation for two sets of design points—one with hybrid storage and the other conventional battery only design as shown in Table 2. It compares the yearly LOLP, battery degradation, and the COE of the system. With battery degradation, the life of battery is halved due to ambient and operating conditions. As a result, there is two rupees increase in the p.u. cost of the system. Overall, the reliability of any system improves with the addition of a long-term storage with mid-term storage (battery) at a low COE of the system.

3.2 Off-Grid Welding Shop Small industries like welding shop are characterised by spiky changes in load, which is 4–8 times the base demand. An off-grid welding shop powered by PV requires battery and a short-term storage like supercapacitor to account for minutely load fluctuations. The welding shop logged load is shown in Fig. 4a. The only electric load other than welding machine is two incandescent light bulbs. The welding shop operates for around 5–6 h on an average when solar irradiance is available. For the simulation, it is assumed that the one hour load profile is repeated for 7 h, i.e. from 8:30 am to 3:30 pm. The daily energy demand is around 3.0 kWh. The seasonal variations in load, irradiance, and temperature are neglected during simulation. As a result, the hybrid storage combination becomes only battery and supercapacitor storage. For this context, the load current rises rapidly from the base current. Both PV and supercapacitor are employed to supply this power. As the load is continuous, the supercapacitor SOC depletes. In order to avoid this, battery supplies a continuous

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Table 2 Comparison of two design points with manufacturer data and with battery degradation model for telecom tower Component

PV

With manufacturer life of battery

With battery degradation model

HESS

HESS

Battery-only system

Battery-only system

Size (kWp )

37

65

37

65

lifetime (years)

25

25

25

25

Size (kWh)

96

96

96

96

lifetime (years)

10

10

5.3

5.1

H2 storage

Size (m3 )

5.2

−

5.2

−

lifetime (years)

20

−

20

−

Fuel cell

Size (kW)

4.75

−

4.75

−

lifetime (years)

7

−

7

−

Size (kW)

1.55

−

1.55

−

lifetime (years)

10

−

10

−

0

0.81

0

2.37

−

−

3.78

3.87

34.21

27.2

36.41

VRLA battery

Electrolyser Yearly LOLP (%)

Battery degradation (%/year) COE (|/kWh)

DOE target cost of 25.2 H2 storage Actual cost of H2 storage

59.7

61.7

current that is ‘k’ times its nominal current. The ‘k’ is determined according to the battery and supercapacitor SOC, and it never exceeds twice the nominal current (to ensure minimum battery degradation). During the process, if supercapacitor is completely depleted, battery supplies the remaining load. In addition, PV charges both supercapacitor and battery when excess energy is available. The ideal storage size from sizing curve for PV, battery, and supercapacitor to cater to the second-level fluctuations are 1.2 kW, 2.23 kWh, and 5.16 Wh. The DC distribution side voltage is selected to be 24 V. A bi-directional DC-DC converter needs to be employed at the terminals of supercapacitor to keep the voltage fluctuations minimal. The simulation is done for a day with 7 h of welding load. The SOC distribution of battery and supercapacitor is shown in Fig. 4b. From the figure, it is clear that the load is met at all times in the required voltage range. The sharp fluctuations in the second level are met by supercapacitors. The base load and charging of supercapacitor at a constant rate (depending on the SOC of supercapacitor and battery) are taken care by batteries. PV also charges the supercapacitor as and when it is available. The SOC of the battery remains more or less constant when PV is available as seen in Fig. 4b. As a result, the battery degradation is nominal (0.008%/day), and hence, the p.u. cost (COE) of this system is about 13 |/kWh.

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Fig. 4 a Welding shop load and b SOC variation for battery and supercapacitor during welding Table 3 Comparison of two design points for welding context Component

With manufacturer life of battery

With battery degradation

HESS

Battery only system

HESS

Battery only system

Size (kWp )

1.2

4.2

1.2

4.2

Lifetime (years)

25

25

25

25

Size (kWh)

2.4

1.01

2.4

1.01

Lifetime (years)

6

6

6.68

less than half a year

Size (Wh)

5.4

−

5.4

−

Lifetime (years)

15

−

15

−

Daily LOLP (%)

0

0.488

0

0.488

Battery daily degradation (%/day)

−

−

0.0082

7.18

COE (|/kWh)

13.2

35.2

13

>85

PV

VRLA battery

Supercapacitor

Incorporating Battery Degradation in Stand-alone PV Microgrid …

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Table 3 compares two systems on the boundary curves: (i) hybrid storage and (ii) battery-alone system with manufacturer given life and by incorporating battery degradation. It can be clearly seen that supercapacitor reduces battery degradation and increases the battery life. Life cycle costing becomes inaccurate if battery degradation model is not considered in contexts like welding as presented in the case of battery-alone systems. Battery-alone systems will not be able to handle these type of fluctuations (7–8 times the base load) resulting in its deterioration in less than a year. Otherwise, the battery should be heavily oversized. This leads to high life cycle cost of the system. Thus, the best solution is to have a hybrid energy storage of battery and supercapacitor.

4 Conclusion This paper models and simulates PV-based microgrid with hybrid storage by incorporating the battery degradation. This helps in understanding the performance of hybrid storage in a microgrid system. The reliability and cost of energy is also evaluated and compared with conventional battery-alone system. Reliability of the system improves with the addition of a long-term storage to battery-alone systems. If the load is repetitively spiky, an addition of short-term storage improves the battery degradation. In addition, the battery degradation has direct correlation with life cycle cost of the system. Typically, while calculating the life cycle cost, the life cycle given by manufacturer is considered. The life of battery under actual operating conditions is different. By incorporating the capacity fade of battery into the system, a more realistic life cycle assessment is achieved. Two case studies—off-grid telecom tower and a welding shop—are simulated. For these contexts, we need to have hybrid storage to improve reliability and reduce battery degradation. As an example, the isolated welding shop with annual energy demand of 1408.5 kWh, the addition of supercapacitor improves the life of battery from 2 to 8 years, thereby improving life cycle cost of the system from 18 to 13 |/kWh. In addition, the daily LOLP is reduced from 7.2 to 0%.

References 1. Jacob, A.S., Banerjee, R., Ghosh, P.C.: Sizing of hybrid energy storage system for a PV based microgrid through design space approach. Appl. Energ. 212, 640–653 (2018) 2. Jacob, A.S., Banerjee, R., Ghosh, P.C.: Trade-off between end of life of battery and reliability in a photovoltaic system. J. Energy Storag. 30, 101565 (2020) 3. Villalva, M., Gazoli, J., Filho, E.: Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24(5), 1198–1208 (2009) 4. PV Power Tech: ECO 225–250 W specification sheet. PV Power Tech (2018). [Online]. Available https://www.pvpowertech.com/60-cells-poly. Accessed 26 Apr 2019

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5. Ceraolo, M.: New dynamical models of lead-acid batteries. IEEE Trans. Power Syst. 15(4), 1184–1190 (2000) 6. Barsali, S., Ceraolo, M.: Dynamical models of lead-acid batteries: implementation issues. IEEE Trans. Energ. Convers. 17(1), 16–23 (2002) 7. Jackey, R.A.: A simple, effective lead-acid battery modeling process for electrical system component selection. SAE Paper, 01-0778 (2007) 8. Martel, F., Kelouwani, S., Dube, Y., Agbossou, K.: Optimal economy-based battery degradation management dynamics for fuel-cell plug-in hybrid electric vehicles. J. Power Sour. 274, 367– 381 (2015) 9. Exide Industries: Exide Gel Battery Technical Literature, pp. 1–53 (2014) 10. Maxwell Technologies Inc: Test procedures for capacitance, ESR, leakage current and selfdischarge characterizations of ultracapacitors (2015). [Online]. Available http://www.maxwell. com/images/documents/1007239-EN_test_procedures_technote.pdf. Accessed 26 Mar 2018 11. Maxwell Technologies Inc: Data sheet bc series ultracapacitor (2013). [Online]. Available http://www.maxwell.com/images/documents/bcseries_ds_1017105-4.pdf. Accessed 27 Mar 2018 12. O’Hare, R.P., Cha, S.-W., Colella, W., Prinz, F.B.: Fuel Cell Fundamentals, 3rd edn. Wiley, New Jersey, USA (2006) 13. Saeed, W., Warkozek, G.: Modeling and analysis of renewable PEM fuel cell system. Energ. Procedia 74, 87–101 (2015) 14. Kaldellis, J.K.: Optimum hybrid photovoltaic-based solution for remote telecommunication stations. Renew. Energ. 35(10), 2307–2315 (2010)

Simulation Studies on Design and Performance Evaluation of SAPV System for Domestic Application M. R. Dhivyashree, M. B. Gorawar , V. G. Balikai , and P. P. Revankar

1 Introduction The generation of the solar photovoltaic generation is intermittent and subject of constraints such as availability of sun, time of the day, season and the sky conditions. The study of behavior of such a system becomes an important aspect before commissioning the power plant to analyze the techno-economic parameters. The system performance can be predicted and analyzed by the computational tool such as PVsyst which facilitates user to determine the system performance at the given location for specific configuration. The said tool can be used to simulate stand-alone, grid-connected solar photovoltaic system, water pumping system as well as DC gridconnected system. The tools come with an intuitive user interface for selecting the system to be simulated and selection of balance of system components for photovoltaic system from the large database of commercial photovoltaic, battery, inverter and PCU manufactures. The reported research presents design and performance analysis of the off-grid stand-alone solar photovoltaic system to cater the energy demands of rural domestic application. Stand-alone photovoltaic system (SAPV) is independently operated energy generation system using solar energy. The system neither imports nor exports any energy from the grid as it is not connected to grid hence called stand-alone system. Major components of the SAPV system are charge controller (MPPT or PWM), inverter or PCU and battery which are commonly called as balance of the system connected to solar photovoltaic module. The solar modules are arranged in specific series and parallel combination to form array and string in the large-scale power generation plant. The commercial installation of solar photovoltaic power generation demands thorough economic and technical feasibility studies, prior to its on-site installation by PV system designer. The present study evaluates the solar power availability at location, and further, the system is designed through the PVsyst M. R. Dhivyashree · M. B. Gorawar · V. G. Balikai · P. P. Revankar (B) School of Mechanical Engineering, K.L.E. Technological University, Hubballi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_44

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DATA ANALYSIS

RECOMMENDATION

Fig. 1 Stages of simulation implementation

software on specifying the domestic power consumption as shown in Fig. 1. The power generation scheme designed through software is analyzed in context of the metrological data at the location of the installation.

2 Literature Review The design analysis of SPV system as reported by various researchers, academicians and system designers using simulation tools has proved to be reliable approach. Kandasamy et al. reported on grid-connected SPV system analyzed with PVsyst for its performance ratio and various power losses (temperature, internal network and power electronic) for 1 MW SPV system along with life cycle cost [1]. Suresh and Thomas reported PV system characterization for solar irradiance, temperature and wind speed. The system adopted non-uniform operating field losses that assessed 7468 Wh of energy demand on basis factors such as temperature, soiling, seasonality, partial shading, system voltage, losses and aging [2]. Yadav et al. reported on Hamirpur with annual solar radiation of 4.4 kWh/m2 -day having an installed 1 kWp SAPV system simulated in PVsyst that evaluated an annual performance ratio of SPV of 0.724 on an average basis [3]. Irwan et al. reported on exhaustion of conventional energy and its climatic impact. The design aspects and assessment of SPV system based on actual field trials on 1 kW off-grid PV system at, New Delhi, India were investigated in the study [4]. Srivastava and Giri highlight the importance of simulation software for SPV systems to predict the output power. Research elaborates the study carried on grid-connected SPV system comprising of 2000 PV modules of 250 Wp rating connected with the 50 kW grid-tied inverter. The simulation shows that 901.44 MWh can be generated in a year that can be fed to grid with 83.1% performance ratio [5]. Barua et al. evaluated grid-connected SPV system on basis of NASA metrological data. The simulation predicted SPV generation of 590 MWh equal to 11% of annual consumption of Pondicherry University [6]. Tapaskar et al. have explored various renewables to meet rural energy needs. The study preferred distributed generation to grid for load size lower than a breakeven point that justifies capital investment for grid line extension to remote location [7].

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3 Methodology The design of the stand-alone system for the domestic application was accomplished through three design steps.

DATA COLLECTION

DATA ANALYSIS

RECOMMENDATION

3.1 Data Collection This step involves collection of critical data required to determine energy requirements of domestic consumer. The geographical location is also assessed for its metrological data for predicting the power generation and performance analysis of designed system. The critical step has system designer interacting with consumer apart from site survey for suitable installation space at the consumer premises for shadow-free zone.

3.2 Data Analysis This was accomplished on software that makes assessment of metrological data at given location and system sizing computed through PVsyst. The user specifies power needs on hourly basis with system autonomy integrated to metrological data.

3.3 Recommendation Computational tool designs the SAPV system component sizing based on userspecified demands and metrological data at the site. The simulation gives detailed performance and system behavior with recommendations suggested to make suitable modifications in system parameters or user end to accomplish better performance.

4 Design of Stand-alone Solar Photovoltaic System The typical energy flow in SAPV system is shown in Fig. 2. The power generated from SPV modules is fed into charge controller for power regulation in order to optimally

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CHARGE CONTROLLER

BATTERY

INVERTER

LOAD

Fig. 2 Generalized block diagram of a typical SAPV system with AC loads

Fig. 3 Site details to be specified in the PVsyst

charge the battery for storage of power to be utilized in the non-sunny hours of the day. The light energy (photo) is converted into electric energy by SPV module and is stored in the battery as chemical energy. The electric energy stored in the battery is DC in nature which is used to cater the load demands. The DC electric power extracted from the battery is connected to power converter known as inverter which converts DC electric power into AC power to cater the AC loads; however, DC loads can be directly connected to the charge controller with state of charge (SOC)-based load cut of system for prevention of over-discharge of the storage battery system. The design of the SAPV system at site is dependent on the weather condition at the site specified by the user. The metrological data from the trusted and reliable source get imported into the software on specifying the geographical coordinates (latitude and longitude) of the location. The monthly metrological data at the site with horizontal global radiation, diffused radiation, ambient temperature and wind velocity can be seen represented in the tabulated format in the software interface. Figure 3 represents input window of interface to specify the geographical details.

4.1 Geographical Data and Solar Potential Assessment The metrological data imported into PVsyst from authenticated databases (such as NREL) are in two distinct time-based formats—one being the hourly data and other is monthly data format. Imported weather data include horizontal global radiation, diffuses radiation, ambient temperature and wind velocity at specified geographical coordinates. The present studies were based on test location situated in Vidynagar, Hubballi, India, as specified in the software as shown in Fig. 3. The sun path for the specified location is graphically represented in Fig. 4 at 15.37° N and 75.12° E.

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Fig. 4 Sun path for the location

4.2 Module Orientation and Hourly Generated Solar Radiation Data The orientation of SPV modules plays an important role in the optimal power generation that in turn depends on the geographical location index of the installation and day-of-the-year varying with season. The general practice of aligning the solar module is to tilt the panel equal to latitude angle of the given site—however, this is considered to be quick and approximate method. The precise angle of tilt can have a relation to declination angle (δ) determined by the Eq. (1) 

360 δ = 23.45 sin (284 + n)) 365

 (1)

that has n to represent the number for day-of-the-year (“n” is 25 for January 25th and 41 for February 10th). Seasonal variation and change in sun path at the given location result in different optimal tilt angles throughout the year. The tilt angle can be varied manually as per the season or a tracking device and can be implemented for continuous sun path tracking and automatic aligning of solar module to the sun direction. The simulation studies in the present research assume the fixed type of solar module tilted at an angle of 16° as shown in Fig. 5. The constraints imposed by economic feasibility and maintenance considerations compelled to choose a fixed type of solar module installation for simulation purpose; later case of tracking would incur additional energy and cost factor for tracking mechanism.

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Fig. 5 Simulationgenerated data

Monthly solar irradiation data consisting of horizontal global irradiation, horizontal diffuse irradiation, temperature, etc., are shown in Fig. 6; these data are the site-specific and are imported from the Internet databases.

Fig. 6 Orientation of SPV modules

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4.3 Load Data of the Simulated Installation Site The user interface of the PVsyst allows the user to specify the load, its power consumption and usage hours per day for determining total daily energy needs of the user. The daily energy needs are further used by the software to size the storage battery bank and calculate the solar module required to cater the user needs and charge the battery bank. The solar module and battery bank sizing are critically dependent on the load distribution during the day—for example, if major loads are run at the sunshine hours, the size of battery bank would be reduced and vice versa. Figures 7 and 8 show the loads with their power ratings along with the hours of usage during the day. Domestic energy demand peaking at evening time and detailed hourly distribution of the load respectively can be noticed in the graphical display.

Fig. 7 Load data used for the simulation study

Fig. 8 Graphical display of defined hourly load usage at rural household

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Table 1 Module and battery specifications for simulated SAPV system PV module specifications

Battery specifications

Module/Manufacturer

Generic

Nominal power

75 Wp

Voltage

12 V

Technology

Polycrystalline silicon

Battery capacity

103 Ah

Short-circuit current

4.4 A

Permissible DOD

80%

Open-circuit voltage

16.9 V

Technology

Lead–acid/tubular

PWM charge controller 12 V, 10 A

Manufacturer

Exide

Inverter 12 V, 800 VA Pure Sine Wave

4.4 Configuration of the Proposed SAPV System The use of system components is decided by cost and availability of the specific components. The large database of the PVsyst allows user to select the desired component from the specific manufacturer supplying at the site of installation. The components chosen for the present simulation are presented in Table 1. It is very important to select the best components in the system to achieve costefficient and reliable system

4.5 PV Array Sizing The array is a stack of SPV modules arranged to generate required voltage and current to cater to loads and battery charging. The DC power from PV array must account to losses in later power storage stage and conversions like losses in charge controller, battery and inverter. These losses add up to power demands and determine rating and PV module capacity in array. The component losses are dependent on type and variant of component used—for MPPT charge controller, efficiency is in range of 90–95% and PWM charge controller has value from 70 to 85%. The battery efficiency ranges from 80 to 85%, while inverter efficiency is 70–80% for square wave and 85–95% for pure sine wave. Size of PV Array =

Daily consumption of the Energy (Sunshine hours at the location) × (Operating hours of PV modules)

(2)

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4.6 Battery Sizing Battery is the major component of the SAPV system with type and capacity decided by capital investment and consumer load demand, respectively. The batteries such as lithium-ion are the best for power storage as they are lightweight and efficient but the cost of these batteries would pose the budgetary inflations on total installation cost of the system. Sealed Maintenance-Free (SMF) batteries which are spill proof can also be used which are cheaper than lithium-ion batteries but costlier than lead– acid batteries. Commonly, tubular lead–acid batteries of c10 rating typically called as “tubular solar lead–acid battery” are used in the domestic solar installations as they have higher depth of discharge (DOD) and high current charge and discharge capability. These batteries if properly maintained can have life of 8–12 years of life with 1500 cycles @ 80% DOD, 3000 cycles @ 50% DOD and 5000 cycles @ 20% DOD. Battery capacity of the SAPV system is determined by the equation below Battery Bank Capacity =

(Average Daily Consumption) × (Autonomy in days) (System Voltage) × (Depth of Discharge)

where autonomy can be defined as the reserve power stored in the battery in terms of number of days to cater the load for non-sunny days.

4.7 Charge Controllers These are used to regulate the power from the PV array and charge the storage batteries. Two variants of charge controller are used in the stand-alone solar photovoltaic system—pulse width modulation (PWM)-based charge controller and MPPTbased charge controller. Low-powered SPV systems are designed with PWM-based charge controller to keep the system cost minimal. The high-power SPV systems typically above 1 kW are designed with maximum power point transfer (MPPT)-based charge controllers to extract maximum power out of PV module. These MPPT-based charge controllers are costlier compared to the PWM-based controllers but also have higher conversion efficiencies up to 95%. System designer has to make suitable trade-off between the choice of charge controller based on the system ratings and installation costs.

4.8 Inverter Home appliances use AC power for their operation—Indian appliances are manufactured to operate at AC electric power at 230 V and 50 Hz. Inverter converts DC

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power stored in the battery bank into AC power to cater the requirement of AC loads. Inverters can also be classified into square wave and sine wave inverters depending on the output waveform of the AC electric power. The square wave inverters are cheaper compared to sine wave inverters, but they produce square wave which is considered harmful for smooth operation and life of the appliance used. AC appliances running on the square inverter produce humming sound and exhibit inefficient operating behavior. However, sine wave inverters are costlier, but the output of the inverter is approximately similar to the gird power which is considered safe, smooth and efficient for the operating AC appliances. Modern-day solar inverters have inbuilt charge controllers featuring both PWM and MPPT variants which can be directly connected to solar modules.

5 Results and Discussions This section of the research article gives insight into the technical interpretations drawn on basis of the post-processed results furnished by the simulation tool. Figures 9, 10, 11, and 12 give the information of solar radiation data at specified location, signifying the solar potential available at the site of investigation. Similarly, Figs. 13, 14, 15, 16, 17, and 18 emphasize on the operational aspects of a particular design of solar photovoltaic system installed at the location. The simulation results of SAPV model designed in PVsyst at the selected geographical location have been discussed in the following section. The average power available at the given location is 5.37 kW, and the system is generating an average of 128.4 kWh per year. The system is designed from two-day autonomy and five percent allowable

Fig. 9 Array power generation

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Fig. 10 Daily array output energy

Fig. 11 Incident irradiation distribution

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Fig. 12 Incident irradiation tall distribution

Fig. 13 Normalized production of 75 Wp

loss of load which can be significantly seen in the months of June, July and August of the year.

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Fig. 14 Normalized production and loss factor

Fig. 15 Performance ration and solar fraction

5.1 Financial Aspects of SAPV System The feasibility of any engineering project is dictated by the economic parameters. The implementation of SAPV for power generation through roof-top installations has been assessed through inbuilt programming tools in PVsyst. Figure 19 presents

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Fig. 16 State of charge daily distribution

Fig. 17 Average SOC of the battery throughout year

the user interface for communicating the financial aspects of the project in terms of the cash inflows and outflows.

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Fig. 18 Available solar energy

Fig. 19 Financing aspects of the designed SAPV system in PVsyst

5.2 Sustainability Issues of the SAPV System-CO2 Mitigation Assessment PVsyst has carbon balance tool that allows the estimated savings of CO2 emissions out of designed SAPV system. Life cycle emissions (LCE) represent the emissions of CO2 connected to the respective system component or energy amount including total life cycle of a component, operation factors, production and disposal, etc. Total carbon balance of SAPV system is the difference between produced and saved CO2 emissions as shown in Figs. 20 (Fig. 21). Where E grid: energy yield of the SAPV system throughout the year System lifetime: system life in years (30 years) Grid LCE: average amount of CO2 emissions per unit of energy produced by the grid SAPV LCE: Amount of CO2 emissions released during commissioning and operation of the SAPV system.

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Fig. 20 Overview of the project carbon emission saving

Fig. 21 Variation of tCO2 with year

6 Conclusions The present article elaborates simulation and design of SAPV system for a Hubballi location in India. The computational tool has facilitated the optimal design of the system considering the user demands along with the metrological data of the site under consideration. The output of the SAPV was predicted to study the long-term behavior of the designed system. The SAPV system was designed to cater the load of rural domestic needs accounting to a daily energy need of 355 Wh. The optimal system was proposed on the design with SPV module of 75 Wp, charge controller of 12 V 10 A and lead–acid battery of 103 Ah. The average SOC of the battery throughout the year was 84.1% with 5% loss of load. The critical operation period during the month of June, July and August of the year was marked out for taking preventive measures through allied conventional power or grid imports. The CO2 mitigation of the system was also calculated and found to be 1.83 tonne during the 30 year lifetime for the SAPV system. The total capital investment on the system was assessed to be around Rs. 0.22 lakh. The system as per the design and prediction will serve the purpose of catering the energy demands of rural domestic application. The rate of return on investment for stand-alone SPV system ranges from 16 to 18% with a payback period for investment between 6.25 and 5.5 years. The societal impact of this research was to enhance living standards in rural India by means of a sustainable energy solution.

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References 1. Kandasamy, C.P., Prabu, P., Niruba, K.: Solar potential assessment using PVsyst software. 667– 672 (2013). https://doi.org/10.1109/icgce.2013.6823519 2. Suresh, P., Thomas, J.: Performance analysis of stand-alone PV systems under non-uniform operating conditions using PVsyst. Adv. Res. Electr. Electron. Eng. 1(4), 19–25 (2014). Print ISSN 2349-5804; Online ISSN 2349-5812 3. Yadav, P., Kumar, N., Chandel, S.S.: Simulation and performance analysis of a 1 kWp photovoltaic system using PVsyst. Comput. Power Energ. Inform. Commun. (ICCPEIC) 0358–0363 (2015) 4. Irwana, Y.M., Ameliaa, A.R., Irwantoa, M., Ma, F., Leowa, W.Z., Gomesha, N., Safwati, I.: Stand-alone photovoltaic (SAPV) system assessment using PVsyst software. In: International Conference on Alternative Energy in Developing Countries and Emerging Economies (2015) 5. Srivastava, R., Giri, V.K.: Design of grid connected PV system using PVsyst. Afr. J. Basic Appl. Sci. 9(2), 92–96 (2017). ISSN 2079-2034 6. Barua, S., Hossain, M.S., Mahmud, M.S., Rahman, M.W.: A feasibility study of low voltage DC distribution system for LED lighting in building. In: Innovations in Power and Advanced Computing Technologies (i-PACT) (2017) 7. Tapaskar, R.P., Revankar, P.P., Ganachari, S.V., Yaradoddi, J.S.: Biomass energy and bio-solar hybrid energy systems. In: Martínez, L., Kharissova, O., Kharisov, B. (eds.) Handbook of Ecomaterials. Springer Publication (2018)

Development of a Dynamic Battery Model and Estimation of Equivalent Electrical Circuit Parameters Sourish Ganguly, Subhrasish Pal, and Ankur Bhattacharjee

1 Introduction Battery modeling is the key to various battery storage system designs, especially in areas of renewable energy storage. Renewable technologies, such as solar or wind, do not produce a prolonged power output; and hence, electrical energy storage from a non-conventional energy source becomes a mandatory requirement. Proper design of an efficient battery model is a primary factor in the effective utilization of the power source, such as a solar panel. The model, thus obtained, is vital in the testing of various charge controller algorithms, required for the design of systems, such as electric-vehicles (EV). The fundamental challenge with the analysis of an electrical equivalent battery model is that the parameters change significantly throughout charge or discharge. Lack of constancy in the model parameters becomes an engineering challenge for the proper sizing of a battery for its applications. Since the dynamic characteristics of a battery [1, 2] vary with its state of charge (SOC) [3], proper estimation of the model parameters becomes an absolute necessity. Working with an equivalent electrical model simplifies the analysis of electrical characteristics of the battery with high precision. From literature, various concepts on battery modeling are observed [4–10]. Study has also shown that the values of the electrical parameters tend to change over time, cycle use and temperature. The main objective of this paper is to develop a robust model which considers all the above behavioral changes of the electrical parameters of different types of batteries by estimating their parameters with the help of measured S. Ganguly International Institute of Information Technology, Hyderabad, Telangana, India S. Pal Institute of Engineering and Management, Kolkata, India A. Bhattacharjee (B) Birla Institute of Technology and Science, Hyderabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_45

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charging and discharging data, at dynamic conditions of the batteries. This is critical for high efficiency in algorithms involving charge controllers. The novelty of the procedure is that the model is not affected by the chemistry of the battery, which is demonstrated in this paper. The parameter estimation has been carried out in MATLAB through an iterative process to ultimately minimize the error between the experimental plot of battery terminal voltage and the simulated plot of the same. The final values of the estimated battery parameters can then be utilized as polynomial functions of the state of charge. The rest of the paper consists of the following sections: Sect. 2 consists of the overall description of the equivalent electrical circuit used in the battery model, Sect. 2 contains the description of the model used for the parameter estimation process, Sect. 4 includes the estimation process and its results, and Sect. 5 consists of the conclusion of the paper.

2 Overall System Description Literature shows various models of a battery, based on battery chemistry, such as the Rint model, Thevenin’s model, RC model, PNGV model, etc. [1]. In the following estimation process, the one time constant (OTC) model has been employed. As seen from Fig. 1a, the equivalent electrical model is able to describe the transient battery characteristics involving time constants, which was not possible in the Rint model. The model’s internal impedance parameters are represented by Randles’ equivalent circuit model [2] which consists of an open-circuit voltage source E oc , active electrolyte resistance, or the solution resistance R0 and a parallel combination of a double-layered capacitance C 1 and a charge transfer resistance, or the polarization resistance R1 . R1 describes the transfer of charge at interface of the electrode and the electrolyte, during charging and discharging [4]. Since all the parameters of the battery, namely E oc , R0 , R1 and C 1 , are dynamic in nature and are functions of the

Fig. 1 a Battery equivalent circuit based on OTC model. b Thevenin’s model of battery equivalent circuit

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SOC, they are described by dynamic equations. The parameters keep changing with the electrochemical processes in the battery. The equations pertaining to the model are given as follow: E t = E oc − Z in Ibat

(1)

where E t and E oc are the terminal voltage and open-circuit voltages of the battery, respectively; while I bat is the current through the battery which is assumed positive during discharge and negative during charging. Z in is the equivalent internal impedance comprising of R0 , R1 and C 1 . The internal impedance of the battery constituted by R0 , R1 and C 1 can be estimated by the loop equation given in (2): Z in =

E t − E oc ±I

(2)

where E t is the terminal voltage of the battery and E oc is the open-circuit voltage of the battery. The + and − sign of current ‘I’ denotes charging and discharging, respectively. The open-circuit voltage which is a dynamic quantity depending on the SOC of the battery can be estimated by using the well-known Nernst equation as a function of SOC (3) at a temperature T and self-discharge potential drop.  E oc = n ×

E (at 50% SOC) +

  SOC 2RT ln F 1 − SOC

(3)

where n is given by the number of cells connected in series, from the work of Bhattacharjee et al. [4]. E oc = open-circuit voltage of the battery, R = 8.314 J mol−1 K−1 , T is the working temperature (in K), F = 96,485 C mol−1 , E (at 50% SOC) is the open-circuit voltage at 50% SOC. Figure 2 shows the variation of the open-circuit voltage of a 30 V (20 cells in series) vanadium redox flow (VRF) battery with SOC while charging. The parameter estimation involves accurately modeling the variation of the internal impedance of the battery with changing battery capacity. From Fig. 1, the circuit is reduced to an equivalent Thevenin’s circuit, as shown in Fig. 1b, after applying Laplace transformation. Thevenin’s impedance of the electrical equivalent circuit Z in (s) is evaluated, as shown in (4). The internal impedance obtained is a function of the SOC. Each of the estimated dynamic parameters can be obtained as polynomial functions of SOC, and hence, analysis of the internal impedance of the battery can be carried out.

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Fig. 2 Variation of E oc with SOC for 20 cell VRFB

 Z in (s) = R0 +  = R0 +

R1 × R1 +

1 sC1 1 sC1



R1 R1 C 1 s + 1

 (4)

R0 , R1 and C 1 are functions of SOC.

3 Model Description 3.1 Proposed Battery Estimation Model and Its Components The simulation model designed in MATLAB, as given in Fig. 3a, shows a battery block with a controlled current source, which acts as the source (or sink, depending on the direction of current). A voltage sensor measures the simulated output terminal voltage of the battery. The input (the current source) and the output (V out ) in the figure are loaded with the experimental data, which is then utilized in the parameter estimation process to optimize the simulated response of the system. Randles’ model of a battery is modeled as shown in Fig. 3b and consists of an opencircuit voltage source (E oc ) in series with a resistor (R0 ) and parallel combination of a resistor (R1 ) and a capacitor (C 1 ). Each of these parameters mentioned above is tabulated in look-up tables (LUTs), with the horizontal axis consisting of the SOC (ranged from 0 to 1) as shown in Fig. 4b, c. Except E oc , which is tabulated from the Nernst Eq. (3), the rest of the parameters which are required for the estimation are observed to be dissimilar for charging and discharging. Hence, for each of the parameters (except E oc ), two separate LUTs are employed, one for charging and the

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Fig. 3 a Simulation model for estimating dynamic parameters, b the battery equivalent circuit in MATLAB with E oc as the open-circuit voltage and resistor R0 and parallel RC branch consisting of R1 and C 1

Fig. 4 The parameter estimation model with a open-circuit voltage (E oc ), b resistance (R0 ), and the parallel RC branch (R1 and C 1 ) referred to their respective LUTs. c SOC calculation block of the simulation model. d Subsystem of the SOC estimator block

other for discharging. The following figures show the various parts of the proposed model developed for carrying out the estimation process. Figure 3b shows the overall model of the equivalent circuit employed for the battery. Figure 4 shows the various components (subsystems) of the overall model as shown in Fig. 3b. Figure 4a shows the open-circuit voltage block (E oc ), along with the SOC calculating block. Figure 4b shows the subsystem of the RC branch block, and Figure 4c shows the subsystem of the series resistance (R0 ) block.

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3.2 Calculation of SOC The SOC calculation block is designed on the principle of the Coulomb counting (CC) method. Current flowing through the battery during charging is integrated with time and is added to initial capacity. Depending on charging or discharging, the SOC would increase or decrease, respectively (6). In the simulation model’s state of charge calculation block, shown in Fig. 4d, the block takes the instantaneous value of current from the current sensor and evaluates the SOC as described by Eqs. (5) and (6). The state of charge is found out by dividing the present capacity of the battery by the total capacity of the battery. Mathematically, the SOC of the battery is given as SOC =

q(t) qmax

(5)

where q(t) = capacity of the battery as a function of time t and qmax = maximum capacity of the battery. q(t) is calculated as: t q(t) = q0 +

i(t)dt

(6)

o

4 Estimation and Results 4.1 Estimation Process The estimation has been carried out through Simulink parameter estimation tool. The optimization has been carried out using trust-region-reflective algorithm, which minimizes the sum-squared error of the simulated response by iteratively tuning each parameter of the equivalent circuit model. As mentioned above, the parameters were initialized as constant values for all states of charge and were tabulated in LUTs before the beginning of the estimation process. The iteration optimizes the response by updating the model parameter values for C 1 , R1 and R0 after each iteration in their respective LUTs, resulting in dynamic values of parameters at each SOC.

4.2 Results Obtained Figure 5a shows the measured output of a 30 V, 7.2 kWh VRF battery at 40 A constant current charge at 27 °C, and the initial simulated response of the battery model with

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Fig. 5 a Measured and simulated response of the battery terminal voltage (V out ) before estimation is started (with constant parameters for every SOC) (with a constant charging current of 40 A at 27 °C), b simulated and measured terminal voltage (V out ) during 40 A constant current charging of 30 V, 7.2 kWh VRF battery, c simulated and measured terminal voltage (V out ) during 40 A constant current discharging of 30 V, 7.2 kWh VRF battery with tuned dynamic parameters

40 A constant current input and temperature 27 °C. As evident from the figure, a significant error between simulated and measured output voltage is observed. This is due to an erroneous assumption where all battery parameters are employed fixed values. A similar comparison has been carried out after completion of the estimation process, as shown in Fig. 5b. Simulation Response Figure 5 shows the simulated response compared with the experimentally obtained output voltage of 30 V, 7.2 kWh VRF battery after completion of parameter estimation. Similarly, a process to match the output voltage of the battery model with 40 A constant current discharge with that of the experimental battery (30 V, 7.2 kWh VRFB) is carried out, as shown in Fig. 5c. The estimation process is also carried out in an identical manner for a 12 V, 3.6 kWh lithium-ion battery with charging/discharging at 50 A constant current. The performance of the model in terms of sum-squared error is shown in Table 1. Estimated Parameters The parameters of the estimated model are plotted versus SOC, from the updated LUTs of the tuned battery model. Since the variation of the parameters with SOC is usually different for cases of charging and discharging, separate plots are made for both the cases. The following figures, Fig. 6a–f, show the plots of the estimated dynamic electrical parameters (R0 , R1 and C 1 ) of the 30 V VRF battery during charging and discharging respectively.

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Table 1 Performance of the estimation process on the battery model S. No.

Battery type

Sum-squared error between of the simulated response of the battery model (in V) During charging

During discharging

Before estimation

After estimation

Before estimation

After estimation

1

30 V, 7.2 kWh VRF battery

1.28

6.19 × 10−4

3.66

6.32 × 10−4

2

12 V, 3.6 k Wh Li-ion battery

1.21

5.51 × 10−4

2.91

8.18 × 10−4

Since the estimation is also carried out with a 12 V, 3.6 kWh lithium-ion battery [11], the following figures, Fig. 7a–f, show the plots of the estimated dynamic electrical parameters (R0 , R1 and C 1 ) of the 12 V lithium-ion battery during charging and discharging, respectively. The parameters obtained after estimation are plugged into the battery model to obtain the optimized response of the system.

5 Conclusion The proposed model performance is validated by the experimental data as input to the model. The internal circuit parameters are found to be dynamic with SOC while charge and discharging. Even though the parameters have been estimated using constant current of certain values for charging and discharging, the model can be used with dynamic values of charging or discharging current in larger systems. It is observed from the plots of the estimated parameters (Figs. 6 and 7) that the components of the equivalent impedance of the battery follow a certain trend: During charging, the solution resistance (R0 ) has an overall decreasing nature with respect to increasing SOC; and an overall increasing nature is observed with respect to increasing SOC during discharging. This trend is observed to be similar with different values for both types of batteries employed in the experiment. The variation of doublelayered capacitance C 1 with SOC is observed to be identical for both charging and discharging processes with very similar curves for both the VRF battery and the Li-ion battery. The capacitance values are observed to be very high in magnitude, which is due to the double-layered phenomenon in the electrochemical cells. As the dimensions of the electrodes and the cell remains constant, the only variable, E (permittivity of the electrolyte), should be very high due to high energy density near the electrodes. From Table 1, the sum-squared errors of the responses of the model are shown, which are observed to be in the orders of 10−4 volts. The postestimation response of the simulation model is shown in Fig. 5b, c, which accurately predicts the behavior of the battery at different SOC. Future work would include curve fitting analysis to obtain polynomial functions for all parameters obtained

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Fig. 6 Dynamic nature of parameters: a active electrolyte resistance R0 , b charge transfer resistance R1 and c double-layered capacitance C 1 —during charging for a 30 V, 7.2 kWh VRF battery (at 40 A constant current charge at 27 °C); d active electrolyte resistance R0 , e charge transfer resistance R1 and f double-layered capacitance C 1 —during discharging for 30 V, 7.2 kWh VRF battery (at 40 A constant discharge, 27 °C)

through estimation, which can be used in charge controllers, such as systems having solar PV application, to achieve accurate maximum power point tracking (MPPT) utilizing the dynamic values of impedance of the battery. Implementation of more accurate models with higher number of time constants can be implemented as a continuation of work on the current proposed system. The work has been carried,

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Fig. 7 Dynamic nature of parameters: a active electrolyte resistance R0 , b charge transfer resistance R1 , and c double-layered capacitance C 1 —during charging for 12 V, 3.6 kWh Li-ion battery (at 50 A constant charging, 27 °C); d active electrolyte resistance R0 , e charge transfer resistance R1 , and f double-layered capacitance C 1 —during discharging for 12 V, 3.6 kWh Li-ion battery (at 50 A constant current discharge, 27 °C)

assuming constancy in temperature throughout the charge/discharge cycles. Physical factors, such as variation of temperature, leakage losses and aging, play great roles in battery performance and can be taken into consideration for analysis to ensure better prediction of the battery storage performance in practical power systems.

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References 1. Zhang, X., Zhang, W., Lei, G.: A review of Li-ion battery equivalent circuit models. Trans. Electr. Electron. Mater. 17(6), 311–316 (2016) 2. Randles, J.E.B.: Kinetics of rapid electrode reactions. Discussions of the Faraday Society (1947) 3. Pop, V., Bergveld, H.J., Danilov, D., Regtien, P.P.L., Notten, P.H.L.: Battery Management Systems Accurate State-of-Charge Indication for Battery-Powered Applications, vol. 9. Springer, The Netherlands 4. Bhattacharjee, A., Roy, A., Banerjee, N., Patra, S., Saha, H.: Precision dynamic equivalent circuit model of a Vanadium Redox Flow battery and determination of circuit parameters for its optimal performance in renewable energy applications. J. Power Sour. 396, 506–518 (2018) 5. Berrueta, A., Martin, I.S., Sanchis, P., Ursúa, A.: Comparison of state-of-charge estimation methods for stationary Lithium-ion batteries. In: Presented at the Conference 42nd Annual of the IEEE Industrial Electronics Society (IECON), Florence, Italy, 24–27 October 2016 6. Ye, Y., Shia, Y., Cai, N., Lee, J., He, X.: Electro-thermal modeling and experimental validation for Lithium ion battery. J. Power Sour. 199, 227–238 (2012) 7. Saha, B., Goebel, K.: Modeling Li-ion battery capacity depletion in a particle filtering framework. In: Annual Conference of the Prognostics and Health Management Society (2009) 8. Gao, L., Liu, S., Dougal, R.A.: Dynamic Lithium-ion battery model for system simulation. IEEE Trans. Compon. Packag. Technol. 25(3) (2002) 9. Omar, N., Widanage, D., Monem, M.A., Firouz, Y., Hegazy, O., den Bossche, P.V., Coosemans, T., Mierlo, J.V.: Optimization of an advanced battery model parameter minimization tool and development of a novel electrical model for Lithium-ion batteries. Int. Trans. Electr. Energ. Syst. 24, 1747–1767 (2014) 10. Rahmoun, A., Biechl, H.: Modelling of Li-ion batteries using equivalent circuit diagrams. Przegl˛ad Elektrotechniczny (Electr. Rev.) 88, 152–156 (2012) 11. Yao, L.W., Aziz, J.A., Kong, P.Y., Idris, N.R.N.: Modelling of Lithium-ion battery using MATLAB/Simulink. In: 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013) (2013)

A Novel Switched Inductor Switched Capacitor-Based Quasi-Switched-Boost Inverter P. Sriramalakshmi

and Sreedevi V. T.

1 Introduction The voltage source inverter finds wide range of applications in stand-alone and gridconnected renewable energy systems (RES) [1, 2]. The voltage obtained from the RES needs to be stepped up owing to the low output voltage. It is necessary to connect an additional DC-DC boost converter to obtain a high AC voltage. Thus, it results in two stages of power conversion which increases the price and affects the performance of the inverter [3]. In the conventional inverter, both the devices in a single phase cannot be turned on and shoot-through is not permitted. Moreover, the shoot-through can cause short circuit in the DC supply. The drawbacks of conventional inverters are overcome by the single stage Z source inverters (ZSI) proposed in the literature [3]. It consists of an impedance network which includes two inductors and two capacitors. In this topology, both upper and lower switches in a single phase can be fired at the same time. Also, the shoot-through can boost the input voltage with high reliability. But the traditional ZSI has limitations on providing boost voltage where a low-level voltage needs to be inverted into a higher AC voltage. In recent years, many researchers are involved in bringing out novel topologies on ZSI networks, new pulse width modulation (PWM) strategies, implementation of modeling and control techniques. To avoid the inrush current, quasi-ZSI (qZSI) is proposed in [4]. Various qZSI derived topologies are discussed in [4]. For high boost applications, switched inductor-based qZSI [5–8] is used. The trans-ZSI, transformer assisted ZSI are proposed to get a high boost voltage with reduced capacitor count [9–12]. An improved trans-ZSI [13] is proposed to offer continuous input current along with boost inversion. However, large size of P. Sriramalakshmi (B) · Sreedevi V. T. Vellore Institute of Technology, Chennai Campus, Chennai, India e-mail: [email protected] Sreedevi V. T. e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_46

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inductors and diodes used in ZSI and its variants increase the size and cost of the entire system. Whenever the transformer is used in the configurations, the effect of leakage inductance needs to be handled very carefully. It results in high voltage spike in the inverter input voltage which reduces the efficiency of the converter system. The switched-boost inverter and quasi-switched-boost inverters (SBIs/qSBIs) are introduced in [14–20] which can overcome the drawbacks associated with the ZSI and qZSIs. They use reduced count of passive elements but increased count of active devices than the ZS/qZSIs. The SBI derived topologies such as CFSI which has the very same characteristics like ZSI [21], trans-qSBI [22] are suggested in which the voltage stress on the boost network components such as capacitor, diode and switch is same as that of the DC-link voltage. The SBI with four switches is introduced in [23]. The half bridge SBI with low capacitor voltage stress is proposed in [24]. The T-type-qSBI is suggested in [25]. The half bridge and full bridge SBI with continuous input current are presented in the literature. The higher voltage gain can be obtained either by paralleling various topologies or by using switched inductor (SL) cell, switched capacitor (SC) cell. Usually SL-based qSBI is used with voltage fed inverter [26, 27] and SC-based qSBI is used with current fed qSBI [28, 29]. In this paper, a SL cell and SC cell are combined together to form the boost network and to boost the overall voltage gain of the inverter topology. A novel SL-SC-based qSBI topology is designed and analyzed in this paper. The inverter is designed with a low DC input voltage of 32 V to get the DC-link voltage of 205 V and an inverted AC voltage of 113 V (rms). It is implemented in MATLAB/Simulink environment for further analysis and the simulation results are discussed in detail.

2 Single-Phase SL-SC qSBI Topology Figure 1 depicts the single-phase SL-SC-based qSBI topology which is the improved configuration of a single-phase SC-based qSBI topology of type-1 presented in [28].

Fig. 1 Single-phase SL-SC-based qSBI topology

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Fig. 2 a Shoot-through state. b Non-shoot-through state

In type-1 of the SL-SC qSBI, the negative terminal of the inverter bridge is connected to the negative terminal of the DC source as shown in Fig. 1. It has a DC source (Vin ), a boost network cascaded with a VSI. The boost network includes a SL cell and SC cell. The SL cell consists of a pair of inductors (L 1 , L 2 ) and three diodes (D4 , D5 , D6 ) and the SC cell consists of two capacitors (C1 , C2 ), three diodes (D1 , D2 , D3 ) and one active switch (S0 ).

3 Operating Principle of the Proposed Topology The proposed topology has two states of operation as in classical SBI and qSBI topologies. One is shoot-through state and the other is non-shoot-through state.

3.1 Shoot-Through State During shoot-through mode as depicted in Fig. 2a, both the inductors L 1 , L 2 , capacitor C1 are charged together and C2 is discharged. Diodes D2 , D4 , D6 are conducting and D1 , D3 , D5 are non-conducting. During this state, both the switches (S1 , S4 ) or (S2 , S3 ) in the inverter bridge are on along with the boost network switch S0 . The DC-link voltage (VPN ) is zero; hence, the inverter bridge is shorted. There is no voltage across the load during this mode of operation.

3.2 Non-Shoot-Through State During non-shoot-through state as given in Fig. 2b, the inductors L 1 , L 2 , the capacitor C1 are discharged and the capacitor C2 is charged. Diodes D1 , D3 , D5 are conducting and D2 , D4 , D6 are non-conducting. The boost network active switch S0 is off. This state is as that of the active state of the classical inverter. In this state, inverted voltage appears across the load.

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4 Steady-State Analysis of SL-SC Topology During shoot-through state, the inductor voltages (VL 1 , VL 2 ) are given by, VL 1 = VL 2 = Vin = VC2

(1)

The capacitor voltage is related as, VC1 = VC2

(2)

The DC-link voltage across the inverter bridge is obtained as, VPN = 0

(3)

The capacitor currents are given by, IC 1 = IC 2 − I L 1 − I L 2

(4)

IC2 = IC1 + Iin

(5)

During non-shoot-through state, VL 1 = Vin − VC2 − VL 2(NST) VL 2(NST) = L 2

dI L 2 dt

(6) (7)

IC2 = Iin − IPN

(8)

IC1 = −IPN

(9)

VPN = VC1 + VC2

(10)

  Applying volt second balance to inductor voltage VL 2 , (1 − D)VL 2(NST) + D(Vin + VC2 ) = 0 VC2 =

1+ D Vin 1 − 3D

Average voltage across the capacitors,

(11) (12)

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VC1 = VC2 =

1+ D Vin 1 − 3D

497

(13)

Peak DC-link voltage is obtained as,  VPN = VC1 + VC2 =

 1+ D 1+ D + V in 1 − 3D 1 − 3D

(14)

Peak DC-link current is obtained as, IPN = Boost factor B is given by, B =

(1 − D) VPN RL

(15)

2(1 + D) . (1 − 3D)

(16)

VPN Vin

B=

5 Passive Components Design Like single-phase conventional topologies, the proposed inverter too produces the low-frequency ripple content at the DC side of the topology. The design equations for ripple inductor currents are given in Eqs. (17) and (18) and only the high frequency ripples are taken for consideration. The low-frequency inductor current ripples are eliminated by adopting a suitable feedback control. The ripple content of the inductor currents can be given by, I L 1 =

Vin + VC2 DT L1

(17)

I L 2 =

Vin + VC2 DT L2

(18)

where T is the operating frequency of the inductors. The voltage ripple across the capacitors are given by, VC1 =

(I L 1 + I L 2 ) − IPN (1 − D)T C1

(19)

IPN (1 − D)T C2

(20)

VC2 =

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As presented in [28], the proposed topology can be extended to n-cell topology. By adding more number of SC cells along with SL structure, the inverter topology can be extended to multi cell SL-SC-based qSBI topology.

6 Modulation Strategy for the Proposed SL-SC qSBI Topology The principle of operation of the single-phase SL-SC qSBI inverter topology is validated using the simple boost PWM technique [3]. The single-phase peak AC voltage (Vacpk ) can be obtained by, Vacpk = MVPN = MBVin

(21)

where M is the modulation index and B is the boost factor. Gain G = MB =

Vacpk Vin

(22)

With the following limitation given in (23), the shoot-through state is inserted into the traditional zero states itself. D+M ≤1

(23)

7 Simulation Studies The simulation model of 200 W single-phase SL-SC-based qSBI is designed to verify the operating modes and steady-state analysis. The MATLAB/Simulink software is used to validate the proposed inverter. The design values of the inverter topology are inductance L = 1 mH, capacitance C = 470 µF, filter inductance L f = 1 mH, filter capacitance C f = 4.7 µF, load resistance RL = 50 . The switching frequency f s of H-bridge switches and boost network switch (S0 − S5 ) is considered as 20 kHz. The input voltage (Vin ) is taken as 32 V and the output voltage is 113 V (rms) at 50 Hz. The modulation index M and duty ratio D are considered as 0.78 and 0.22, respectively. The simulated voltage waveforms of diodes in the switched capacitor structure (SC) and voltage stress across the switch S0 are shown in Fig. 3a. It is obvious that the voltage stress across the diodes D1 , D2 , D3 is 205 V, 100 V and 100 V, respectively. The voltage stress across the switch S0 in the SC-based network is obtained as 100 V. It is obvious that the voltage stress across the boost network is very low compared to that of other conventional qSBI topologies. Figure 3b depicts the simulated capacitor voltage waveforms VC1 , VC2 of 103 V each.

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Fig. 3 a Diode voltages waveforms and switch stress. b Capacitor voltages, DC-link network and current and DC-link voltage. c Simulated waveforms of inductor and source current waveform. d Simulated waveforms of filtered load voltage and load current

The DC-link current of about 10 A is also shown. The DC-link voltage (VPN ) is obtained as 206 V which is shown in Fig. 3b. The current through the inductors L 1 , L 2 in the inductor cell is shown in Fig. 3c as 5 A each. The source current is obtained as 10 A. The filtered peak output voltages and peak output current are obtained as 160 V and 3.2 A, respectively, as shown in Fig. 3d.

8 Performance Evaluation of the Proposed Topology 8.1 Duty Ratio (D) Versus Boost Factor (B) The comparison between duty cycle (D) and boost factor (B) of the proposed converter with the conventional topologies under SBC technique is shown in Fig. 4. Compared to other topologies, the proposed topology provides a higher voltage boost and inversion. With the same modulation index, the SL-SC-based qSBI gives higher

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Fig. 4 Shoot-through duty ratio versus boost factor

gain and it improves the output voltage quality. When the duty ratio reaches 0.3, the boost factor becomes significant for the proposed topology. With the simulation studies, it is obvious that the proposed topology is able to provide the boost factor of 6.4 with the input DC voltage of 32 V and duty ratio of 0.22. It is slightly less than the theoretical calculation due to the snubber resistance and capacitance present in the active and passive switches. With the same operating parameters, ZSI, CFSI and qSBI can provide the boost factor of 1.79. The SL-qSBI provides 2.29, and SC-qSBI provides (with single cell) the boost factor of 3.58.

8.2 Modulation Index (M) Versus Voltage Gain (G) The relation between the modulation index (M) and the voltage gain (G) of various single stage boost inverter topologies is depicted in Fig. 5. It is clear from Eq. (22) that the gain (G) depends on D and M. It may also be observed from Fig. 5 that the maximum operating value of D and M is governed by D + M ≤ 1. With the reduced value of modulation index, the proposed inverter is capable of providing higher voltage gain compared to that of the traditional inverter topologies. With the modulation index (M) of 0.78 and input voltage of 32 V, the proposed topology is able to provide the peak ac voltage of 160 V with a gain (G) of 5.

8.3 Harmonic Profile Figure 6 shows the harmonic profile of load voltage of the SL-SC qSBI topology. It is observed that the proposed topology has the THD of 2.44% which very well meets

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Fig. 5 (M) versus (G)

Fig. 6 Harmonic spectrum of load voltage

the IEEE standard. Due to the absence of dead time in the switching pulses, THD is minimized compared to conventional VSI topologies.

9 Conclusion A novel single-phase SL-SC-based quasi-SBI topology is discussed in detail. The proposed topology offers a high voltage gain by replacing the inductor in SC-qSBI topology by a switched inductor cell without changing any other elements. The proposed topology is suitable wherever a high voltage gain needs to be obtained from a low DC voltage source like RES-based applications. To summarize the features of the SL-SC-based qSBI topology, performance comparison is done between the

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proposed topology and the classical inverters in terms of voltage gain, voltage boost and THD. The performance comparison exhibits that the proposed topology has higher boost factor with a minimum duty ratio, as compared to the conventional topologies. Also, it is able to produce a boost factor of 6.4 and voltage gain of 5 with a minimum duty ratio of 0.22 which is not possible with conventional topologies. Finally, the operation of the proposed topology is verified with simulated waveforms.

References 1. Erickson, R.W., Maksimovic, D.: Fundamentals of Power Electronics. Kluwer, Norwell, MA (2001) 2. Lazzarin, T.B., Bauer, G.A.T., Barbi, I.: A control strategy for parallel operation of single-phase voltage source inverters: analysis, design and experimental results. IEEE Trans. Ind. Electron. 60(6), 2194–2204 (2013) 3. Peng, F.Z.: Z-source inverter. IEEE Trans. Ind. Appl. 39(2), 504–510 (2003) 4. Anderson, J., Peng, F.: Four quasi-Z-source inverters. In: 2008 IEEE Power Electronics Specialists Conference, Rhodes, Greece, pp. 2743–2749 (2008) 5. Nguyen, M.K., Lim, Y.C., Cho, G.B.: Switched-inductor quasi-Z-source inverter. IEEE Trans. Power Electron. 26(11), 3183–3191 (2011) 6. Pan, L.: L-Z-source inverter. IEEE Trans. Power Electron. 29(12), 6534–6543 (2014) 7. Ho, A., Chun, T., Kim, H.T.: Extended boost active-switched-capacitor/switched-inductor quasi-Z-source inverters. IEEE Trans. Power Electron. 30(10), 568–5690 (Dec 2014) 8. Nguyen, M.K., Lim, Y.C., Choi, J.H.: Two switched-inductor quasi-Z-source inverters. IET Power Electron. 5(7), 1017–1025 (2012) 9. Nguyen, M.K., Lim, Y.C., Kim, Y.G.: TZ-source inverters. IEEE Trans. Ind. Electron. 60(12), 5686–5695 (2013) 10. Qian, W., Peng, F.Z., Cha, H.: Trans-Z-source inverters. IEEE Trans. Power Electron. 26(12), 3453–3463 (2011) 11. Loh, P.C., Li, D., Blaabjerg, F.: -Z-source inverters. IEEE Trans. Power Electron. 28(11), 4880–4884 (2013) 12. Mo, W., Loh, P.C., Blaabjerg, F.: Asymmetrical -source inverters. IEEE Trans. Ind. Electron. 61(2), 637–647 (2014) 13. Nguyen, M.K., Lim, Y.C., Park, S.J.: Improved trans-Z-source inverter with continuous input current and boost inversion capability. IEEE Trans. Power Electron. 28(10), 4500–4510 (2013) 14. Upadhyay, S., Ravindranath, A., Mishra, S., Joshi, A.: A switched-boost topology for renewable power application. In: 2010 Conference Proceedings IPEC, vol. 10, pp. 758–762 (2010) 15. Mishra, S., Adda, R., Joshi, A.: Inverse Watkins–Johnson topology—based inverter. IEEE Trans. Power Electron. 27(3), 1066–1070 (2012) 16. Ravindranath, A., Mishra, S., Joshi, S.: Analysis and PWM control of switched boost inverter. IEEE Trans. Ind. Electron. 60(12), 5593–5602 (2013) 17. Adda, R., Ray, O., Mishra, S.K., Joshi, A.: Synchronous-reference-frame-based control of switched boost inverter for standalone DC nanogrid applications. IEEE Trans. Power Electron. 28(3) (2013) 18. Nguyen, M.K., Lim, Y.C., Park, S.J.: A comparison between single phase quasi-Z-source and quasi-switched boost inverters. IEEE Trans. Ind. Electron. 62(10), 6336–6344 (Apr 2015) 19. Ravindranath, A., Avinash, J., Santanu, M.: Pulse width modulation of three-phase switched boost inverter. In: 2013 IEEE Energy Conversion Congress and Exposition (2013) 20. Nguyen, M.K., Le, T.V., Park, S.J., Lim, Y.C.: A class of quasi switched boost inverters. IEEE Trans. Ind. Electron. 62(3), 1526–1536 (2015) 21. Nag, S.S., Mishra, S.: Current-fed switched inverter. IEEE Trans. Ind. Electron. 61(9) (2014)

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22. Asl, E.S., Babaei, E., Sabahi, M.: High voltage gain half-bridge quasi-switched boost inverter with reduced voltage stress on capacitors. IET Power Electron. 10(9), 1095–1108 (2017) 23. Nguyen, M.-K., Tran, T.-T.: A single-phase single-stage switched-boost inverter with four switches. IEEE Trans. Power Electron. 33(8), 6769–6781 (2018) 24. Asl, E.S., Babayi, M.H.: Steady-state and small-signal analysis of high voltage gain half-bridge switched-boost inverter. IEEE Trans. Ind. Electron. 63(6), 3546–3553 (2016) 25. Do, D.-T., Nguyen, M.-K.: Three-level quasi-switched boost T-type inverter: analysis, PWM control, and verification. IEEE Trans. Ind. Electron. 65(10), 8320–8329 (2018) 26. Nguyen, M.-K., Le, T.-V., Park, S.-J., Lim, Y.-C., Yoo, J.-Y.: Class of high boost inverters based on switched-inductor structure. IET Power Electron. 8(5), 750–759 (2015) 27. Zhu, M., Yu, K., Luo, F.L.: Switched-inductor Z-source inverter. IEEE Trans. Power Electron. 25(8), 2150–2158 (2010) 28. Nguyen, M.-K., Duong, T-D., Lim, Y.-C., Kim, Y.-G.: Switched-capacitor quasi-switched boost inverters. IEEE Trans. Ind. Electron. 65(6), 5105-5113 (2018) 29. Sriramalakshmi, P., Sreedevi, V.T.: A single phase cascaded five level quasi switched boost inverter based on switched capacitor structure. In: IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)), India, pp. 1–6 (2018)

Investigation of Energy Performance of a High-Rise Residential Building in Kolkata Through Performance Levels of Energy Conservation Building Code, 2017 Gunjan Kumar, Biswajit Thakur, and Sudipta De

1 Introduction India is the world’s third largest producer and consumer of electricity with installed capacity of 344 GW as on May 31, 2018. All India per-capita electrical consumption has increased from 631.4 kWh (2005–2006) to 1075 kWh (2015–16) [1]. On volume basis, India is generating a total of 2344.2 million metric ton of CO2 emissions which is 7% of world total emissions [2] and third highest after China and USA. Under Paris agreement on climate change, India has submitted its intended National Determined Contribution (INDC) with commitments to reduce emission intensity of its GDP by 33–35% by 2030 from 2005 level [3] and having nationwide campaign for energy conservation target to save 10% [4] of energy consumption by 2018–2019. In order to achieve energy efficiency in India, Energy Conservation Act was enacted in 2001, under which Bureau of Energy Efficiency (BEE) was created in 2002. One core focus of BEE is to reduce energy intensity of commercial buildings and high-rise residential buildings through Energy Conservation Building Code (ECBC) 2007. Due to technological development in energy efficiency, execution flexibility and fast-track code implementation, second version of ECBC was launched in June 2017 [5] with provision of energy efficiency performance levels. This code prescribes the three levels of energy efficiency like ECBC baseline building, ECBC+ building and ECBC super building. Residential buildings consume about 75% of total electricity used by building sector and are second highest emitter of greenhouse gases (GHGs) after industrial sector. The generation of private building stock in urban zones is moving rapidly toward multi-storey private structures from the prior method of building singular G. Kumar · S. De (B) Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India e-mail: [email protected] B. Thakur Department of Civil Engineering, Meghnad Saha Institute of Technology, Kolkata 700150, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_47

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homes. Planned 12 million affordable houses by 2022 Government of Indian will keep the building sector rolling to new height in the years to come. The gross electricity consumption in residential buildings has been rising sharply over the years. Given the current and anticipated rapid growth in the residential building stock across India, the Energy Conservation Code for residential buildings (Part I: Building Envelope Design; Eco-Niwas Samhita 2018) was established by the Ministry of Power in December 2018 [6]. Residential buildings codes require energy efficiency performance levels, and hence, an effort is given in this paper to explore energy saving potential of high-rise residential building based on performance-level approach of Energy Conservation Building Code (ECBC) 2017. This code addresses the complex thermodynamics of a building for minimum energy performance, passive design strategies and incorporate advanced technologies to realize the code compliance. The building models were designed as per ECBC guideline with focus on envelop and HVAC system. Energy performance analysis was carried out in e-QUEST simulation software for actual design, ECBC baseline, ECBC+ and ECBC super. The obtained results compare the achievable energy performance improvements of a typical high-rise residential apartment building in the warm and humid climate of Kolkata over its actual conventional design by complying with the three ECBC specified levels. It may be useful to the policy makers for finding out the probable positive impacts following code compliance, possible scope of building energy code improvement for better code compliance, energy efficiency benchmarking and facilitate energy policy decision for this segment.

2 Methodology 2.1 Building Data Collection The building data and required drawings like floor plan , elevation and section of a high-rise residential building at Kolkata, West Bengal, India is collected along with other required construction and system details. Table 1 shows the details about the building selected for the present study.

2.2 Operation Schedule Occupancy considered is from 5 pm to 9 am on weekdays and Sundayis considered 24 h open. Day time is considered as unoccupied.

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Table 1 Input building data of Kolkata location Parameters

Description

Building type

High-rise multifamily residential

Location

Kolkata, West Bengal, India

Climatic zone

Warm and Humid

Floors

G + 19

Area,

ft2

155,103

Conditioned area, ft2

131,187

Un conditioned area, ft2

23,916

Building orientation

Longer axis is in E-W direction

2.3 Envelope Details Envelope details for built building are given in Table 2.

3 Energy Modeling and Simulation Building energy modeling and simulation closely mimic the actual building with real world at design stage itself and give performance understanding without carrying field test before going for the actual construction. It helps at early stage to optimize system Table 2 Opaque envelope specification Opaque assembly

Construction layers

Specification

Exterior wall assembly

Assembly layers: Cement plaster with sand-aggregate, 25.4 mm Brick, common, 304.8 mm Cement plaster with sand-aggregate, 25.4 mm

U-value = 1.84 W/m2 K

Roof assembly

Assembly layers: Concrete, LW, 40 Lb., 101.6 mm Concrete, HW, Dried, 140 Lb., 152.4 mm Cement plaster with sand-aggregate, 25.4 mm

U-value = 1.16 W/m2 K

Vertical fenestration

U-value = 4.9916 W/m2 K: SHGC = 0.50, VLT = 0.50

Window wall ratio

24.40%

HVAC

Split system, single zone DX, Air cooled

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design and guide for selection of most performing building materials, equipments, appliances and schedules. It serves the interest of occupant, building owner and environment. Simulation input parameters take care of the thermal comfort, visual comfort and indoor air quality as required for occupant’s productivity. It facilitates designer to select effective equipment, material and helps to opt climate responsive strategies. Owner interest is served with building energy optimal performance, scope of energy benchmarking and lower operational cost. Energy saving achieved at the end will reduce environmental impact and promote sustainable building design solution. Whole building performance method is used for energy modeling and simulation.

3.1 Description Energy Modeling Software A building following ECBC code shall need to show compliance through whole building energy simulation software that has been approved by BEE. e-QUEST is among the BEE recommended tools for building energy simulation based on DOE 2.2 platform. It is a widely used and well-accepted building energy simulation tool. Whole building energy simulation involves energy consumption prediction using software taking into consideration of integrated approach of design like building orientation, shape, climate zone, envelope, heat load, lighting load, comfort condition, equipment efficiencies, operational schedule, etc. on annual calculation of energy consumption. e-QUEST simulation tool, which runs on DOE 2.2 simulation engine, is used for the purpose of analysis. The software has the capability to model three-dimensional geometry, envelope, lighting, process and HVAC loads to accurately represent the energy consumption of a building. Design cases for different performance levels are modeled as per the ECBC guidelines to calculate the relative energy efficiency improvements over the actual as built building.

3.2 Weather File The project is located at Kolkata; thus, IND_Kolkata.428090_ISHRAE.bin weather file has been used.

3.3 Building Model Description A representative model of the considered building is devolved using the energy modeling software and elevation views of the same from two different directions are presented in Fig. 1. The details of the parameters are given in Table 3.

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Fig. 1 Views of the building as modeled in e-QUEST

Table 3 Building model input parameters Description

Unit

Actual design

ECBC baseline

ECBC +

ECBC super

Wall U-value

W/m2 °K

1.84

0.40

0.34

0.22

Roof U-value

W/m2 °K

1.16

0.33

0.20

0.20

Glazing U-value

W/m2 °K

5

3.0

2.2

2.2

0.50

0.50

0.50

0.50

50

27

27

27

Window shading

Yes

Yes

Yes

Yes

HVAC system type

Split system, single zone DX 3.4

3.5

Glazing SHGC Glazing VLT

%

Cooling EER

3.5

3.3

Lighting power density

W/ft2

0.70 (software default)

Equipment power density

W/ft2

1.90 (software default)

Occupancy

ft2 /person

200 (software default)

Zone cooling set point

°F

78

78

78

Zone heating set point

°F

68

68

68

3.4 Simulation Output Building simulation is performed in e-QUEST simulation software through whole building performance method by keeping total unmet hours less than 300. Presented simulation results highlight three levels of energy performance: ECBC, ECBC+ and

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ECBC super building as prescribed by the revised ECBC code 2017 and compare the same with actual as built case to assess the achievable performance complying with the mentioned three levels in a warm and humid climate of Kolkata.

4 Results and Discussions Based on simulation outcome, monthly load variation for space cooling, ventilation fans, Misc. equipment, area lighting and total annual energy consumption load is given in Figure 2 and overall saving is given in Table 4. Figure 2 shows variation of major monthly loads like space cooling, ventilation fans, Misc. equipment, area lighting across the year. For first two months (January, February) and last two month (November, December), the energy load seems to be low as compared to the same for remaining eight months where there is significant load as expected in Kolkata’s climate. In this study with reference to Fig. 3, it is to be noted that in annual energy consumption, Misc. equipment load and space cooling contribute maximum on load profile with further contribution from lighting load and ventilation fans load. Saving for space cooling load, ventilation load and total energy with ECBC-level approach of envelope design and HVAC system efficiency is in Fig. 4. Envelope parameter is selected as per the guideline given in ECBC 2017 for ECBC, ECBC+ and ECBC super design. Results show space cooling load and ventilation load decreases form actual design case to ECBC super design case with overall reduction by 16.17% and 20.88%, respectively. This is due to the combined effect of improved envelop and selected high star BEE rating HVAC system for given level of performance. It has been observed that the annual energy demand decreases form actual design to ECBC super design but with a value of 7.2%. A building complies with the ECBC Table 4 Annual energy consumption by end-use electricity (kWh × 1000) End uses

Actual design

ECBC baseline

Space cooling

1008.9

Ventilation fans Exterior lighting Misc. Equipment Area lighting Total

% Saving

ECBC+

% saving

ECBC super

% saving

992.2

1.65

916.9

9.11

845.7

16.17

95.3

77.7

18.46

77.0

19.20

75.4

20.88

5.1

5.1

–

5.1

–

5.1

–

1114.1

1114.1

–

1114.1

–

1114.1

–

316.6

316.6

–

316.6

–

316.6

–

2540.0

2505.6

1.35

2429.6

4.34

2356.9

7.2

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Fig. 2 Monthly energy consumption a ECBC b ECBC+ c ECBC super and d As built building

Fig. 3 Annual energy consumption pattern a ECBC b ECBC+ c ECBC super and d As built building

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Fig. 4 Percentage energy saving with ECBC-level approach of design

code using whole building performance method, when the estimated annual energy use of the proposed design is less than that of the standard design. The energy performance index (EPI) of a building is its annual energy consumption in kilowatt-hours per square meter (kWh/m2 ) of the building. The EPI ratio of a building is the ratio of the EPI of proposed building to the EPI of standard building. The EPI ratio of a building should be less than or equal to the EPI ratio of the applicable building type and climate zone of the code. Achieved result of present residential building study is in line with requirement as EPI ratio for ECBC, ECBC+ and ECBC super is 1, 0.96 and 0.94, respectively. Present study considers only the variation of envelope parameters, HVAC system rating and other significant parameter like lighting load variation, electro-mechanical equipment efficiency is considered as fixed values for all case in order to understand the impact of envelope design for annual energy consumption. However, cumulative effect of all above mentioned parameters is expected to give significant energy saving and establish the need of Energy Conservation Building Code for residential building with all components.

5 Conclusion Design of high-rise residential building in line with Energy Conservation Building Code with performance-level approach is a sustainable energy solution as it offers design flexibility and energy saving. ECBC 2017 performance-level approach for envelop and HVAC design was applied for energy performance study of residential building and to assess its saving potential. As per the provisions of the code, variation of thermal performance of the opaque and non-opaque part of the envelop and

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variation in the efficiency of the HVAC systems is considered only. Effect of the variation in other energy-consuming parameters such as exterior and interior lighting, electro-mechanical equipments is yet to be explored. Results show that the space cooling load and annual energy consumption decrease with the improved level of design, i.e., ECBC baseline, ECBC+ and ECBC super. Further exploration of the energy performance of the residential buildings in light of the ECBC Residential Code is deemed necessary for better understanding of the process.

References 1. Central Electricity Authority (CEA) (2017) Growth of electricity sector in India from 1947– 2017. CEA, Government of India, New Delhi. http://www.cea.nic.in/reports/others/planning/ pdm/growth_2017.pdf. Last accessed 14 Mar 2019 2. Chandel SS, Sharma A, Marwaha BM (2016) Review of energy efficiency initiatives and regulations for residential buildings in India. Renew Sustain Energy Rev 54:1443–1458 3. NITI Aayog. India Energy Security Scenario, 2047. NITI Aayog, Government of India, New Delhi. Available at http://indiaenergy.gov.in/iess/default.php. Last accessed 1 May 2018 4. Yu, S., Tan, Q., Evans, M., Kyle, P., Vu, L., Patel, P.L.: Improving building energy efficiency in India: state-level analysis of building energy efficiency policies. Energy Policy 110, 331–341 (2017) 5. Energy Conservation Building Code User Guide 2017 (2017). https://beeindia.gov.in/sites/def ault/files/BEE_ECBC%202017.pdf. Last accessed 14 Mar 2019 6. Bureau of Energy Efficiency (2017) Eco-Niwas Samhita 2018, Energy Conservation Building Code for Residential Buildings Part I: Building Envelope. ISBN 978-81-936846-3-4. https:// www.beeindia.gov.in/sites/default/files/ECBC_BOOK_Web.pdf. Last accessed 14 Mar 2019

Addressing Last Mile Electricity Distribution Problems: Study of Performance of SHGs in Odisha Sneha Swami and Subodh Wagle

1 Introduction Despite the push for electrification schemes specifically geared for grid extension and universal access, the current status on the ground of electricity access to poor consumers is dismal, especially in remote areas [1]. The last mile of electricity distribution remains one of the unconquered territories for distribution utilities (DU) across India. Un-metered connections, illegal connections (tapping on distribution line), poor efficacy, and efficiency in metering and bill collection are common in those areas. [2]. Un-metered and illegal connections lead to high level of losses on the system (sometimes even 40–50%), while poor billing and collection efficiency lead to high levels of revenue losses. Unscheduled power outages, low-quality of supply (frequent interruptions and low voltages), lacuna and mistakes in billing, and unresponsive consumer service lead to consumer dissatisfaction, non-payments of bills, theft, and other malpractices [2, 3]. The following are some of the problems faced by distribution utilities (DU) in the last mile of electricity distribution: • Limited Demand: Electricity consumption in rural areas is very less as compared to an urban area or on commercial feeder. As utilities earn less revenue, they give less priority for providing 24 h of supply on rural feeders [4]. • Remote Locations and Sparse Consumer Spread: Due to the remoteness of villages and thin spread of consumers, feeders are extended over long distances, however, without provision of adequate number of transformers and other equipment. This leads to the increased losses, frequent interruptions, and low quality of supply, all, in turn, leading to reducing the bill collection efficiency [5]. S. Swami · S. Wagle (B) Centre for Policy Studies (CPS), IIT Bombay, Mumbai 400076, India e-mail: [email protected] S. Wagle Centre for Technology Alternatives for Rural Areas (CTARA), IIT Bombay, Mumbai 400076, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_48

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• Accessibility Problems: Villages are often far from the substation or the DU Office located in a town where electricity bill amount is collected and complaints are registered (Field experience). For consumers, it is difficult and expensive to travel every time to reach the office and do a follow-up of their complaints. This prompts illegal access or meter tampering by consumers. • Lack of Consumer Awareness and Capability: Sometimes rural consumers are not even aware of the protocols for complaints or for getting new/metered connection or raising the complaints. This affects not just the service quality and revenue but also the accountability of DU towards the consumers. • Limited Human Resources: Addressing a large number of consumer complaints with limited human resource manpower (available with the DU) creates many predicaments for consumers. With limited manpower in hand, it is difficult to detect theft cases in all areas. • Employee Theft: In some cases, DU employees indulge in malpractices like recording lesser consumption than actual metered consumption or simply not providing meter to the consumer [6]. This affects the revenue generated by the utility. • Bill Complaints: Meter reading is the most crucial part of revenue generation process. If there are mistakes in meter reading then it takes long time and energy of consumers to rectify it. This hampers the overall services provided to consumers. To address such problems in the last mile and to improve the performance of electricity distribution sector, the provisions allowing Distribution Franchisees (DF) were introduced in Electricity Act 2003 [7]. Section 2 of the Act defines franchisee as: “franchisee means a person authorized by a distribution licensee to distribute electricity on its behalf in a particular area within his area of supply”. Section 5 has provisions for local distribution in rural areas by Panchayat institutions, co-operative societies, non-governmental organizations, and the user’s association to work as a franchise. DFs are supposed to improve bill collection efficiency and reduce losses in the specified area. DF can have a combination of functions like, • Maintenance of the Local Grid: Preventive maintenance, Repairs of the fault in the grid, repairs of the faults at consumer’s end • Handling the Metering Cycle: Meter reading, preparing bills, distributing bills, collecting bill amount • Addressing Consumer Grievances: giving new metered connections, solving consumer complaints regarding meter, bills, or connection problems, • Help DU in Controlling Theft. Over the past 10 years, various utilities have attempted to adopt different versions of the DF model. While a handful of DFs have been operating successfully, some of these DFs were aborted at the bidding stage itself and others were terminated due to various problems [8]. As most of the DFs are operating at the district-level, they require significant capital investment and incur high operational costs. In most cases, districts having very high losses were selected by DUs for the DF experiment. Torrent Power in Bhiwandi, Maharashtra is one of the most successful examples of

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DF which managed to bring down losses from 73 to 24% [9]. But, apart from this DF, DFs in different states could not accomplish the given targets and hence have been shut down. The main reasons for terminating DF contracts include: huge amounts of debt, failure to improve bill collection efficiency, increased consumer complaints, and failure to reduction in losses consistently [10, 11]. Many cases of DF show that transition of responsibility from DU to DF did not make much of positive difference in performance, rather, in some areas, it made the situation worse (i.e., increased losses and consumer complaints). DFs tried to change the style of management but they could not effectively address ground-level problems in the last mile of the distribution network. Though there is one case in Andhra Pradesh State where women SHGs were contracted for metering-billing and revenue collection activities. There were 12 rural feeders in tribal areas which were maintained by self-help group named IKP Mandal Mahila Samakhya. This area prominantly has power theft by direct hooking, and it is difficult to arrest theft since it is influenced by Maoist movement. Along with SHGs tribal youth is also engaged and the consumer complaints are being redressed with in a stipulated time. It is not only creating employment, but also it has yielded very fruitful results. The line losses were reduced from 47.33% to 20.26% over a span of 2 years [12]. In many other cases policy documents and DF contracts articulate targets and responsibilities of DFs, they fail even to mention the need for or measures for building capabilities of DFs to handle the multi-faceted, chronic, and complex problems in the last mile of the distribution network. On this background, few DFs from Odisha employed an innovative strategy of appointing SHGs from villages to handle tasks of distribution of bills and collection of payments. This paper presents the research understanding this institutional innovation and carries out preliminary assessment of its contribution to improvement in the performance of DF in the last mile.

2 Methodology This paper presents the research prompted by the previous discussion on difficulties faced by DFs and the possible remedy of involving community-level organizations such as SHGs in the last mile operations in the electricity distribution sector. The research question is articulated as: How local institutions like SHGs, NGOs and PRIs could address last mile functions in the electricity distribution sector? To answer the research question, the study focused on the initiative of one districtlevel distribution franchise (DF) called Feedback Distribution Company (FEDCO) from Odisha, which engaged SHGs for the last mile operations in its area of work. For this study, semi-structured interviews were carried out in Hindi and English languages with field and office staff of FEDCO and SHGs involved in the process of implementing this SHG model under distribution franchise. All interviews were transcribed in English for thematic analysis. Contract documents, collection, and

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billing efficiency data was collected from the franchise office and analyzed in a systematic manner.

3 SHGs in the Last Mile of Electricity Distribution 3.1 Organizational Structure and Working Feedback Distribution Company (FEDCO) is the DF working under the DU in Odisha called Central Electricity Supply Utility (CESU). FEDCO is operating in 16 divisions of one district and has engaged 142 SHGs for handling tasks of metering, billing, and collection of payments (MBC). Figure 1 shows the organizational structure of FEDCO. The distinctive and novel features of this organizational structure are as follows: • SHG Management Team: This team consists of one Manager and two Assistant Managers, who are socially active and educated persons from the local area. They help FEDCO in communicating with women of the SHGs. They help SHG members to solve community-level issues by personally visiting the field locations. They keep the track of performance of all SHGs and focus on the improvement of teams with poor performance. Before starting the work FEDCO’s officials and SHG management team conducted community meetings in villages to make consumers aware of paying bills and checked the status of metered connection in villages.

Fig. 1 Organizational structure of FEDCO operations and SHGs

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• Consumer Care Centre: This Centre is operational 24-by-7. Consumers can call on the toll-free number provided to them for registering their complaints regarding electricity supply or fault. • Analytics Team: This team works to identify theft cases from the analysis of available consumption data. It also monitors volume of work handled and overall performance of SHG members. • Vigilance Team: This team detects theft cases and takes action against them. SHG members and Analytics team provide them with information on suspected theft cases. Table 1 describes specific tasks delegated to SHGs under six broad areas of responsibilities. To ensure good performance from SHGs on these tasks, FEDCO conducted 3 day-long sessions for training and building capabilities before SHGs started their operations. Apart from training on procedures to follow, members of SHGs were also trained to operate machines that are used for taking meter reading and making calculations for bills. FEDCO also has stipulated operating procedures for members of SHGs to carry out the tasks assigned to them. For example, if a consumer is not available at home they have to visit again to take the meter reading. Most of the villagers decide the day on which they will pay the bill, SHG members have to collect the bill amount on that day. SHGs get paid in the form of incentives. Money is transferred to their registered bank accounts every month. Existing incentive structure is mentioned in Table 2. FEDCO staff members along with SHG teams conducted community meetings. During those meetings, they tried to solve past billing related problems as well as made them aware of paying future bills timely. Awareness was spread via SHG members, meter readers, banner, declaring in speakers, and printed leaflet. This helped them to fetch the arrears from rural consumers. Table 1 Areas of responsibilities and specific tasks of SHGs Broad areas of responsibilities

Specific tasks

Meter reading and billing

Metering and billing cycle starts after 5th of every month Infra-Red meter reading, mobile-based and manual entry Trained for using these machines and understand bill components

Bill collection

Collection starts after 15th of the month Receipt of payment is given to the consumers after payment

Arrears collection

Collection drive after every 3 months Section officers accompany for collection of arrears

Consumer complaints

Consumer complaint form is with members which they fill up For urgent complaints, they call section office or field staff

New connection

SHG members fill the form with them Guide consumers about the further procedure

Theft

Community members inform SHG members about suspected cases

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Table 2 Incentive structure for SHG’s payment Billing

Collection

Rs. 3/– per meter reading

6% on total collection

Rs. 5/– per extra bill generated

Additional 2% incentive on achieving collection efficiency greater than 100%

Additional incentive of Rs. 2/– per bill, for billing new service connections

Additional 1% incentive on net current collection from 90 to 100% of the current billed amount

Table 3 Present Practices to address last-mile electricity distribution problems Last-mile functions in the electricity distribution

Present practices in FEDCO’s area

Provision of adequate, quality, reliable, affordable supply of electricity

It is not outsourced to local institutions nor to DF. DU has all the responsibility and control over it

Management of Supply provided to franchise, Management of resources (financial and manpower), repair and maintenance of the system in distribution area

DU only informs DF of outages or DF informs DU on maintenance requirements. Odisha, having surplus power, does not have load shedding for many years

Revenue generation (meter reading, billing and All LV-side infrastructure and consumer collection), responsibilities of staff members complaints are handled by DF (FEDCO’s) field staff. HT side maintenance is under DU (CESU) Grievance redressal and conflict resolution

This is completely under FEDCO. They have SHGs and meter readers for MBC activities

Expansion of distribution system for adding new consumers and total increase in supply due to increased consumption

Consumer grievances are handled by FEDCO

While adopting this institutional innovation, DF has also invested in infrastructure and manpower to improve the supply and services in villages. For example metering technologies used are easy to handle and can be monitored by the sensors. Thus, DF is able to monitor their field employees and hold them accountable for their work. As SHGs are only doing the MBC, tasks they cannot be held accountable for is quality of supply provided, franchise is responsible for that. From all the interviews present practices followed in last-mile connectivity functions, in the Nayaghar district are listed in Table 3.

3.2 Lessons from FEDCO’s Innovation Some interesting insights were brought out through the interaction with staff members of DF and SHGs. Employing SHGs to take MBC tasks in the last mile

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of electricity distribution was not aimed at addressing all the problems in the last mile of distribution mentioned before. However, the innovation did help address some. • Improving Consumer Awareness and Capability: As SHG members are always present in the village, consumers report their complaints over a phone call and don’t have to go to the office every time. Easy access to a person representing DF has helped consumers to report their complaints immediately and take follow-up. Any doubts on the part of the consumer about a new metered connection, meter reading, or bill can be addressed immediately. The trained SHG members act as resource persons for villagers. • Reducing Electricity Theft: Women SHG members were found to be more effective in handling the theft-related tasks. As women working in SHG and consumers are from the same community, identifying theft is easier for these women, sometimes from neighbors of the consumer indulging in theft. Over the tea and casual conversations, they get to know if someone is stealing the electricity. Being women, they can easily go inside the homes and check if meters are tempered. • Reducing Employee Theft: FEDCO has started monitoring of meter reading and bill collection work of each employee, including SHG members. They analyze the data from GPS tracker, the submitted readings, bill collection, and talk to the SHG members personally if their performance is poor. Also apart from monitoring, other positive incentives are provided, such as prizes to best-performing SHGs. • Reducing Billing Complaints: Many meter reading complaints arise due to lack of mechanism for cross-checking of entered reading against actual reading. Taking and uploading a photograph of the meter reading is mandatory in the process of bill preparation. The photographs are used for cross-checking by the team in the head office of FEDCO. Any discrepancies found are reported on the same day and corrected. As bills are generated on the spot, consumers can also check its accuracy. This has resulted in significant improvement in efficiency of billing and collection. This also keeps SHG members accountable to enter correct meter reading and brings transparency in the system. • Provision of Adequate Human Resources: Engaging SHG members for these activities has created additional human resources in the last mile, at very low costs compared to hiring full-time employees. The rate of attrition is very low for these local women. This trained and experienced work force in the last mile has helped DF address last mile problems in fast and effective manner, immensely improving the quality of service provided to consumers. As another evidence of improvement in performance, Fig. 2 shows reduction in Aggregate Technical and Commercial (AT&C) losses in the Nayaghar district from 59 to 39% which is the highest percentage reduction in areas under FEDCO.

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Fig. 2 Performance of SHGs

4 Conclusions The paper underscores the need to find new and innovative solutions to address the well-known and chronic problems in the last mile connectivity of the electricity distribution sector, especially in rural areas. SHGs appear to be an attractive solution to the failure of Distribution Franchisee, especially in view of the credibility, grounding, rapport, and intimate knowledge possessed by SHGs of local consumers and community. With the required training and capabilities, SHGs can surely address the problems related to electricity theft, consumer complaints, and lack of human resources which will eventually improve consumer satisfaction. Strict monitoring by DF will bring accountability and transparency in the system. The observations and analysis of initiative in Odisha presented in this paper further the expectation that SHGs can be one possible solution for the vexed problems in the last mile of the electricity distribution sector. The research presented in this paper is still undergoing and is expected to bring in more insights and better understanding of this solution.

References 1. Ramji, A., Soni, A., Sehjpal, R., Das, S., Singh, R.: Rural energy access and inequalities: An analysis of NSS data from 1999-00 to 2009-10, TERI-NFA Working Paper No. 4, The Energy and Resources Institute (2012) 2. Chaurey, A., Ranganathan, M., Mohanty, P.: Electricity access for geographically disadvantaged rural communities-technology and policy insights. Energy Policy 32(15), 1693–1705 (2004)

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3. Bhattacharyya, S.C.: Energy access problem of the poor in India: Is rural electrification a remedy? Energy Policy 34, 3387–3397 (2010) 4. Prayas Energy Group, Electricity Supply Monitoring Initiative (ESMI) data, https://www.wat chyourpower.org/index.php. Accessed on 12th Feb 2019 5. Gibson, J., Olivia, S.: The effect of infrastructure access and quality on non-farm enterprises in rural Indonesia. World Dev. 38, 717–726 (2010) 6. Sharma, T., Pandey, K.K., Punia, D.K., Rao, J.: Of pilferers and poachers: combating electricity theft in India. Energy Res. Soc. Sci. 11, 42–52 (2016) 7. Electricity Act 2003, Central Electricity Regulatory Commission. http://www.cercind.gov.in/ act-with-amendment.pdf. Accessed 27 Jan 2019 8. Business line: Maharashtra Govt’s Discom wants to rope in private players in six more cities, Dated on 19 Oct 2017. http://www.thehindubusinessline.com/companies/maharashtra-govtsdiscom-wants-to-rope-in-private-players-in-six-more-cities/article9916037.ece 9. Tankha, S., Misal, A.B., Fuller, B.W.: Getting reforms done in inhospitable institutional environments: untying a Gordian Knot in India’s power distribution sector. Energy Policy 38, 7121–7129 (2010) 10. Thakur, T., Bag, B., Prakash, S.: A critical review of the franchisee model in the electricity distribution sector in India. Electricity J. 30, 15–21 (2017) 11. Banks, J.P., Bowman, C.D., Gross, T.P., Guy, J.: The private sector: cautiously interested in distribution in India. Electricity J. 11, 21–28 (1998) 12. Success Stories, REC Institute of Power Management and Training. http://www.recipmt.com/ success.php. Accessed 3 Feb 2019

Transient Stability Analysis of Wind Integrated Power Network Using STATCOM and BESS Using DIgSILENT PowerFactory Neha Manjul and Mahiraj Singh Rawat

1 Introduction Unlike conventional generators, the wind energy produces stochastic power output due to inherent wind characteristics. In recent years, the power production from wind energy resources has increased exponentially. Therefore, the stability of power system becomes an important issue in modern power systems. The transient stability is among one of the electrical power system stability. According to IEEE/CIGRE joint task report [1] “Transient stability is concerned with the ability of the power system to maintain synchronism when subjected to a severe disturbance, such as a short circuit on a transmission line.” In Denmark, the power output from wind energy supplies more than 20% of the local electricity consumption and the aim of the Danish government to increase this share to 50% by 2025 [2, 3]. The voltage and transient stability of wind farms integrated into the power grid was studied in [4]. The ability of fast response under large disturbances, the power converter of doubly-fed induction generators (DFIGs) enables wind farm to reach steady-state conditions much faster than conventional generators. In [5], the sensitivity-based method was investigated in order to determine the impact of DFIG based wind turbine generators (WTGs) on small-signal and transient stability of power systems. The intermittent generation characteristics of wind farms lead to fluctuating power output which may further push the stability margin to its limit. In [6], a method was proposed for fast assessment of the transient stability margin (TSM) considering the uncertainty of wind generators. In [7], a wide area control (WAC) was proposed to enhance the transient stability of the DFIG integrated power grid. A real-time method for the assessment of transient stability of a power system comprising wind N. Manjul (B) · M. S. Rawat National Institute of Technology Uttarakhand, Srinagar Garhwal 246174, India e-mail: [email protected] M. S. Rawat e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_49

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turbine was proposed in [8]. The proposed real-time method utilizes the corrected kinetic energy method for determining the critical clearing time. In [9], a coordinated control scheme for superconducting magnetic energy storage (SMES) devices in the wind power integrated system were investigated in order to enhance the overall transient stability of the system. To enhance the transient stability of large power system the researchers had applied the STATCOM and BESS [10]. It is observed that power system integrated with DFIG based wind farms was less sensitive to transient disturbances such as fault clearing time, voltage sags and wind penetration below a certain threshold. Above threshold the wind farm has adverse effect on transient stability of system [11]. Authors in [12] had utilized energy capacitor system (ECS) comprised of electric double layer capacitor (EDLC) and power electronics device to improve the system transient stability. In [13], the performance of the DFIG based wind farm on different transient disturbances had investigated. In this paper, the transient disturbances such as sudden load change, three-phase faults, sudden change in wind speed, and wind gust are investigated in a power system comprising wind farms. To enhance the system transient stability, the devices, i.e., BESS and STATCOM are also considered. The paper is structured as follows: Sect. 2 explains the model of DFIG based wind farm and its control. Section 3 discusses the BESS and its control. Section 4 describes the STATCOM and its control. Finally, Sect. 4 concludes the results. The standard IEEE 14 bus test system is used to obtain the results. All simulation studies have performed in DlgSILENT PowerFactory software.

2 Modeling of DFIG Based Wind Power In literature [14–16], different models of DFIG based wind power for the transient stability study have been proposed. The schematic diagram of the grid integrated DFIG is represented in Fig. 1. The DFIG based wind turbine has the ability to independently control the active and reactive power during transient disturbances. In this paper, the DFIG based wind turbine model available in PowerFactory library have utilized. The block diagram of DFIG with its controllers is shown in Fig. 2.

3 Modeling of BESS and Its Control The BESS technology provides fast active power compensation in power systems during transient disturbances. The BESS technology comprises of two parts: battery and rectifier/inverter. A voltage source converter (VSC) functions as a rectifier and inverter during charging and discharging, respectively. The schematic diagram of a typical battery storage is represented in Fig. 3. The state of charge (SOC) defines the current status of battery. If SOC is one, then the battery is fully charged while SOC is zero, then the battery is fully discharged.

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DFIG

Tranformer Grid

Gear Box DC

DC AC

AC

RSC

GSC

Fig. 1 Grid integrated DFIG based wind power SpeedRef speed

Pitch Control

Turbine

pw

Shaft

vw

pt

MPT

Slow freq Meas.

Pref-in

Fmeas

Over freq power reduction

Pref

PQ control

Ird_ref Irq_ref

Ir Control

Pctrl,qctrl

Compen sation

Cosphiu, sinphiu

PQ total

Ird Irq

Protection

Vac bus

usr, usi

DFIG

Psir_r,qsir_j Cosphiref, sinphiref Cosphim, sinphim

Theta meas.

Id, Iq

Speed Control

Vac gen

beta

Current Measurement

Irot

Fig. 2 Block diagram of DFIG with controllers (PowerFactory library model) Udc

0 0

PQ Measurement

p

1

DC Side Calculation

l

Battery Model

1 3 4

Fig. 3 Block diagram representation of battery

Ucell SOC ICell

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AC- Voltage Measurement

Cosref;sinref

vin

frq

Frequency Measurement Frequency Control

dpref

Id_ref_out; Iq_ref_out

PQ-Control

PQ Measurement

Id_ref_in; Iq_ref_in

Ucell

Battery Model

Converter

SOC

Charge Control

Icell deltai

Fig. 4 Block diagram representation of composite model of BESS

There are two main constraints on the system: first is the rated power/current of the converter and the second is the capacity of the battery that is the amount of stored energy. In this paper, the BESS of 30 MVA capacity has utilized. The composite model of battery with its controller is shown in Fig. 4. The voltage output from a typical BESS can formulate using Eq. (1). UDC = Umax × S OC + Umin × (1 − S OC) − I Z i

(1)

where SOC: State of charging; U max : Maximum voltage output; U min : Minimum voltage output; I: current; Z i : equivalent internal impedance.

4 Modeling of STATCOM and Its Control The static compensator (STATCOM) is a FACTS device connected in shunt position and used for reactive power compensation in the transmission network. The schematic diagram and V-I characteristics of STATCOM is shown in Fig. 5. The basic STATCOM device consists of VSC, coupling transformer, and capacitor bank. The STATCOM has the ability to supply/absorb reactive power independent with system voltage at the point of common coupling (PCC) during a transient disturbance. The control scheme for typical STATCOM is shown in Fig. 6.

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VAC

1.0

VCON

VSC VDC

Ic

Icmax

0

ILmax

IL

Fig. 5 STATCOM and V-I characteristics

Voltage DC Meas

Vdc

Voltage AC Meas

Vac

PLL Sinref, cosref

Id_ref

VDC /VAC Convertor Q Meas

PWM Convertor Iq_ref

qac

Fig. 6 Control scheme of STATCOM

5 Simulation and Results In this paper, the IEEE 14 bus test system is utilized to investigate the transient stability of the system comprising of the DFIG based wind turbine which is shown below in Fig. 7. The test system consists of five generators (Synchronous generator -02 Nos, Synchronous Compensator -03 Nos), 17 transmission lines, 11 constant load demand, 19 buses, and 8 transformers. The total system active and reactive load demand is 259 MW and 73.5 MVAr, respectively. The 30% load scaling has set for the test system. In order to compensate for the increased load demand, the 13 Nos. DFIG based wind turbine having a 6 MW capacity each has integrated into the test system. The transient stability of test system comprising DFIG based wind farms

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Bus_13 G1 Bus_14

Bus_12

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Bus_9

G8

G6

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WTG 1

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+ -

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Fig. 7 IEEE 14 bus test system

in the presence of STATCOM and BESS has been investigated during following transient disturbances. 1. 2. 3. 4.

Sudden change in load demand Three-phase fault in transmission line Sudden change in wind speed Effect of Wind gust.

The following cases are taken into consideration. Case 1: Test system with DFIG based wind farm Case 2: Test system with DFIG based wind farm, STATCOM, and BESS.

5.1 Sudden Change in Load Demand The sudden change in load demand is considered as a transient event. The sudden increase in load demand at selected bus can introduce system transients. In this paper, the power system performance is investigated for 20% increase in load demand at bus 3, 4, and 9 at after 1.0 s. The temporary difference in the power balance between

Transient Stability Analysis of Wind Integrated Power Network … 1.02

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Fig. 8 Variation of various parameters under sudden load change

the mechanical and electrical power of each generator can lead to acceleration of rotor angle for the whole system. The variation in various parameters of different elements of the IEEE 14 bus test system is represented in Fig. 8. It is observed from Fig. 8, the transient response of various parameters in case 2 has better performance compared to case 1.

5.2 Three-Phase Fault in Transmission Line The most severe disturbance in the power network is three-phase fault on the transmission network. In this paper, a symmetrical three-phase fault is created at 1.0 s on transmission line connected between bus 2 and 3, which is cleared at 1.5 s. When three-phase fault occurs in the system, the dynamics during the post fault can

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become unstable because of inadequate damping supplied from the generator. The combination of BESS and STATCOM offers an additional degree to add damping in the system and assist with mitigating the instability problem. The results obtained through simulations are represented in Fig. 9. 1.3

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Fig. 9 Variation in different parameters under three-phase fault

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5.3 Sudden Change in Wind Speed The simulation is started normally and sudden increase in wind speed from 11.019 m/s to 14 m/s. When the wind speed is increased, the negative slip is also increased, therefore the power delivered from the stator side is decreased and power delivered from rotor side is increased. The system gets back in stable state after some seconds. When generator speed is increased then the pitch angle of the system settles to a new value. The power delivered by the generators also settles to a new value so that the maximum power can be achieved from the new speed. The simulation results are shown in Fig. 10. 15

50.03

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Active power (MW)

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26 25.8 25.6 25.4 25.2 25

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Fig. 10 Variation in different parameters under sudden change in wind speed

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Active Power (MW)

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0

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Fig. 11 Variation in different parameters under wind gust transient condition

5.4 Effect of Wind Gust A wind gust starting at 2 s and ending at 5 s is simulated. All parameters are settling back to its original position. Hence a wind gust can reach its original position of the event, but this event takes a longer time to settle down to the steady-state. The results obtained from the simulation are represented in Fig. 11.

6 Conclusion In this paper, the transient stability of a power system comprises of the DFIG based wind farm is investigated. To enhance the transient stability of the system, STATCOM

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and BESS are applied. The transient disturbances, i.e., sudden change in load demand, three-phase fault on the transmission line, sudden change in wind speed, and the effect of wind gust has investigated on wind energy integrated power system with/without STATCOM and BESS. It is observed that STATCOM and BESS are the most effective solution to improve the transient stability of a power system with wind farms. They can absorb or produce active and reactive power, according to the requirement under transient events.

References 1. Kundur, P., Paserba, J., Ajjarapu, V., Anderson, G., Bose, A., Canizares, C., Harziargyriou, N., Hill, D., Stankovic, A., Taylor, C., Cutsem, T.V., Vittal, V.: Definition and classification of power system stability. IEEE Trans. Power Syst. 19(2), 1387–1401 (2004) 2. Muyeen, S.M., Tamura, J., Toshiaki, M.: Stability augmentation of a grid-connected wind farm, 1st edn. Springer, London (2009) 3. Fox, B., Bryans, F., Flynn, D., Jenkins, N., Milborrow, D., O’Malley, M., Watson, R., AnayaLara, O.: Wind Power Integration: Connection and System Operational Aspects, 2nd edn. IET, United Kingdom (2014) 4. Muljadi, E., Nguyen T.B., Pai M.A.: Impact of wind power plants on voltage and transient stability of power systems. In: IEEE Energy 2030 Conference, pp. 1–7. IEEE, Atlanta, GA, USA 5. Gautam, D., Vittal, V., Harbou, T.: Impact of increased penetration of DFIG based wind Turbine Generators on transient and small signal stability of power systems. IEEE Trans. Power Syst. 24(3), 1426–1434 (2009) 6. Hua, K., Mishra, Y., Ledwich, G.: Fast unscented transformation based transient stability margin estimation incorporating uncertainty of wind generation. IEEE Trans. Sustain. Energy 6(4), 1254–1262 (2015) 7. Yousefian, R., Bhattarai, R., Kamalasadan, S.: Transient stability enhancement of power grid with integrated wide area control of wind farms and synchronous generators. IEEE Trans. Power Syst. 32(6), 4818–4831 (2017) 8. Tajdinian, M., Seifi, A.R., Allahbakhshi, M.: Transient stability of power grids comprising wind turbines: new formulation, implementation, and application in real time assessment. IEEE Syst. J. 13(1), 894–905 (2019) 9. Jiang, H., Zhang. C.: A method of boosting transient stability of wind farm connected power system using S magnetic energy storage unit. IEEE Trans. Appl. Superconduct. 29(2) (2019) 10. Kanchanaharuthai, A., Chankong, V., Loparo, K.A.: Transient stability and voltage regulation in multimachine power systems vis-à-vis STATCOM and battery energy storage. IEEE Trans. Power Syst. 30(5), 2404–2416 (2015) 11. Chowdhury, M.A., Shen, W., Hosseinzadeh, N., Pota, H.R.: Transient stability of power system integrated with doubly fed induction generator wind farms. IET Renew. Power Gener. 9(2), 184–194 (2015) 12. Muyeen, S.M., Takahashi, R., Ali, M.H., Murata, T., Tamura, J.: Transient stability augmentation of power system including wind farms by using ECS. IEEE Trans. Power Syst. 23(3), 1179–1187 (2008) 13. Baggu, M.M., Chowdhury, B.H.: Performance of doubly fed induction machine windgenerators during grid and wind disturbances. In: 38th North American Power Symposium, pp. 49–56. IEEE, Carbondale, IL, USA 14. Ledesma, P., Usaola, J.: Doubly fed induction generator model for transient stability analysis. IEEE Trans. Energy Convers. 20(2), 388–397 (2005)

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15. Kim, D.J., Moon, Y.H., Nam, H.K.: A new simplified doubly fed induction generator model for transient stability studies. IEEE Trans. Energy Convers. 30(3), 1030–1042 (2015) 16. Coughlan, Y., Smith, P., Mullane, A., O’Malley, M.: Wind turbine modeling for power system stability analysis—a system operator perspective. IEEE Trans. Power Syst. 22(3), 929–936 (2007)

Experimental Investigation of Solar Drying Characteristics of Grapes S. P. Komble, Govind N. Kulkarni, and C. M. Sewatkar

1 Introduction Drying helps in the preservation of agricultural food products. Open sun drying is the oldest drying method. This method has limitations of being unprotected from rain, wind-borne dirt and dust, insects, rodent, and other animals. Solar drying was explored to understand the drying characteristics of many agricultural products. Lewis [1] first presented a mathematical model to describe drying characteristics. An exponential model for drying of porous products was proposed. In this model drying coefficient varied with the rate of diffusion, surface evaporation, and thickness. Page [2] reported the effects of temperature and relative humidity of the drying air and proposed an expression for moisture content and drying time. A review of various drying models was done [3]. The models in the review were evaluated on the basis of coefficient of correlation (r), reduced chi-squared (χ 2 ), RMSE, and MBE values. The review observed that Page’s model provided highest value of the coefficient of correlation and least value of reduced chi-square. Experiments were carried out on grapes in Turkey [4]. Grape behavior is very well related by two-term model. As far as kinetic studies are concerned, experimental investigation recorded that Page’s model was more fitting to Thompson seedless grapes for determining drying constants [5]. An analytical model of drying chamber air and grapes was presented [6]. The authors concluded S. P. Komble · C. M. Sewatkar (B) Department of Mechanical Engineering, Govt. College of Engineering and Research, S. P. Pune University, Avasari, Pune, India e-mail: [email protected] S. P. Komble Department of Mechanical Engineering, Vishwakarma Institute of Technology, S. P. Pune University, Pune, India G. N. Kulkarni Anna Saheb Dange College of Engineering and Technology, Ashta, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_50

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that latent heat of vaporization of grapes was higher than that of free water. The latent heat was found to be the function of the moisture content of the grapes. Many researchers investigated drying air temperature, relative humidity, velocity, effects of initial and final moisture content [7–20]. Research work was also undertaken on solar drying kinetics of grapes by forced convection with various available thin-layer models. It is observed that not much attention is given to the natural convection solar drying process with thin-layer models. In the presented experimental work dry run tests identified the maximum mean temperature inside the dryer for loading the product. The study focused on the applicability of thin-layer mathematical models for solar drying of grapes placed in a cabinet type natural convection solar dryer. The study was carried out in the climatic conditions of Pune, India.

2 Experimental Setup Figure 1 shows the schematic diagram of natural convection cabinet type double glazing solar dryer with auxiliary electric heating arrangement and dimensions 2 m length, 1 m width and 0.8 m height. Figure 2 shows the actual photo of solar dryer system. A black-coated sheet is fitted below the glass cover to prevent direct heating of the products. This sheet acts as absorber plate. The sidewalls and base of the solar dryer were insulated. For air circulation inlet and outlet pipes are provided at the front and top of the dryer, respectively. Product to be dried is loaded on the trays. Electric heaters of 3 kW ratings are provided at the bottom portion inside the dryer. Though heaters are an integral part of the dryer, electric heaters are not used in the present experimentation. The ambient air enters through the inlet pipes of dryer. Through the glass glazing, solar radiation strikes the absorber plate. The absorber plate becomes hot and radiate

Fig. 1 Schematic diagram of natural convection cabinet type double glazing solar dryer

Experimental Investigation of Solar Drying Characteristics …

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(a) Front view of solar dryer

(b) Rear view of solar dryer Fig. 2 Actual photo of solar dryer system

heat in the dryer space. Incoming air becomes hot. Hot air passes over the products to be dried, heats up the product, removes moisture from the product, and rises towards the outlet. The product under experimentation in this work is grapes. Grapes are pretreated before loading into the dryer. Initially, grapes were dipped into ethyneoleate oil of 250 ml, solution of potassium carbonate of 300 gm, and water for 5 min to increase the permeability of waxed coat. In this experiment, 15.6 kg of grapes were used. To improve the quality of drying, the grapes were sprayed by the solution of oil and carbonate for three days. Grapes

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were dried for 8–9 days. During this period measurements were taken from 10 to 17 h with an interval of 1 h each day. Gradual growth of grapes is shown in Fig. 5.

3 Result and Discussion 3.1 Dry Run Tests on Solar Dryer To know the performance of dryer without load, the dry run tests involved running the dryer in the following conditions Case 1: Inlets and outlets—open, Case 2: Inlets and outlets—closed Case 3: Inlets and outlets—closed with heaters

Temperature, ºC

Case 4: Inlets and outlets—open with heaters. Temperature of the air in the dryer changes with time and space, therefore, maximum mean temperature of air in the dryer is recorded in all the four cases. This information is used as a guide to load the product. Figure 3 demonstrates variation of mean temperature of air in the dryer for the above cases from 1000 to 1700 h with an interval of 1 h. The global radiations are measured at these time instances using pyranometer. It is observed that the dryer temperature, ambient temperature, and global radiation are increased from morning to noon and after that decreases towards evening which is as expected. Difference between dryer temperature and ambient temperature represents 160

Case1

140

Case2 Case3

120

Case4 100 80 60 40 20 10

11

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Time, hrs Fig. 3 Variation of average instantaneous temperature

15

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Experimental Investigation of Solar Drying Characteristics …

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Case4 600 400 200 0 10

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Time, hrs Fig. 4 Variation of average radiation intensity with time over the period

the heat gain in inlet air inside the dryer. The corresponding variation in the radiation intensity during the same time period is shown in Fig. 4, which confirms the results of temperature variations. These tests were conducted from 31st March to 3rd April 2017; each day corresponds to the respective case. In case 1, all the inlet and outlet pipes were kept open. The rate of air circulation in the dryer became the highest. The air temperature in the dryer, therefore, observed to be almost uniform at all the locations. Difference in the dryer and ambient air temperature was also minimal in this case. In case 2, all the inlet and outlet pipes were closed. Auxiliary electric heaters were not operated. This has increased the average temperature of the dryer as there was no airflow within the dryer. In case 3, all inlet and outlet pipes are closed. There was no convection current. The average temperature inside the dryer, therefore, shoots up substantially. In case 4, all the inlet and outlet pipes were opened. Auxiliary electric heaters were operated. The dryer air temperature, in this case, is higher than case 1 and 2.

3.2 Test Results with Loaded Trays With the help of photographs, the progressive stages of drying of grapes over 8 days are shown in Fig. 5. The change in the color of the grapes confirmed the conversion into raisins. This process required 8 days with the proposed dryer. The measurement of moisture content of the grapes and raisins was carried out using moisture analyzer. It was found that initial moisture present in the grape was

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Fig. 5 Growth of grapes to raisins from day 1 to day 8

77% which reduced to 13.6% after complete drying of the grapes into raisins. This is the safe moisture content limit for obtaining good quality raisins with better taste and odor.

3.3 Drying Characteristics of Grapes In the present work, drying was conducted in the solar dryer for eight days. The average velocity and temperature noted at inlet pipes and outlet pipes over the period of eight days were 0.68 m/s and 41.1 °C, respectively. Moisture contents were determined using correlations given in Table 1. Due to natural convection relative humidity varies continuously so MR (moisture ratio) is taken as M − Me /Mo − Me . The value of R (correlation coefficient) and reduced ψ 2 helps us to understand best fitting of equation. The expressions for ψ 2 and R2 are given below: 2 N  n=i MRexp i − MRpre i ψ = N −n 2

Table 1 Mathematical models applied to the drying curves [4]

(1)

Thin-layer drying model

Model name

MR = exp(−kt)

Newton

MR = exp(−kt n )

Page

MR =

exp(−kt)n

Modified page

MR = a exp(−kt)

Henderson and Pabis

MR = a exp(−kt) + c

Logarithmic

MR = a exp(−k0 t) + b exp(−k1 t)

Two-term

MR = 1 + at + bt 2

Wang and Singh

Experimental Investigation of Solar Drying Characteristics …

  N  n=i MRexp i − MRexp MRprei − MR pr e R = 2  N  2 N  n=i MRexp i − M R exp n=i MRprei − MR pr e 2

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(2)

In the above expressions MRexp , i is the ith experimental moisture ratio, MRpre i is the ith predicted moisture ratio, N is the number of observations, and n is the number of constants [4]. Dependence of drying kinetics variable on constants was observed in this work. During the drying experiments, variation in the values of parameters was in the following range Ambient air temperature, °C Relative humidity of ambient air, % Average dryer temperature, °C Relative humidity of air in the dryer, % Global solar radiation intensity, W/m2

29–40 24–38.5 34–52 34 to 93 198–976

The initial moisture content of the grapes on dry basis was 3.34 kg while the same reduced to 0.1580 kg. Newton model resulted in a higher R2 and lower ψ 2 . So this model is used to represent the drying characteristics of grapes in this work and constants and coefficients of model used for regression analysis were as follows. MR = exp (−kt)

(3)

where k = 0.042704. At any point of point moisture content in grapes can be found out by the above model, Drying variables very much depend on various constants and coefficients in model, with R2 = 0.9869 and ψ 2 = 9.09 × 10−3 . The experimental work illustrates modeling and validation of drying process of grapes. Constants of the empirical models can be expressed in terms of air temperature, humidity, and velocity. The computed values of moisture content for particular drying conditions were validated by experimental moisture content values. The predicted values indicated the suitability of the Newton model in describing drying behavior of grapes. Drying time and moisture ratio are introduced in Newton model to obtain the best fit. Low values of standard error and high values of correlation coefficient demonstrate the suitability of exponential model in describing the drying characteristics of seedless grapes. Among various drying models studied, Newton model accurately predicted the drying characteristics of grapes. Newton model effectively described the thin-layer drying kinetics. Logarithmic scale on Y-axis is useful to understand and it is showing a straight line as shown in Fig. 7.

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Moisture Ratio

1 0.8 0.6 0.4 0.2 0 0

10

20

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40

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Drying Time (hrs) Fig. 6 Variation of moisture ratio with drying time for all models

In Lewis or Newton model, the difference in moisture content between the material and the equilibrium moisture content in the drying air condition is proportional to rate of drying. This rate of drying can be determined by drying conditions such as temperature, humidity of air, velocity of air, and the heat supply to which grapes are exposed. Drying of grapes involves evaporation and diffusion. Newton model is based on assumptions that there are constant drying conditions and given material does not shrink greatly during drying. The drying constant K varies with rate of diffusion and surface evaporation and with thickness. In this case, due to pretreatment surface is made porous and therefore initially surface evaporation is rapid as compared to diffusion, this can be observed in Fig. 6 where experimental results and Newton model is matching very much till 14 h then later diffusion is rapid as compared with surface evaporation, but rate of diffusion is slow and grapes take longer time to come to safe moisture limit that is from 14 h to 56 h, so after the second day there is some variation in experimental results and Newton model. This is due to variation in drying conditions by natural convection. Therefore, value of K affects very much to the drying process as per the external drying conditions. Thus in this experimental work, controlling factors are the drying conditions govern the drying phenomenon rather than the controlling factors of material itself, which are not considered in Newton model. Therefore, Newton model effectively describes drying kinetics in this study (Fig. 7).

4 Conclusion With dry run tests, a guideline could be obtained about maximum mean temperature inside the dyer.

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1 0

7

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Moisture Ratio

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56

Moisture Ratio (Exp) Newton Model Page Model Modified Page Model Handerson and Pabis Logarithmic Model

0.25

0.125

Two Term Model 0.0625

Two Term Exponential Model Wang & Singh Model

0.03125

Drying Time (hrs) Fig. 7 Variation of moisture ratio with drying time for all models (logarithmic scale)

Moisture content of grape varied with tray location. Drying rate was higher at two locations first in the top region near the glass cover and second at the front portion of lower and upper trays. Product comes directly in contact with high-temperature air at theses points. Relative humidity of air entering the dryer is low while that of leaving the dryer is high due to gain in moisture from grapes. Drying process occurs in falling rate period and thin-layer solar drying of grapes was studied.

References 1. Lewis, W.K.: The rate of drying of solid materials. J. Ind. Eng. Chem. 13(5), 427–432 (1921) 2. Page, G.E.: Factors influencing the maximum rates of air drying shelled corn in thin layers. MS Thesis. Purdue University, US. pp. 1–46 (1949) 3. Mujumdar, A.S.: Handbook of Industrial Drying, 3rd edn. Taylor and Francis Group, LLC. (2006) 4. Yaldiz, O., Ertekin, C., Uzun Ibrahim H.: Mathematical modelling of thin layer solar drying of sultana grapes. Energy 26(5), 457–465 (2001) 5. Sawhney, R. L., Pangavhane, D.R., Sarsavadia, P.N.: Drying Studies of Single Layer Thompson Seedless Grapes. In: International Solar Food Processing Conference Proceedings, pp. 1–20, Indore, India (2009) 6. El-Ghetany, H.: Experimental investigation and empirical correlations of thin layer drying characteristics of seedless grapes. Energy Convers. Manag. 47(11–12), 1610–1620 (2006) 7. Lyes, B., Azeddine, B.: Numerical simulation of drying under variable external conditions: application to solar drying seedless grapes. J. Food Eng. 76(2), 179–187 (2006)

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8. Krokida, M.K., Karathanos, V.T., Maroulis, V., Marinos-Kouris, V.: Drying kinetics of some vegetables. J. Food Eng. 59, 391–403 (2003) 9. Ibrahim, D., Mehmet, P.: The effects of dipping pretreatments on air-drying rates of the seedless grapes. J. Food Eng. 52(4), 413–417 (2002) 10. Fadhel, A., Kooli, S., Farhat, A., Bellghith, A.: Study of the solar drying of grapes by three different processes. Desalination 185(1–3), 535–541 (2005) 11. Singh, S.P., Jairaj, K.S., Srikant, K.: Universal drying rate constant of seedless grapes: a review. Renew. Sustain. Energy Rev. 16(8), 6295–6302 (2012) 12. Nascimento, P., Silva, C., Gomes, J., Hamawand, I.: Description of seedless grape drying and determination drying rate. J. Agric. Stud. 2(2), 1–10 (2014) 13. Doymaz, ˙I., Akgün, N.A.: Study of thin-layer drying of grape wastes. Chem. Eng. Commun. 196(7), 890–900 (2009) 14. Cakmak, C., Yıldız, C.: The drying kinetics of seeded grape in solar dryer with PCM-based solar integrated collector. Food Bioprod. Process. 89(2), 103–108 (2011) 15. Belessiotis, V., Delyannis, E.: Solar drying. Sol. Energy 85(8), 1665–1691 (2011) 16. Bennamoun, L.: An overview n application of exergy and energy for determination of solar drying Efficiency. Int. J. Energy Eng. 2(5), 184–194 (2012) 17. VijayaVenkataRaman, S., Iniyan, S., Goic, R.: A review of solar drying technologies. Renew. Sustain. Energy Rev. 16(5), 2652–2670 (2012) 18. Ekechukwu, O.V., Norton, B.: Review of solar-energy drying systems II: an overview of solar drying technology. Energy Convers. Manag. 40(6), 615–655 (1999) 19. Singh, P., Shrivastava, V., Kumar, A.: Recent development in greenhouse solar drying. Renew. Sustain. Energy Rev. 82(3), 3250–3262 (2018) 20. Gallali, Y.M., Abujnah, Y.S., Bannani, F.K.: Preservation of fruits and vegetables using solar drier: a comparative study of natural and solar drying, III; chemical analysis and sensory evaluation data of the dried samples. Renew. Energy 19(1–2), 203–212 (2000)

Feedback and Feedforward Control of Dual Active Bridge DC-DC Converter Using Generalized Average Modelling Shipra Tiwari and Saumendra Sarangi

1 Introduction Growing focus towards the renewable sources to produce power which is clean and non-polluting has increased over the decades to sustain the conventional sources from getting depleted. In order to harness these powers, the various interfaces are needed after which it is ultimately supplied to the load demands. The power electronic converters are also an interface between the renewable sources or any other source to the load. Among these converters, the dual active bridge is one such converter which is an isolated bidirectional DC-DC converter used for high- and medium-power applications. The dual active bridge converter was first proposed by De Doncker et al. [1, 2], which can be used for high-power applications including electric vehicles, interfacing devices for renewable sources, smart grids and solid-state transformers applications. The dominance of this isolated bidirectional DC-DC converter is providing buckboost property as well can be seen in terms of high- power density [1, 3], highly efficient, low passive components, inherent soft switching, circuit symmetry, etc. Earlier, due to lack of availability in the advanced power electronic switches, the power density achieved in a DAB converter was restricted. However, with the developments in the advanced IGBT modules, the power level was increased and hence the efficiency was enhanced. The modelling of the DAB converter was also done using small-signal analysis [4–8] and also the usage of DAB in a very high-frequency applications where the size of the transformer is very compact such as for aerospace applications [9]. The new modelling is also proposed and compared with the existing S. Tiwari (B) National Institute of Technology, Srinagar, Uttarakhand, India e-mail: [email protected] S. Sarangi Motilal Nehru National Institute of Technology, Prayagraj, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_51

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models [10]. The various drawbacks like reactive power and conduction losses have also been minimized so as to improve the efficiency of the converter [8, 11]. The basic circuit analysis can be carried out in continuous time domain and discrete domain [7] where the latter analysis becomes more complex to understand. The continuous time domain makes the analysis based on the state-space model of the system where the constraint applied is the assumption “small ripple”. This assumption is however, not true in the cases where high ripples or harmonic contents are present [12]. Therefore, the overall analysis of using only the DC component (approximated) might not be able to provide the actual dynamics of the system parameters. Therefore, the generalized average modelling technique [12–14] can be used to provide the alternating behaviour of the inductor current which contains the zeroth-order, firstorder and increased order harmonics. In [13], the author has provided the complete analysis of the DAB converter considering the forward power flow and developed the full-time continuous sixth-order model considering the zero-order and first-order harmonics of the inductor current and output capacitor voltage. The author also used the assumption of DC component of inductor current to be zero and developed the decoupled circuit equations which was the reduced third-order model. The full order model was more accurate than the reduced order model. However, as the converter is bidirectional, the input side parameter was not considered as when this converter is interfaced with another system, the input parameter will definitely affect the system performance and dynamics will vary. In order to control the input side, the feedforward control can be used whereas for the output side, the feedback controlling technique has been employed [15]. In this paper, the author has modelled the DAB converter taking into account the input capacitor voltage with detailed analysis. It is observed that, the input parameter when used in the dynamics, the equations will modify and depend on this parameter as well. The system performance is also analysed through the equations of generalized average modelling as well as extensive simulations. The simulation results are shown and it is observed that the output as well as the input voltage is controlled using the two PI controllers which are easily tuned. The only limitation is the usage of two controllers. However, the input controlled using output relation also makes the system slower as it involves the inverse trigonometric functions which are slow loops and will ultimately make the system slower. The overall paper is divided into six sections where Sect. 2 describes the DAB converter basics. Section 3 briefs about the generalized average modelling technique which is used here. In Sect. 4, the control aspects is discussed and simulation results are presented in Sect. 5. Finally, Sect. 6 concludes the paper.

2 The Dual Active Bridge Converter The DAB converter consists of two H bridges on either side of the high-frequency (HF) transformer with an external inductor connected as a power transfer link. There is a phase shift φ between the primary and secondary voltage (referred to the same

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Fig. 1 The dual active bridge converter

side) which decides the direction of power flow. Positive phase shift will make the power flow from high voltage to low voltage side (forward direction) and similarly negative phase shift will do the opposite. The waveforms associated with forward power flow are shown in Fig. 2. The power output of the converter is given as [1] Po =

nVs Vo d(1 − d) 2 fs L s

(1)

where d = πφ ; f s = switching frequency (Fig. 1). The circuit equations and modelling are described in the next section.

3 Generalized Average Modelling The fixed switching frequency of 0.5 duty ratio is taken and a fixed frequency of 10 kHz. This is the simple single phase shift control technique where the secondary side voltage lags the primary voltage by a phase shift φ = dπ . The waveform of the technique is shown in Fig. 2 for better understanding. With the switching of the corresponding switches for positive and negative voltages, the two voltage levels at the transformer sides are converted into a switching function given by [13] ⎤ ⎡ s1 (τ ) = 1; 0 ≤ t < T2 ⎥ ⎢ −1; T ≤ t < T 2 ⎥ ⎢ ⎣ s2 (τ ) = 1; dT ≤ t < T + dT ⎦ 2 2 2 −1; 0 ≤ t < dT 2 Therefore, primary and secondary voltages are

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Vpri = s1 (τ )Vc1 (τ )

(2)

Vsec = s2 (τ )Vc2 (τ )

(3)

Taking the state variables i L (τ ), Vc1 (τ ), Vc 2(τ ) as inductor current, input and output side capacitor voltages being time-dependent are related through equations given as following (4) Vs (τ ) − i s (τ )rs = Vc1 (τ ) VL (τ ) = L

using (4) i c1 (τ ) =

di L (τ ) = s1 (τ )Vc1 (τ ) − s2 (τ )Vc2 (τ ) dτ

(5)

Vc2 (τ ) = Vo (τ )

(6)

i c1 (τ ) = i s (τ ) − s1 (τ )i L (τ )

(7)

Vs (τ ) − Vc1 (τ ) C1 d Vc1 (τ ) = − s1 (τ )i L (τ ) dτ rs

(8)

C2 d Vc2 (τ ) Vc2 (τ ) = s2 (τ )i L (τ ) − dτ RC2

(9)

To make these time-varying nonlinear systems of equations into linear time-invariant system, state-space averaging is applied by using Fourier series to represent the system variable. Thus, the zero-order and first-order harmonics terms are considered and the equations involved is clearly mentioned in [13]. Using those equations, the zero-order and first-order harmonic content variable is given where the fundamental harmonic component is bifurcated into real and imaginary parts. The system state variables are < Vc1 >o , < Vc1 >1R , < Vc1 >1I , < I L >o , < I L >1R , < I L >1I , < Vc2 >o , where R and I are the real and imaginary parts and subscript “o” is the DC component or the mean value. The switching function is like a square wave and its Fourier coefficients are given as < s1 >o =< s2 >o = 0 < s1 >1R = 0; < s1 >1I = < s2 >1R =

(10) −2 π

−2 sin(dπ ) −2 cos(dπ ) ; < s2 >1I = π π

(11) (12)

The state-space equations by applying averaging and Fourier series property are given in the following matrix

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dX = AX + BU dt

(13)

where X = [Vc1o , Vc11R , Vc11I , I L o , I L 1R , I L 1I , Vc2o ]T and U = Vs . The matrix A comes out to be ⎤ ⎡ −1 4 0 0 0 0 0 rs C 1 πC1 ⎢ 0 −1 ωs 0 0 0 0 ⎥ ⎥ ⎢ rs C 1 ⎢ 0 −ω −1 2 0 0 0 ⎥ s rs C1 πC1 ⎥ ⎢ ⎥ ⎢ 0 0 0 ⎥ ⎢ 0 0 π−4L 0 ⎢ 2 sin(dπ) ⎥ ⎥ ⎢ 0 0 0 0 0 ωs πL ⎢ −2 2 cos(dπ) ⎥ ⎦ ⎣ πL 0 0 0 −ωs 0 L sin(dπ) −4 cos(dπ) −1 0 0 0 0 −4 πC πC2 RC2 2 and B = [ rs1C1 0 0 0 0 0 0]T .

3.1 Steady state and small-signal analysis At steady state, the derivative terms (d X/dt) = 0 and the matrix are reduced to 4thorder, and small-signal analysis is carried out by taking small perturbations in the parameters, d = D + d (vc1 )0 = Vc10 + Vc10 and similarly other large signals are represented in terms of steady state and smallsignal states, and the small-signal averaged model is derived using trigonometric approximations as sin(π d) = π d and cos(π d) = 1. The reduced 4th-order matrix is similar to 3rd-order matrix by removing the Vc10 row. In the 3rd-order case [13], the relation between input and output voltage came out to be Vo 8R sin(Dπ ) (14) = Vs π 2 ωs L However, Eq. (14) will be true for 4th-order as shown Vo 8R sin(Dπ ) = Vc1 π 2 ωs L

(15)

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Thus, the relation between input–output voltage is now changed to 8R sin(Dπ ) ∗ π 2 ωs L Vo = 2 Vs (π ωs L) + (8 sin(Dπ ))2 rs R

(16)

Therefore, designing the system equations based on input state variable also changes the system dynamics. The derived reduced order model after applying small-signal perturbations is shown as Aˆ ⎤ ⎡ −1 4 0 0 rs C 1 πC1 2 sin(Dπ) ⎥ ⎢ 0 0 ωs πL ⎥ ⎢ −2 2 cos(Dπ) ⎦ ⎣ −ωs 0 πL L −4 cos(Dπ) −1 0 −4 sin(Dπ) πC2 πC2 RC2 c20 −2 sin(Dπ)Vc20 4 sin(Dπ)I L I LR T Bˆ = [0 2 cos(Dπ)V − 4 cos(Dπ)I ] πL πL C2 C2 T ˆ X = [Vc10 I L R I L I Vc20 ] Uˆ = d. The characteristic equation |s I − A| is calculated and given as

|s I − A| = s 4 + (a1 )s 3 + (a2 )s 2 + (a3 )s + (a4 )

(17)

1 + rs1C1 + π 20.5 where a1 = RC LC1 2 8 a2 = π 2 LC2 + ωs2 + Rrs C1 1 C2

a3 =

ω2 ωs2 8 + π 2 rs LC + rs CS 1 RC2 1 C2 2 ωs2 (π D) − 4sin . rs RC1 C2 π 4 L 2 C1 C2

+

0.5 π 2 R LC1 C2

a4 = The obtained transfer functions of both the voltages to be controlled are given as (ao )s 3 + ( rsaCo 1 )s 2 + (b1 )s + (b2 ) Vo (s) VC2 (s) = = d(s) d(s) |s I − A| 4(sin(π D)I L I −cos(π D)I L R ) C2 8ωs2 cos2 (π D)Vo 8 sin2 (π D)ωs Vo + + ao ωs2 π LC2 π LC2 a ω2 D) sin(π D) 8ωs Vo − 4Vo cos(π + rsoC1s . πrs LC1 C2 π 3 L 2 C1 C2

(18)

where ao = b1 =

b2 = Similarly,

+

2ao 4π 2 LC1

(c1 )s 2 + (c2 )s + (bo ) VC1(s) = d(s) |s I − A|

D) cos (π D) s cos(π D) o sin (π D) where bo = 0.5VπoRωLC − 4Vo sin(π − 4V − π 3 L 2 C1 C2 π 3 R L 2 C1 C2 1 C2 D)Vo c1 = −0.5 πsin(π LC1 D) o cos(π D) o sin(π D) c2 = −0.5ωsπVLC − 0.5V + 0.5aπo 2cos(π . π R LC1 C2 LC1 1 2

3

(19) 0.5ao ωs sin(π D) π 2 LC1

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The control to output transfer functions in Eqs. (18) and (19) is tuned using PI controller using the feedforward and feedback control of the output and input variables discussed in the next section.

4 Feedforward and Feedback Control The output and input side capacitor voltage is to be controlled for which the simple PI controller is tuned, respectively. Usually the input side control is accompanied by inverter buffer at the comparator input, while the output tuning is done according to the transfer matrix obtained from the above derivation. The open-loop system itself comes out to be stable, and therefore, the simple tuning of PI controller makes the control easy. For the primary side switches, each switch is maintained the duty cycle of 0.5 by comparing a constant with a repeating signal of frequency 10 kHz. The output is the pulse of 0.5 duty cycle. This is fed to the primary side switches. However, the secondary side switches are provided the delay of dTs as modulated by the PI controller. The basic control diagram is shown in Fig. 3. The parameter specifications are shown in Table 1.

5 Simulation Results The following results are validated using MATLAB/Simulink for the two control desired. These are shown in the following results shown in Figs. 4, 5 and 6. The

Fig. 3 Closed-loop control Table 1 Parameter specifications Parameter Vs VO Value

150 V

150 V

C1

C2

L

fs

100 µF

540 µF

500 µH

10 kHz

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Fig. 4 Input controlled voltage

Fig. 5 Output controlled voltage

Fig. 6 Inductor current

increased ripple at the input side is mainly due to the capacitor value taken to be 100 µF. It can be seen that the input as well as the output voltages are as required along with the inductor current, which is also within the range.

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6 Conclusion The generalized average modelling technique is applied to the DAB converter including the input parameter so as to employ the feedforward as well as feedback controlling method. The bidirectional converter property makes it greatly used in the applications where the input as well as output side voltages need to be controlled, as incase of solar arrays. The small-signal approximation used here includes the DC component as well as fundamental harmonic component to capture the alternating behaviour of the inductor current. The modelling is done and it is found that the input side parameter will affect the overall system dynamics and the voltages are controlled. Two PI controllers are used here for controlling both side voltages, and the simulated results are presented.

References 1. De Doncker, R.W.A.A., Divan, D.M., Kheraluwala, M.H.: A three-phase soft-switched highpower-density DC/DC converter for high-power applications. IEEE Trans. Ind. Appl. 27(1), 63–73 (1991) 2. Kheraluwala, M.H., Gasgoigne, R.W., Divan, D.M., Bauman, E.: Performance characterization of a high power dual active bridge DC/DC converter. In: Industry Applications Society Annual Meeting, 1990, Conference Record of the 1990 IEEE, pp. 1267–1273. IEEE (1990) 3. Demetriades, G.D., Nee, H.-P.: Characterization of the dual-active bridge topology for highpower applications employing a duty-cycle modulation. In: Power Electronics Specialists Conference, 2008. PESC 2008. IEEE, pp. 2791–2798. IEEE (2008) 4. Mi, C., Bai, H., Wang, C., Gargies, S.: Operation, design and control of dual H-bridge-based isolated bidirectional DC-DC converter. IET Power Electron. 1(4), 507–517 (2008) 5. Bai, H., Mi, C., Wang, C., Gargies, S.: The dynamic model and hybrid phase-shift control of a dual-active-bridge converter. In: Industrial Electronics, 2008. IECON 2008. 34th Annual Conference of IEEE, pp. 2840–2845. IEEE (2008) 6. Demetriades, G.D., Nee, H.-P.: Dynamic modeling of the dual-active bridge topology for highpower applications. In: Power Electronics Specialists Conference, 2008. PESC 2008. IEEE, pp. 457–464. IEEE (2008) 7. Krismer, F., Kolar, J.W.: Accurate small-signal model for the digital control of an automotive bidirectional dual active bridge. IEEE Trans. Power Electron. 24(12), 2756–2768 (2009) 8. Krismer, F., Kolar, J.W.: Closed form solution for minimum conduction loss modulation of DAB converters. IEEE Trans. Power Electron. 27(1), 174–188 (2012) 9. Naayagi, R.T., Forsyth, A.J., Shuttleworth, R.: High-power bidirectional DC–DC converter for aerospace applications. IEEE Trans. Power Electron. 27(11), 4366–4379 (2012) 10. Bai, H., Nie, Z., Mi, C.C.: Experimental comparison of traditional phase-shift, dual-phaseshift, and model-based control of isolated bidirectional DC–DC converters. IEEE Trans. Power Electron. 25(6), 1444–1449 (2010) 11. Bai, H., Mi, C.: Eliminate reactive power and increase system efficiency of isolated bidirectional dual-active-bridge DC-DC converters using novel dual-phase-shift control. IEEE Trans. Power Electron. 23(6), 2905–2914 (2008) 12. Sanders, S.R., Mark Noworolski, J., Liu, X.Z., Verghese, G.C.: Generalized averaging method for power conversion circuits. IEEE Trans. Power Electron. 6(2), 251–259 (1991) 13. Qin, H., Kimball, J.W.: Generalized average modeling of dual active bridge DC-DC converter. IEEE Trans. Power Electron. 27(4), 2078–2084 (2012)

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14. Mueller, J.A., Kimball, J.: An improved generalized average model of DC-DC dual active bridge converters. IEEE Trans. Power Electron. (2018) 15. Kazimierczuk, M.K., Massarini, A.: Feedforward control of DC-DC PWM boost converter. IEEE Trans. Circuits Syst. I: Fundam. Theory Appl. 44(2), 143–148 (1997)

Performance Assessment and Parametric Study of Multiple Effect Evaporator Pranaynil Saikia, Soundaram Ramanathan, and Dibakar Rakshit

1 Introduction Reverse Osmosis (RO) rejects are treated in multiple effect evaporators (MEE) in zero liquid discharge (ZLD) wastewater treatment systems. MEE is used to evaporate water from RO rejects and concentrate the rejects before sending it to a centrifuge in ZLD wastewater treatment systems for salt separation. MEE utilizes steam of temperature of 120–200 °C to evaporate wastewater [1]. Thermal energy input for the device can be sourced from solar concentrators. In the present study, a simulation tool has been proposed using an analysis based design to estimate the gross area required for the solar concentrator to operate MEE and also to study the daily performance analysis using energy conservation laws. Based on the model parametric analysis was conducted following which optimization dealing with parameters for operating MEE was performed with the objective of maximizing exergy. Unlike traditional boilers where heat is supplied to vaporize products, in single-effect evaporator systems steam provides energy for vaporization and the vapor product is condensed and removed from the system, while in multiple effect evaporator, the vapor product of the previous effect is used to provide energy for a next vaporization unit and the process continues for subsequent effects. MEE has been traditionally used for desalination. However, influence of different wastewater parameters on steam requirements and its effect on the operation of the system is comprehensively unavailable. This study has attempted to summarize it, also it has optimized energy solutions for solar-assisted MEE. In the present study, the evaporator is connected to a solar thermal concentrator (Fig. 1). P. Saikia · D. Rakshit (B) Centre for Energy Studies, Indian Institute of Technology Delhi, Delhi, New Delhi 110016, India e-mail: [email protected] S. Ramanathan Centre for Science and Environment, Delhi, New Delhi, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_52

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Fig. 1 Solar assisted multiple effect evaporator

2 Simulation Tool and Algorithm A simulation tool was designed to excel visual basic application (VBA) which can estimate the area required for solar concentrator and the daily performance of the device with varying solar irradiance. The listed eleven parameters are taken as the model inputs—steam temperature at the inlet in the first effect (T s1 ), steam temperature at the outlet in the last effect (T sn ), number of effects (n), feed flow rate (mf ), concentration of the effluent (wastewater) at the inlet in the first effect (X 1 ) and at the outlet in the last effect (X 2 ), heat transfer coefficient of the first effect evaporator (U D ), optical efficiency of the solar concentrator, concentration ratio (CR), top loss coefficient of the solar concentrator’s receiver tube and the city (for solar irradiance). The basic principle behind the tool is energy balance. Multiple effect evaporators were designed first and corresponding to the useful heat requirement of the first effect of the MEE, area required for solar concentrator was determined. For the present mathematical modeling the flow is assumed to be in steady and uniform. The fluids are assumed to be isotropic and homogeneous. To design the interconnected series of evaporator, the heat transfer (Q) happening in each of the effects were assumed to be equal [2], Q1 = Q2 = Qn

(1)

Sub-indices 1, 2, n denote properties such as temperature, area, etc. of Effect (I), Effect (II), Effect (n) of MEE, s denotes steam, B denotes effluent to be concentrated.

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U D1 A1 (Ts1 − TB1 ) = U D2 A2 (Ts2 − TB2 ) = U Dn An (Tsn − TBn )

(2)

U D1 A1 T1 = U D2 A2 T2 = U Dn An Tn

(3)

Q of the n effects was determined following the below steps. Step 1: Determination of T n . Assuming area of the heat transfer in all of the evaporators to be identical the above equation gets reduced to Eq. (4). U D1 T1 = U D2 T2 = U Dn Tn

(4)

From Earle [3], in each effect the heat transfer coefficient degrades by approximately 5 percent consequently, i.e. U Dn = 0.95 × U Dn−1

(5)

Also by having a knowledge on the steam inlet temperature and its vapor temperature at the final effect/chamber/evaporator the T n can be estimated as T1 + T2 + · · · + Tn = Ts1 − Tsn

(6)

   Tn 1 + (Un /U1 ) + · · · + Un /U(n−1) = Ts1 − Tsn

(7)

Using Eqs. (5) and (7), T n of the ‘n’ effects were determined. Step 2: Steam temperature in each effect (Tsn ). Based on T 1 , T n , T s1 , T sn the temperature of steam inlet and outlet in the ‘n’ effects were determined. Step 3: Calculation of latent heat of vaporization of the effluent. Since the water has salt contaminants the latent heat of vaporization has been taken from the empirical relations given by Sharqawy et al. [4]. This equation considers other thermal properties of the salt contaminants which influence the latent heat rate. λs = a(1) + a(2) × Tsn + a(3) × Tsn2 + a(4) × Tsn3 + a(5) × Tsn4

(8)

h f = λs × (1 − 0.001 × S)

(9)

The equation is valid for the range 0 < T sn < 200 °C, and salt concentrations 0 < S < 240 g salt per kg water. T sn is brine temperature and S is salt concentration of effluent in g salt/kg, a(1) = 2.5008991412 × 106 ; a(2) = −2.3691806479 × 103 ; a(3) = 2.6776439436 × 10−1 ; a(4) = −8.1027544602 × 10−3 ; a(5) = −2.0799346624 × 10−5 are constants. Step 4: Calculation of steam evaporated and brine produced at each effect. If the evaporators are working in balance, then all the vapors from the first effect are condensing and in their turn evaporating vapors in the second effect. Also assuming that heat losses can be neglected, there is no appreciable boiling point elevation of

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the more concentrated solution, and the feed is supplied at its boiling point, as it is a steady system, the energy balance can be summarized as below, m s1 × h s1 = m s2 × h s2 = m sn × h sn

(10)

Neglecting the sensible heat changes, as it is very minimum in comparison to the latent heat exchange in the system. Similarly, the mass balance of the feed can be given as, m s1 + m s2 + · · · + m sn = m f × (S2 − S1 )/1000

(11)

Solving Eqs. (10) and (11), msn was determined. Subsequently, the brine produced at each effect was estimated as   B2 = m f × X 1 /1000 − m s1

(12)

Bn = Bn−1 − m sn

(13)

Step 5: Calculation of heat transfer rate and heat transfer area. The heat transfer rate and area were determined using the below equation Q n = m s λs = U Dn An (Tsn − TBn ) = U Dn An Tn

(14)

Step 6: Calculation of area required for solar concentrator. Solving Eq. (12), ms1 was determined. The heat requirement to evaporate this water was assumed to be the useful heat required to be collected by the solar collector. The basic energy balance equation of a solar concentrator [5] is given by, qu = η0 Ic Aa − Uc (Tr − Ta )Av

(15)

In which η0 is the optical efficiency, I c is the radiation incident on gross solar collector, Aa is the gross area required for solar concentrator, U c is the overall heat loss coefficient of the absorber tube, T r is the temperature of the absorber tube, T a is the ambient temperature and Av is the absorber tube area. The instantaneous efficiency is given by ηc = qu /(Ic Aa )

(16)

ηc = η0 − {Uc (Tr − Ta )/(Ic × CR)}

(17)

From which

Thus, gross area required for the solar concentrator system was estimated as below,

Performance Assessment and Parametric Study of Multiple Effect …

Aa =



      m f × (S2 − S1 )/1000 1 + h s1 / h s2 + · · · + h s1 / h sn      ×C p × (Ts − T )a Ic × η0 − (Uc (Tr − Ta )) Ic × CR

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(18)

Through iteration, the performance of the device on a daily basis was studied for the derived area required for solar concentrator and heat transfer in each effect and temperature of steam generated in each effect was estimated. Step 7: Entropy and exergy analysis. Specific entropy of steam was calculated using simplified equation given by Affandi et al. [6] based on steam temperature. ln Sg = a + b(0.35 × ln(1/Ts )) + c/Ts2 + d/Ts4 + e/Ts5

(19)

where a = 1.47735, b = 0.53242, c = −0.01923, d = 0.02974, e = −0.00802. Exergy (X) is calculated using the entropy calculated above [7], X = h − h 0 − T (S − S0 )

(20)

3 Performance Analysis The performance of the system was studied for four prominent tropical solar climatic zones—Delhi for composite climate, Jodhpur for hot and dry climate, Pune for moderate climate and Hyderabad for warm and humid climate. The number of effects and effluent feed flow rate (20 L/h) were taken as inputs. Three-effect multiple evaporator system of 20 L/h is the smallest evaporator capacity available in the market and their specifications were obtained from market survey. Heat transfer coefficient of the first effect evaporator (UD ) was taken as 2270 J/m2 C s (stainless steel material). Steam temperature at the inlet in the first effect (T s1 ) was assumed to be 150 °C and steam temperature at the outlet in the last effect (T sn ) was assumed to be 100 °C so that the system can operate at atmospheric pressure and energy will not be needed separately to generate low pressure in the rest of the effects. Textile effluent waste was passed in the MEE system, the concentration of the effluent at the inlet in the first effect (X 1 ) was assumed to be 20 g/kg and at the outlet in the last effect (X 2 ) was assumed to be 100 g/kg. Widely reported optical efficiency of solar concentrator systems [8] around 70% and overall top loss coefficient of 10 W/m2 [9] and concentration ratio of 2 [10] were used as observatory numbers to study the performance of the system using the simulation tool. The simulation tool was run to study the performance during the peak hours of solar irradiance between 10.00 a.m and 2.00 p.m. Also, parametric study was conducted with the aid of the simulation model. The model was run for over 200 different input constraints to obtain an output sample over the device performance. The ranges were identified based on a thorough literature survey and industry observations [11, 12] (Table 1).

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Table 1 Constraints range sampled

S. No. Constraint

Range

1

Optical efficiency of the solar collector 40–80%

2

Concentration ratio

3

Mass flow rate

20–500 kg/h

4

Salt concentration

1–55 g/kg

5

Temperature of the steam in 1st effect

150–300 °C

6

Number of effects

3–15

2–600

To arrive at a dimensionless correlation through regression analysis, the corresponding Re for variable mass flow rate, Prandtl number (Pr) for the variable salt concentration, and Nusselt number (Nu) for the variable temperature of the steam generated were estimated. Multiple regression analysis was carried out using IBM statistical software package to arrive at the correlation.

4 Results and Discussion The performance of the system in different climatic zones and need for auxiliary support systems were estimated. Overall, the system was found to generate the desired temperature of steam for most durations of the year except in winter months and monsoon days. Post which, the exergy performance of the system in different climatic zones were assessed. In addition the key parameters influencing the energy and exergy performance of the system were analyzed. Direct relationships are found between the solar radiation, area of the collector and desired temperature reached by the steam in different climatic zones. Though average solar irradiation was high in certain parts like Pune still due to variations in the frequency of occurrence of such high irradiation, the number of operational hours (i.e. the number of times the device could generate steam over 130 °C) the device required auxiliary support (Table 2). In Jodhpur, throughout the year except for monsoon the temperature of steam in the first effect remained an average 180–200 °C. Auxiliary heating is required for at least 16% of the operational hours of the system Table 2 Simulation output Area required for Average solar insolation, in solar concentrator, W/m2 (11.00 a.m–2.00 p.m) in m2

% hours the % hours the MEE operates MEE operates over 130 °C over 150 °C

Jodhpur

0.122

2500

80

Pune

0.127

2402

68

58

Hyderabad

0.130

2353

73

60

Delhi

0.132

2307

75

61

64

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Fig. 2 Temperature profile of different effects of the multiple effect evaporator at Delhi

mostly during the winter months. Auxiliary support system would be required to raise temperatures from 70 to 130–150 °C. In the northern tropical regions in Delhi at least for two months of the year, particularly during the winters—December and January it is difficult to attain the desired temperature at least for 15% of the operational hours in the year due to variations in solar radiations and intensity. In Pune also isolated days of monsoon affect the performance of the evaporator. However, only 17% of the operational hours in the year it will require auxiliary support. In Hyderabad, primarily during the monsoon days in June, July, August the concentrator may not produce the desired temperature of steam, hence fluctuations of temperatures would be a common phenomenon on isolated days of monsoon. Supplementary systems may be necessary to raise the temperature of steam from 130 to 150 °C at least 22% of the operational hours in the year. Due to space constraints, temperature-time profile of only Delhi is presented (Fig. 2). Exergy destruction was observed highest between the radiations falling on the solar collector and steam passed into the first effect of the evaporator. Subsequent exergy destruction between different stages of the evaporators decreased gradually to about 2% in the second stage and then increased to about 4% at the last stage. Exergy destruction of the overall system was largely dependent on the solar irradiation (Fig. 3). By plotting a histogram of the exergy at the solar collector during the considered 1460 h of the system operation, 60–70% of the times exergy was found to vary between 0.51 and 0.8 kJ/s leading to exergy destruction of over 60% between the solar collector and first effect of the MEE. Jodhpur had the highest exergy efficiency system, followed by Hyderabad, Delhi and Pune (Fig. 4). Parametric study using the regression analysis revealed Re as the prime parameter influencing the thermal performance of the device. The following correlation was obtained through multiple regression analysis, Nu = a Reb Pr c ηdQ CRe where r 2 = 0.97, a = 1.692, b = 0.183, c = 0.513, d = 0.283, e = 0.18.

(21)

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Fig. 3 Exergy due to solar irradiance at the solar collector versus percentage exergy destruction at the first stage of MEE

Fig. 4 Histogram of exergy incident on the solar collector

The correlation had a good parity with the model data with a standard deviation error of 0.1–8% and R-square of 0.97 (Fig. 5). Similarly, correlation was obtained by associating entropy of steam generated with Re, Pr, optical efficiency, and concentration ratio for evaluating the performance of the system (Fig. 6).   Sgen = 0.604 Re0.015 Pr 0.345 η00.015 CR0.1l7 r 2 = 0.98

(22)

To arrive at Eq. 21, Re was computed as Re = m˙ L/μA

(23)

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Fig. 5 Parity between the observed and correlated Nu

Here m˙ refers to Steam mass flow rate in kg/s. The heat transfer area was calculated as A = (m˙ × LH)/(T × U )

(24)

LH is the latent heat of vaporization and U is the overall heat transfer coefficient. Heat transfer was modeled to be happening along the curved surface area of the cylinder for unit length. The area thus obtained was used to compute the effective length of heat transfer, A = 2πr h

(25)

where h = 1 m and 2r = L and L = A/π. Generally, Re is computed as, Re = ρu L/μ

(26)

where ρ is the density, u is the velocity, L is the length, and μ is the dynamic viscosity. The Re was also computed using Eq. (27) to validate the findings of Eq. (24). The

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Fig. 6 Parity between the observed and correlated entropy (dimensionless)

diameter of the tubes (L) was assumed 2.4 mm [13]. It was found in literature that the diameter of evaporator tubes to vary in the range 2–5 mm. Velocity was estimated as,   u = (4m) ˙ π d 2ρ

(27)

It was observed that the deviations were minimum (Fig. 7). A t-score of 0.028 × 10−3 was obtained indicating very close similarity. Steam mass flow rate (m) ˙ is the prime parameter which affects the Re. Number of effects in an evaporator impacts the feed flow rate, in turn, the Re (Fig. 8). Re chiefly influences the heat exchange rate and entropy generation in the system. In the laminar flow region (0 < Re < 500) a unit increase in Re could engender up to 50% increase in the convective heat transfer rate or the Nu, in the transitional flow region (500 < Re < 2500) a unit increase in Re could mean only a 10% increase in the Nu and in turbulent region (Re > 2500) only 2% (Fig. 9). Similarly a unit increase in Re varied entropy up to 55%, in the transitional region up to 15% and laminar region up to 5% (Fig. 10). Differing salt concentration influences the viscosity, thermal conductivity, and specific heat of the fluid hence Pr (Fig. 11). Linear variation was

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Fig. 7 Validation of Re with water to be evaporated

Fig. 8 Variation in Re with mass flow rates and number of effects

observed between Pr and Nu (Fig. 12). The solar collector’s concentration ratio or the steam temperature has minimal effect on Nu. 0.4–7% change in Nu is anticipated when concentration ratio is stepped on a unit basis from 2 to 600 and even lesser percentage of change between 0.2 and 4.5% in the entropy. Optical efficiency is defined as the ratio of the energy absorbed by the absorber to that incident on the collector. It includes the effect of mirror surface shape and reflection, transmission

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Fig. 9 Re versus Nu plot

Fig. 10 Re versus entropy plot

losses, tracking accuracy, shading by the receiver, cover transmission, absorptance of the absorber and solar beam incident angle. η0 = S/Ib

(28)

Here I b is the incident solar energy, S is the rate of useful energy per unit area.

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Fig. 11 Salt concentration ratio versus Pr

Fig. 12 Pr versus Nu plot

Linear relationship was observed between optical efficiency of the solar collector and Nu (Fig. 13). Variation of optical efficiency between 40 and 95% resulted in an increase of 1.5–3.4% in Nu and 0.08–0.18% increase in entropy (Fig. 14).

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Fig. 13 η0 versus Nu

Fig. 14 η0 versus entropy

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Performance Assessment and Parametric Study of Multiple Effect … Table 3 Parameter range and values of  (a = 1, b = 1, α = 0.2)

Parameter

Range

573 

Re

3270–16370

0.03

Concentration ratio

2–10

0.5

Pr

6.8–7

0.9

Optical efficiency

60–65%

0.9

5 Optimization Analysis Maximizing heat transfer increases entropy generation and vice versa. Hence the need for holistic optimization was recognized. For the mentioned constrained range described in Table 1, the corresponding energy and entropy generation were computed. This was followed by obtaining the ratio of heat transfer to maximum heat transfer possible (q/qmax ) and ratio of the minimum possible entropy generation to the entropy generated (S min /S). The weightages considered for the ratios were varied as in Rakshit et al. [14],  a

 s b q min = α + (1 − α) wher e, 0 ≤ α ≤ 1 qmax s

(29)

To obtain a holistic optimum,  needs to be minimized. “a” and “b” are arbitrary constants. For the present case, a = b = 1 is considered which signifies equal weightage for heat transfer per unit mass maximization and entropy generation minimization. To determine the optimized parameters the value of α needs to be identified such that  is minimum and this determines the minimum total penalty. The minimum total penalty obtained in heat transfer (approx) was 20% and reduction in entropy generation (approx.) was 80%. Minimum  was obtained in the case of Re (Table 3). It signifies that the parameter holds in the case of arriving at the holistic optimization.

6 Conclusions Solar-assisted MEE can operate effectively over 70% of the time in a year except during monsoons and winter, where auxiliary support is required to raise the temperature of the steam to the desired value. Exergy efficiency of the device depends on the amount of solar irradiance. Also, the exergy efficiency and heat transfer of the system are chiefly related to Re and wastewater flow in the system. Exergy and Nu which represent the convective heat transfer in the fluid are competing parameters and an ideal optimization has to be arrived at for effective operation. Both depend on Re more than any other parameter involved in the study.

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References 1. Vishnu, G., Palanisamy, S., Joseph, K.: Assessment of fieldscale zero liquid discharge treatment systems for recovery of water and salt from textile effluents. J. Clean. Prod. 16, 1081–1089 (2008). https://doi.org/10.1016/J.JCLEPRO.2007.06.005 2. Sarma, G., Deb Barma, S.: Energy management in multiple-effect evaporator system: a heat balance analysis approach (2010). www.i-csrs.org. Available free online at http://www.geman. in. Accessed 15 Mar 2019 3. R.L. Earle, Unit operations in food processing, n.d 4. Sharqawy, M.H., Lienhard, J.H., Zubair, V.S.M.: Thermophysical properties of seawater: a review of existing correlations and data. Desalin. Water Treat. 16, 354–380 (2010). https://doi. org/10.5004/dwt.2010.1079 5. Lovegrove, K., Stein, W.: Concentrating Solar Power Technology: Principles, Developments and Applications, Woodhead Publishing (2012) 6. Affandi, M., Mamat, N., Kanafiah, S.N.A.M., Khalid, N.S.: Simplified equations for saturated steam properties for simulation purpose. Procedia Eng. 53, 722–726 (2013). https://doi.org/10. 1016/J.PROENG.2013.02.095 7. Çengel, Y.A., Boles, M.A.: Thermodynamics : An Engineering Approach. McGraw-Hill (2002) 8. Negi, B.S., Mathur, S.S., Kandpal, T.C.: Optical and thermal performance evaluation of a linear fresnel reflector solar concentrator. Sol. Wind Technol. 6, 589–593 (1989). https://doi.org/10. 1016/0741-983X(89)90095-7 9. Sampathkumar, K., Arjunan, T.V., Pitchandi, P., Senthilkumar, P.: Active solar distillation—a detailed review. Renew. Sustain. Energy Rev. 14, 1503–1526 (2010). https://doi.org/10.1016/ J.RSER.2010.01.023 10. Kalogirou, S.: Use of parabolic trough solar energy collectors for sea-water desalination. Appl. Energy 60, 65–88 (1998). https://doi.org/10.1016/S0306-2619(98)00018-X 11. Ranganathan, K., Karunagaran, K., Sharma, D.C.: Recycling of wastewaters of textile dyeing industries using advanced treatment technology and cost analysis—case studies. Resour. Conserv. Recycl. 50, 306–318 (2007). https://doi.org/10.1016/J.RESCONREC.2006.06.004 12. Correia, V.M., Stephenson, T., Judd, S.J.: Characterisation of textile wastewaters—a review. Environ. Technol. 15, 917–929 (1994). https://doi.org/10.1080/09593339409385500 13. Sen, P.K., Sen, P.V., Mudgal, A., Singh, S.N.: A small scale multi-effect distillation (MED) unit for rural micro enterprises: Part II—Parametric studies and performance analysis. Desalination 279, 27–37 (2011). https://doi.org/10.1016/J.DESAL.2010.11.005 14. Rakshit, D., Balaji, C.: Thermodynamic optimization of conjugate convection from a finned channel using genetic algorithms. Heat Mass Transf. 41, 535–544 (2005). https://doi.org/10. 1007/s00231-004-0569-6

An Approach Towards Sustainable Energy Education in India Pankaj Kalita, Rabindra Kangsha Banik, Samar Das, and Dudul Das

1 Introduction Energy is the key measure for the Human Development Index (HDI) in present industrial society. With the exponentially growing development, the demand for energy is increasing at a tremendous pace. Energy crisis in the upcoming future due to the fossil oil depletion is leading towards higher fuel prices [1, 2]. Also, global warming due to the higher pollution index of the conventional systems has put researchers to rethink about the new sources of harvesting energy efficiently without affecting ecology and environment [3, 4]. Renewable energy sources are found to be playing a vital role in modern energy scenario as it supplies energy without depleting the environment [5]. As the international society has taken steps towards curtailing the greenhouse gas (GHG) emissions and the United Nations Commission on Environment and Development (UNCED 1992) has instructed to implement processes of the sustainable environment; problem arouses for the energy industries [6]. It is due to which some of the RE sectors have seen a tremendous boom in demand and the government is also making supportive policies (e.g., Germany, India, Japan, Netherlands, Denmark, Spain) [6]. Large scale generation of renewable power results in a significant drop in price. Figure 1 provides the electricity generation capacity in gigawatt (GW) across the globe from the renewable sources in the last 10 years while Fig. 2 gives the capacity of electricity generation based on major geographical locations in GW. Increase in demand for renewable energy has resulted in shortage of trained and skilled professionals to launch the industry into this new phase [8]. Most of the industries and firms are therefore not able to find people suitable to design and implement the new systems. Like the conventional energy systems, implementation of renewable energy systems also needs experts [9, 10]. It is a challenge for the energy P. Kalita (B) · R. K. Banik · S. Das · D. Das Centre for Energy, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_54

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Fig. 1 Renewable energy electricity capacity aggregation across the world [7]

Fig. 2 The capacity of electricity generation based on major geographical locations in GW [7]

sector not only to bring new technologies but also to train the new professionals in a systematic way [11]. Engineers and technical experts are therefore requiring periodic training regarding design, installation and maintenance of the RE systems. Young people who can adapt faster with the trending technologies need to be given more emphasis than the people working in conventional systems. The training and education need to be provided not only related to energy education and conservation, but it also should cover issues related to social, economic and environmental associated with the system [12, 13]. Therefore, energy education is very important to

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locate new energy resources, design and develop new technologies, implementation and policymaking to meet the demand [14]. There are several courses offered to enhance the quality of energy education across the globe. Education in RE sector, in essence, is the understanding of various resources, issues related to energy generation, conservation, management and technological challenges [15]. But, it has been observed that most of the courses offered are at undergraduate (UG) and postgraduate (PG) level (about one third) followed by short-term professional courses which contribute about 30%. There are a few courses which offer hands-on training and vocational training and associate level programmes [16]. Significant effort is necessary at the academic level (school, college and university) to create awareness about energy conservation and management. It is therefore of enormous significance to educate the common public on energy and sustainability associated problems and to expose them towards RE technologies. The detailed and specified curriculum for energy education is very limited for different grades of the academic programme in a single project. The objective of the present work is to provide detailed modalities for energy curriculum at different levels viz. school, ITI, Diploma, Under Graduate (UG) and Post Graduate (PG) in engineering discipline for sustainable energy education. In this present work, a generic, systematic and very effective curriculum for energy education at different levels have been incorporated.

2 Modes and Levels of Energy Education Based on the importance and requirement of the energy industry, a model for the renewable energy education programme is suggested as shown in Fig. 3. In the

Fig. 3 Layout of the energy education curriculum at different levels of programme

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model, the education programme is segregated into two broad categories for both school level students as well as for students acquiring professional degrees.

2.1 Energy Education at the School Level Programme Introducing the relevance of renewable energy at the school level is a major challenge [17]. But it is essential to incorporate the idea of RE and its awareness at the school level to have an impact on the students about its importance [18]. Habits of energy conservation can be introduced from the pre-school level. Some attempts towards implementing the RE curriculum at school level has already been made in different countries [18]. But it is important to have appropriate teaching-learning resource materials for successful implementation of RE courses at the school curriculum [19]. Therefore, the curriculum for school level programmes should also be prepared by experts in the field of energy education. It is suggested to incorporate the RE education within the existing subjects at the school level such as science and environmental etc. It is important to provide the basic knowledge of what is energy and awareness of energy conservation at the primary school level [17]. It is suggested to teach the students about energy and environment and its importance in the form of stories and games. At the elementary level, the students will have the idea about various energy sources and their applications like fuels used for transportation. They will learn about different types of energy that is utilized across the world and also about energy conservation and the impact of energy in society. It is suggested to arrange energy expo or fair, model demonstration in lab, which will give the students a brief idea of the various energy generating systems. In the intermediate level, the students will acquire detailed knowledge of the energy sources and about its extraction. The extraction of fossil fuels, its conversion and utilization should be taught at this level. The current trend of energy scenario should also be incorporated in the curriculum and small mathematical problems can also be provided in the course. The curriculum should also provide some small scale projects at that level with hands-on energy systems. Quiz and workshops specific to energy may be organized for effective understanding and development. Some of the small experiments can also be provided in the laboratory classes of the course. The students at the secondary level will gain basic preparatory knowledge and skills in the RE sector for understanding the progress taking place in this field. This will motivate the students to choose RE in their higher studies. They will learn some of the basic principles of operation of simple energy systems in renewable energy. The energy flow pattern and the analysis can also be incorporated at the secondary level. Figure 4 is the suggested course structure that could be introduced in school level curriculum. It is suggested to organize energy and sustainability education-related camps and awareness programs for the illiterates and senior citizens. Tips on energy conservation

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Fig. 4 The layout of energy education at the school level

and efficient utilization of energy appliances would be helpful for creating concern among senior citizens.

2.2 Energy Education at the Professional Level Programme The professional-level programme in energy and sustainable education can be further divided into undergraduate (UG) and postgraduate (PG) programmes and skilled programmes for technicians and mechanics [20]. The skilled programme for the technicians and mechanics offer one-year Industrial Training Institute (ITI) programme and three years Diploma programme as per literature and is suggested for the Indian context. In the professional programmes, in-depth theoretical knowledge of various energy systems should be provided along with practical knowledge in the form of hands-on skill in laboratory courses, design, fabrication and installation in the form of small projects depending upon the structure of the course. The curriculum should be formulated based on the perspective of the energy industries and the structure of the programme. In the UG and PG programme, the student should able to get in-depth theoretical knowledge along with laboratory knowledge for design, implement, investigate and policy-making [15]. However, in doctorate level, emphasis is provided in investigation, design and implementation of new systems and to increase the efficiency of the existing systems. The technicians and mechanics need more hands-on training and skill development along with basic formal education in the energy sector. Energy Education for Degree and Higher Level Programmes. A large number of institutes offer courses on renewable and sustainable energy on undergraduate and postgraduate level [21]. But, it has been observed that adequate attention needed in formulating a course curriculum as the programme itself is multi-disciplinary in nature. To understand the subjects properly, it is important to have pre-requisite knowledge in the respective area in RE systems [22]. For example, a student should have adequate knowledge of thermodynamics, heat transfer, optics and calculus before taking a course on solar energy. It is also important for a student to have

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completed basic courses on conventional and renewable energy systems before study an advanced RE course at PG level. The curriculum preparation for the four years UG programme needs proper attention to cover all the important aspects of an energy engineer. Therefore, the curriculum should cover the fundamental engineering courses (like engineering mechanics, engineering graphics, etc.), basic courses along with the special courses of energy engineering. Apart from the courses on renewable energy sources, their design, implementation and monitoring; the students should also have courses on environment and ecology, energy management and conservation along with transmission and distribution of the generated power. However, they should have studied basic courses like thermodynamics, heat transfer, fluid mechanics, IC engines, basic electrical engineering & electronics, instrumentation and control, power plants etc. to take the advanced courses like energy modelling and simulation, computational heat transfer and fluid flow, integrated energy systems, etc. Apart from the theory courses, there should be adequate lab facility for the students to have practical knowledge and skills in the respective fields. Figure 5 gives the energy engineering-related courses for the undergraduate and postgraduate programme. In the two-year postgraduate programme, a brief knowledge of the RE systems should be provided. In the PG programme, more emphasis should be given to understand the problems and challenges associated with the energy systems and thereby try to give solutions in an innovative way. This will help in designing and planning any energy systems. There should be adequate elective courses (such as hydrogen energy and fuel cell, computational heat transfer and fluid flow) for students to select

Fig. 5 The layout of the energy education curriculum at the graduate and postgraduate level

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courses as per their desire. There should be an appropriate lab facility for the students to have practical knowledge and skills on the respective RE fields. Proper lab facility would also encourage the students in choosing challenging and innovative projects in their course work. Energy Education for ITI and Diploma Level Programme. One of the critical issue encountered by the majority of developing nations is shortage of technically sound workforce. Installations, maintenance and implementation of RE technologies are difficult without trained personals [20]. Presently, education and training for technicians, mechanics and supervisors, etc. are relatively general in nature and concerned with conventional energy generation technologies. It is therefore important to impart a variety of skills in the professional courses to meet the demand of the RE industries [15]. In the ITI and diploma level programmes, focus should be given on competency-based curricula. The curriculum should be structured with the end goal that a worker improves the capacity to transfer and apply the knowledge to new situations. Figure 6 provides the basic energy technology-related course structure that could be offered in ITI and diploma programme. Laboratory Component of Energy Curriculum. The laboratory component of RE curriculum is a key parameter to bridge the understanding between the theoretical and the hands-on skill. The experiments should include fundamentals of science and engineering that are directly relevant to the multiple techniques and processes for the use of renewable energy. This would be quite useful for students from diverse scholarly and professional disciplines to learn about the various energy conversion systems [18]. The laboratory experiments help the students to develop cognitive skills, experimental and investigational skills. It will also help in skill development in the field of data recording, interpretation and analysis. In order to derive helpful

Fig. 6 The layout of the energy education curriculum at ITI & Diploma level

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inferences for assessing the technological, financial, ecological and environmental consequences of renewable power systems, the student should be able to interpret the outcomes of the experimental studies carried out in the laboratory element. Given both energy producers and consumers’ growing awareness of energy, it is anticipated that the laboratory element of a RE learning program will also include studies addressing the recent technologies. It may be quite rewarding for learners to include experiments on prospective RE technologies for the future [18]. The laboratory courses should also be categorized based on the curriculum of different programmes. In the case of ITI and Diploma level, more emphasis should be given in demonstrating the essential instruments/equipment and its operation and maintenance. However, in the case of the undergraduate and postgraduate programme, students should have a broad idea of the various renewable energyrelated instruments. The suggested laboratory components of the renewable energy curriculum are as shown in Fig. 7. Employment Aspects. A fair amount of educational programmes are available in the developed nations to prepare the workforce to undertake studies, research, design and optimize and also offer solutions to the difficulties encountered by the RE industry. There are also institutions in these nations that train workforce for operation, installation and maintenance of the RE equipment. However, the scenario in developing nations is not satisfactory [18]. Although university-level programs have been launched, there are almost no organized methods to prepare and certify technicians/mechanics. Furthermore, in comparison with other standard conventional engineering fields, recruitment of graduates from current degree programs in RE is not very promising. It has been observed that especially in the case of postgraduate programs on renewable energy education, it is not equipped to attract the best-skilled learners reflecting their employment potential [18]. A potential cause for such a

Fig. 7 The layout of the energy engineering laboratories at different levels

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scenario is that the expertise obtained by the learners and especially the abilities do not satisfy the industry’s demands. It is a challenge for developing countries with large-scale unemployment to appropriately frame the RE programmes to provide sufficient employment possibilities, in addition to enabling self-employment. Government Schemes and Programmes. Special government and industrial assistance will lead towards enhancing infrastructure and equipment for imparting renewable power education. Several global and regional organizations have supported education on renewable energy [18]. As part of its Engineering Education and Training Program, ‘United Nations Educational, Scientific and Cultural Organization’ (UNESCO) has highlighted postgraduate training of energy engineering learners. To attain energy independence, Taiwan proposed a policy for energy education (Taiwan Energy Bureau, 2009) to enhance energy literacy as like ‘Nurturing Talent for Energy Technology’ (NTET) programme [5]. Wisconsin ‘K-12 Energy Education Program’ (KEEP) published energy curriculum focused on the knowledge and skills necessary to help future consumers for energy savings and also leverages teacher education to improve and increase energy literacy [5]. The ‘National Energy Education Development’ (NEED) project has created effective network of experts to design and deliver objective for promoting energy conscious and educated society [5]. The ‘International Renewable Energy Agency’ (IRENA) has launched a ‘Renewable Energy Learning Partnerships’ (IRELP) program to increase awareness and expand accessibility of modern energy equipment, as well as to support fresh projects in developing nations [16]. Government of India has launched ‘National Renewable Energy Fellowships’ to encourage learners to undertake postgraduate research in this field [18]. Department of Science and Technology and Indo-US Science and Technology Forum is offering internship opportunities to doctoral students from India in the best universities and laboratories of the United States. Some educational institutions have taken the lead in setting up educational programs in the field of renewable energy at the post-graduate level and are persistent in offering the same for long periods [18].

3 Issues and Challenges The initiative taken for RE education is moderately new. This leads to inconsistency of the structure of the course from institute to institute [18]. Therefore, the course structure and curriculum offered in energy education should be discussed by the experts in the field globally and bring consistency. Moreover, the curriculum and the courses should be focused on and end user-orientated. The experts should ensure synergy between energy, ecology and environment while preparing the courses. There should be an adequate share of laboratory components, industrial or field visits along with short term courses or workshops amongst the academic programmes. Apart from this, teachers and professors should be competent and committed to their goal. As the RE is interdisciplinary in nature and end-user driven field, proper concern must be taken to make the efforts resourceful and effective. Furthermore, there should be

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the availability of books and other resources materials related to the courses. Besides acquirement of knowledge and skills, a RE professional should be resourceful and innovative to develop suitable solutions for explicit conditions [23].

4 Conclusions and Recommendations A number of initiatives have been taken in the past decade for widespread renewable energy education at different levels, however, systematic curriculum based education is scarce. In the present investigation, an emphasis has been given to develop a concrete curriculum at different levels starting from school to post-graduate programme. Based on the review and observations, course curriculum at different levels are also proposed. E-resources (massive open online course (MOOC), distance mode, etc.) as well as curriculum-based learning in the school, college and university level might play a vital role in RE education. Furthermore, these initiatives will lead to conservation of energy and smooth transition from the conventional energy systems to renewable energy for power generation. A course for senior citizen may also be designed and introduced which incorporates the energy savings tips and efficient utilization of energy appliances. Job scenario should also look into consideration as renewable energy as a whole is multidisciplinary. RE education will provide the opportunity towards mitigation of the energy demand and increase the per capita energy consumption for enhancement of the quality of living standards in addition to the growth of the economy. Based on the analysis the following recommendations are proposed • Formulation and refinement of policies for implementation of effective renewable energy education at all levels through the adoption of theory courses and laboratory in the curriculum. • Encouragement for development of more e-learning resources at all levels for higher accessibility. • Mandatory energy education through training, campaign in residential campuses, industries, etc. • Involvement of industrial professional in PG degree in energy and allied subjects for more awareness about energy conservation and management. • Involvement of school students in internship on basic energy systems, energy conservation and the environment in premier institutions/universities.

References 1. Felgueiras, M.C., Rocha, J.S., Caetano, N.: Engineering education towards sustainability. Energy Procedia 136, 414–417 (2017)

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2. Karabulut, A., Gedik, E., Kecebas, A., Alkan, M.A.: An investigation on renewable energy education at the university level in Turkey. Renew. Energy 36(4), 1293–1297 (2011) 3. Kandpal, T.C., Garg, H.P.: Energy education. Appl. Energy 64(1–4), 71–78 (1999) 4. Jaber, J.O., Awad, W., Rahmeh, T.A., Alawin, A.A., Al-Lubani, S., Dalu, S.A., Dalabih, A., Al-Bashir, A.: Renewable energy education in faculties of engineering in Jordan: relationship between demographics and level of knowledge of senior students’. Renew. Sustain. Energy Rev. 73, 452–459 (2017) 5. Lee, L.S., Lee, Y.F., Altschuld, J.W., Pan, Y.J.: Energy literacy: evaluating knowledge, affect, and behavior of students in Taiwan. Energy Policy 76, 98–106 (2015) 6. Jennings, P.: New directions in renewable energy education. Renew. Energy 34(2), 435–439 (2009) 7. International Renewable Energy Agency (IRENA): Renewable Energy Statistics 2019 (2019) 8. Acikgoz, C.: Renewable energy education in Turkey. Renew. Energy 36(2), 608–611 (2011) 9. Benchikh, O.: Global renewable energy education and training programme (GREET programme). Desalination 141, 209–221 (2001) 10. Dias, R.A., Mattos, C.R., Balestieri, J.A.P.: Energy education: Breaking up the rational energy use barriers. Energy Policy 32(11), 1339–1347 (2004) 11. Malkki, H., Paatero, J.V.: Curriculum planning in energy engineering education. J. Cleaner Prod. 106, 292–299 (2015) 12. Stroth, C., Knecht, R., Gunther, A., Behrendt, T., Golba, M.: From experiential to researchbased learning: the renewable energy online (REO) master’s program. Sol. Energy 173, 425–428 (2018) 13. Newborough, M., Probert, S.D., Page, P.A.: Energy education in the UK Problems and perspectives. Energy Policy 19(7), 659–665 (1991) 14. Middleton, P.: Sustainable living education: techniques to help advance the renewable energy transformation. Sol. Energy 174, 1016–1018 (2018) 15. Kandpal, T.C., Garg, H.P.: Renewable energy education for technicians/mechanics. Renew. Energy 14(1–4), 393–400 (1998) 16. Lucas, H., Pinnington, S., Cabeza, L.F.: Education and training gaps in the renewable energy sector. Sol. Energy 173, 449–455 (2018) 17. Newborough, M., Getvoldsen, P., Probert, D., Page, P.: Primary- and secondary-level energy education in the UK. Appl. Energy 40(2), 119–156 (1991) 18. Kandpal, T.C., Broman, L.: Renewable energy education: a global status review. Renew. Sustain. Energy Rev. 34, 300–324 (2014) 19. Lane, J.F., Floress, K., Rickert, M.: Development of school energy policy and energy education plans: a comparative case study in three Wisconsin school communities. Energy Policy 65, 323–331 (2014) 20. Kandpal, T.C., Garg, H.P.: Renewable energy education for technicians/mechanics. Renew. Energy 16(1–4), 1220–1224 (1999) 21. Bhattacharya, S.C.: Renewable energy education at the university level. Renew. Energy 22(1– 3), 91–97 (2001) 22. Gelegenis, J.J., Harris, D.J.: Undergraduate studies in energy education—a comparative study of Greek and British courses. Renew. Energy 62, 349–352 (2014) 23. Ott, A., Broman, L., Blum, K.: A pedagogical approach to solar energy education. Sol. Energy 173, 740–743 (2018)

Simulation-Based Economic Optimization of Nuclear Renewable Hybrid Energy Systems with Reliability Constraints Saikrishna Nadella , Anil Antony, and N. K. Maheshwari

1 Introduction With ever increasing global environment concerns, pollution-free energy generation is the most sought after option for future energy demands of developing countries. Energy sources like nuclear, solar, wind, tidal, etc., are suitable options to cater this situation. Except nuclear, remaining sources of energy are intermittent and statistical in their availability and magnitude of power generation, whereas nuclear is a concentrated energy form having capability for continuous operation at proven capacity factor of more than 90%. A choice of diverse mix of these sources helps in increasing the reliability of the electricity generation with minimum to no pollution. Not only electricity but also other products like hydrogen, process heating, desalinated water, etc., can be generated from this system. This hybrid mix of energy sources, including nuclear, and products is called nuclear renewable hybrid energy system (NRHES). As the renewable energy sources as well as the demands and market prices of the products are variable in nature, a typical installation of NRHES should consider the most effective combination of sources and products to (a) earn maximum profit for given market scenario and/or (b) incur minimum cost in achieving maximum reliability. This turns out to be a complicated optimization problem. In the current study, we aimed at developing a detailed simulation-based techno-economic model for NRHES. This model helps the utility owners to determine the optimum mix of nuclear, solar, and wind sources to achieve a target reliability value at minimum cost. This model can also be used to perform parametric studies for policy decision makers to assess and realize the capabilities of various energy sources for reliable electricity generation.

S. Nadella (B) · A. Antony · N. K. Maheshwari Bhabha Atomic Research Centre, Mumbai 400085, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_55

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2 Techno-Economic Model The NRHES under consideration consists of an AC bus and a DC bus. Various energy sources and loads are connected to these buses as shown in Fig. 1. There is a twoway power conversion (P.C.) system between AC and DC buses. The formulation of techno-economic model includes energy generation models, costing models, and energy management models. These models are described below.

2.1 Energy Generation Models Nuclear reactor. The power generated by nuclear power plant (time averaged) is given by PN (t) = PNr × L F × 106

(1)

where PN (t) is power generated by nuclear reactor (W ), PNr is rated power (MWe), LF is load factor which considers refueling outages and other anticipated/unanticipated shut downs of the plant. Solar PV. Power generated from a solar panel (W ) is calculated by [1] PSPV (t) = Voc (t).Isc (t) · FF

(2)

where V oc (t) is open-circuit voltage, I sc (t) is short-circuit current, and FF is fill factor. They are given by   Kv Voc (t) = Voc,STC 1 + (Tc (t) − 25) 100   G(t) KI Isc (t) = Isc,STC 1 + (Tc (t) − 25) 100 1000

Fig. 1 Schematic of nuclear renewable hybrid energy system under study

(3) (4)

Simulation-Based Economic Optimization of Nuclear Renewable …

FF =

PMPP Voc,STC · Isc,STC

589

(5)

where STC stands for standard test conditions, V oc,STC and I sc,STC are open-circuit voltage (V) and short-circuit current (A) of panel at STC, respectively, K v and K I are voltage and current, temperature coefficients (%/°C), respectively, G(t) is solar radiation flux (W/m2 ), and T c (t) is the PV cell temperature (°C) [1]. Wind Generator. Power generated by wind generator (WG) is estimated as [2]

PWG (t) =

⎧ ⎪ ⎨0 ⎪ ⎩

v(t)3 −vci3 vr3 −vci3

vci > v(t) > vco · PWGr vci ≤ v(t) ≤ vr

(6)

vr < v(t) ≤ vco

PWGr

where vci , vr , and vco are cut-in, rated, and cut-off wind speeds (m/s), v(t) is local wind speed (m/s) at hub height, and PWGr is rated power (W ) of wind turbine. Battery. The maximum charging/discharging current of a battery is determined from its Crating as [2] Imax =

E Ah C

(7)

where E Ah is capacity of battery (Ah). Energy losses during charging and discharging are accounted by efficiency as ηch and ηdis , respectively. Batteries cannot be discharged below certain fraction of its capacity SOCmin and cannot be charged beyond its full capacity. Here, SOC stands for state of charge given as ratio of instantaneous charge and its total capacity. Power conversion system. A two-way power converter is used for converting the DC power to AC power and vice versa across AC and DC buses. Energy losses that occur during conversion are lumped into conversion efficiencies, ηi,AD (AC to DC) and ηi,DA (DC to AC).

2.2 Assessment of NRHES In current study, reliability parameters like loss of load probability (LoLP), annual unmet load (Pul,total ), and annual dumped energy (Pde,total ) are considered. These parameters are defined as follows Loss of load probability (LoLP): Loss of load probability is defined [2] as the ratio of number of hours in which there is a shortfall of supply and total number of hours

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in a year (8760), mathematically 8760 LoLP =

n=1

hours (Pav (t) < PL (t)) 8760

(8)

where PL (t) is load demand and Pav (t) is available power (defined later in this paper) at time t. Total unmet load (Pul,total ): It is the total load demand (Wh) that could not be supplied in a year. Total dumped energy (Pde,total ): It is the amount of energy (Wh) that is dumped (wasted) in a year due to excess generation. Strictly speaking, this is not a reliability parameter. But, it represents the effective utilization of energy.

2.3 Energy Management Philosophy of efficient energy utilization is taken as basis for energy management in current study. The total available instantaneous power, Pav (t), is given as Pav (t) = n N PN (t) + n WG PWG (t) + (n B Pbatt (t)ηdis + n SPV PSPV (t))ηi,DA

(9)

where nj is number of units of jth energy source. The maximum power that can be supplied by a single battery at any point of time ‘t’ is given by 

E Ah (SOC(t)(1 − σ ) − SOCmin ) Pbatt (t) = Vbatt × min Imax , t

 (10)

where σ is coefficient of self-discharge for battery (hr−1 ). The rules/procedures adopted for energy management are 1. As the load is connected to AC bus, it is supplied preferentially from AC power sources like nuclear and wind. 2. In case of excess generation, (a) If battery is not fully charged, battery charging is done from solar PV preferentially. The power for charging is the maximum of available charging power and allowable charging rate. The SOC of battery is updated at every time step. Self-discharge of the battery is also considered. (b) If battery is fully charged or available current is in excess of that required for battery charging, excess energy is dumped. The energy that is about to be dumped will not be passed through power conversion unit. 3. If the generation is insufficient for serving the load and if battery SOC is greater than SOCmin , the batteries are discharged at a power which is maximum of

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required power and possible discharge rate. The SOC of battery is updated at every time step. 4. If load cannot be supplied completely at any time step t, then unmet load (Pul,total ) and loss of load probability (LoLP) are updated 5. The rating of power conversion unit is decided based on operation of NRHES as

Pinv,r = max Pinv, j ∀ j ∈ [1, 8760]

(11)

2.4 Cost Estimation Model Levelized energy cost (LEC) is used to assess the economics of NRHES. It is defined as a constant price at which the utility owner should sell the electricity, to gain all the investment incurred in the project by end of its design life without profit/loss. Inflation rate, i, and discount rate, r, are also considered in total cost estimation. With this consideration, LEC is given as [3] LEC = TPV ×

CRF Served Load

(12)

where CRF is capital recovery factor, for given life time T (years) of the project, given by CRF =

r (1 + r )T (1 + r )T − 1

(13)

Total present value consists of capital costs (CC), operation and maintenance costs (OMC), refurbishment costs (RfC), fuel costs (FC), and decommissioning cost (DC). Salvage value of the discarded components is included in TPV. Mathematically, TPV = CC + OMC + RfC + FC + DC − PSV

(14)

Capital cost. Capital cost is incurred in purchasing all the equipment and systems that are needed to set up an energy generation system. For nuclear reactor, the cost of first core and moderator/coolant inventory is also considered as capital cost. CC = n N SCC N PNr + n WG SCCWG PWGr + n SPV SCCSPV PSPVr + Pinv,r SCCinv + n B SCC B E Ah

(15)

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where Pj,r denotes rated power per unit of jth component, SCCj denotes specific capital cost, i.e., capital cost per W or per Ah of jth component. Operation and Maintenance cost. OMC is generally given as % of capital cost and calculated as OMC0 = 0.01 ×



(OMCk (%)CCk )

(16)

k

Total present value of OMC is

OMC = OMC0 ×

1+i . r −i

1−

1+i T  1+r

T

for r = i for r = i

.

(17)

For nuclear reactor, heavy water make-up cost can also be included in OMC if the plant uses heavy water as moderator and/or coolant. Refurbishment cost. Cost incurred in replacement of some faulty/performance-degraded components before end of life of NRHES is called refurbishment cost (RfC). As an example, in pressure tube-type nuclear reactors, a large portion of the pressure tubes needs to be replaced once in a given interval of time. Unlike O&M cost, RfC is incurred once in a given period of time.     Nref  1 + i j.TR f,k RfC = RfCk (%) · CCk 1+r k j=1

(18)

where N ref,k is number of refurbishments of components of the generating system k in given life of project and T Rf is refurbishment interval. Fuel cost. Solar and wind resources are freely available. For nuclear reactor, fuel cost includes the cost for fresh fuel loaded during refueling. This is estimated based on average discharge burn-up of fuel, BUavg (MWd/Te HM). Mass of heavy metal (like Uranium) required for given annual power generation is given by m HM =

t=8760  t=0

1 PN (t) · t 6 24 × 10 η N · BUavg

(19)

where η N is overall efficiency of plant, t is time period of analysis (=1 h). The mass of fabricated fuel containing mHM amount of heavy metal is determined as m N f = m HM · x · y

(20)

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where x is ratio of molecular weight of fuel material (e.g., UO2 in PHWRs) to molecular weight of heavy metal and y is ratio of mass of fabricated fuel to that of fuel material (to consider structural materials like clad, spacers, end plates, etc.). The annual fuel cost can be determined as FC N , j = m N f · SCF N

(21)

SCFN (Rs./Te fuel) is specific cost of fabricated nuclear fuel. So, FCN becomes FC N =

T 





FC N , j

j=1

1+i 1+r

j (22)

Decommissioning cost. At the end of life of unit, the infrastructure built for energy generation is to be decommissioned and the waste thus generated should be properly treated. The cost incurred in this activity is a one-time cost at the end of life (EoL) of plant. The present value of this cost is estimated as  DC =

1+i 1+r

T ×

 (DCk (%) · CCk )

(23)

k

where DCk (%) gives decommissioning cost as % of capital cost. Salvage value. During refurbishment and at EoL of NRHES, the discarded components carry some economic value called as salvage value. The present values of revenue from salvage of replaced components (PSVrep ) and of total generating system at EoL (PSVEoL ) are given by PSVrep = 0.01 ×

Nref  k



SVk,rep (%) · CCk

j=1

 PSVEoL = 0.01 ×



1+i 1+r

1+i 1+r

 j·TR f,k 

T 

SVk,EoL (%) · CCk

(24)

(25)

k

3 Optimization The objective of optimization problem is minimization of levelized energy cost (LEC) of NRHES subject to following constraints

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SOCmin ≤ SOC(t) ≤ SOCmax LoLP < LoLPmax ; Pul,total < f ul,max PL ,annual ; Pde,total < f de,max PL ,annual

(26) (27)

n SPV , n N , n WG , n B , Pinv,r ≥ 0

(28)

n SPV , n N , n WG , n B ∈ Z

(29)

where f ul,max and f de,max are maximum fraction of load demand that is unmet and dumped in a year, respectively. PL,annual is total annual load demand. It can be observed that the optimization problem is a mixed integer nonlinear programming problem (MINLP) due to nonlinear constraints (Eq. 27) and integer decision variables. In graphical observation of LEC function, it is concluded that it has multiple local minima and hence non-convex in nature. A computational tool Hybrid Energy System Optimization (HESOPT) is developed using NOMAD [5], a derivative-free solver, to solve optimization problem. To avoid locking in local minima variable neighborhood search (VNS) feature of NOMAD is used. This tool also consists of in-house computer codes for energy flow simulation and costing. Energy flow simulation code simulates the energy flow between sources, load, and storage to evaluate the reliability of a given combination of sources and supplies nonlinear constraints to NOMAD. Costing code evaluates the LEC for given configuration of NRHES and acts as objective function for NOMAD. The workflow in HESOPT is shown in Fig. 2.

Fig. 2 HESOPT solver structure, (x is decision variable vector, x 0 is start point for MADS algorithm, x* is sampled point, x** is qualified point, LECmin is minimum LEC found, x optimum is optimum combination found)

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4 Case Study An example case study is performed considering the meteorological data and load demand of Mumbai city, India. PHWR technology is chosen for nuclear power in NRHES and reliability parameters are fixed as: LoLPmax = 0.01, f ul,max = 0.1 and f de,max = 0.1.

4.1 Inputs • Hourly basis solar insolation (for fixed axis, annual optimum orientation), ambient temperature, and wind velocity data are collected from PVGIS [6] for the year 2016. • The values taken for technical parameters involved in the models mentioned in Sect. 2.1 are summarized in Table 1. Technical parameters for nuclear, wind turbines, batteries, and power conversion systems are taken with typical values based on market survey and literature. • Economic parameters for the generating systems are identified based on regulatory and market scenario in India. All the parameters are summarized in Table 2. The inflation and discount rates are considered as 5% and 10%, respectively. Economic parameters for battery and P.C. are taken based on market survey. • The electricity load demand for the city of Mumbai for a typical summer day is considered for analysis [8] and given in Fig. 3a. It can be observed that the average load and minimum load are 2345 MW and 1817 MW, respectively. Table 1 Technical parameter inputs for various generating systems Generating system

Technical parameters

Nuclear (PHWR)

PNr = 700 MWe, ηN = 30%; LF = 90%; Life = 40 y; T Rf = 15 y; BUavg = 6700 MWd/Te HM; x = 1.13443 and y = 1.1 (Eq. 20)

Solar PV [9]

PSPV,r = 300 W; V oc, STC = 44.83 V; I sc, STC = 8.90 A; NCOT = 45 °C; K v = −0.31%/°C; K-I = 0.069%/°C; V MPP = 34.92 V; I MPP = 8.59 A; fixed axis mounted; Life = T Rf = 25 y

Wind generator (typical)

PWGr = 60 kW, vci = 2.5 m/s; vco = 20 m/s; vr = 10 m/s; H = 50 m; Life = T Rf = 20 y

Battery (typical)

E Ah = 200 Ah; C = 5 [2]; σ = 0.002/day [3]; V batt = 12 V; ηch = 0.75; ηdis = 1; Life = T Rf = 4 y; SOCmin = 0.2

Power converter (typical) ηAD = 0.95; ηDA = 0.95; Life = T Rf = 10 y

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Table 2 Economic parameter inputs for various generating systems Generating system

SCC

OMC

RfC (%)

SCF

DC

SVrep (%)

SVEoL (%)

Nuclear (PHWR) [7]

100 Rs/W

2%

5

25,000 Rs./kg

30%

0.5

10

Solar PV [4] 55 Rs./W

1.3%

60

0

0

6

10

Wind 62 Rs./W generator [4]

1.8%

100

0

0

20

20

Battery (typical)

10 Rs./Ah

3%

100

0

0

20

20

Power converter

11 Rs./W

0

100

0

0

10

10

Fig. 3 a Load profile used in case study. b The power generation, supply, and SOC of battery with optimum configuration of NRHES for a period of one year

4.2 Results and Discussion The optimum configuration obtained by HESOPT for the current case with three nuclear reactors is summarized in Table 3. This result is confirmed from graphical solution of this optimization problem in iterative manner. While nuclear power serves significant part (1.89 GW) of the load demand, to serve the rest of load (3 GW is required for renewables due to their Table 3 Optimum configuration of NRHES S. No.

1

Nuclear (MW)

Optimum installation capacity Wind (MW)

Solar PV (MW)

Battery (MWh)

Power converter (MW)

2100

1008.6

2155.7

17,437.9

1081.6

LEC (Rs./kWh)

LoLP

3.58

0.99%

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variable nature. The energy storage (>17 GWh) requirement is found to be very large. Observing the energy generation, supply and SOC of battery bank throughout the simulated year (Fig. 3b), it is found that renewable energy generation in monsoon reduces due to local meteorological conditions. This aspect dominates the selection of battery storage, leading to very large storage capacity requirement which remains underutilized through rest of the year.

5 Conclusions A techno-economic model is developed for simulation-based optimization of NRHES. The resulting MINLP problem is solved using in-house developed tool, HESOPT, which employs open-source NOMAD solver and in-house developed energy flow simulator and costing tools to perform optimization. A case study is performed to showcase the set of inputs required and the methodology of optimization. The case study involves a typical NRHES consisting of nuclear, solar, and wind sources with battery backup and power convertor provisions. The LEC for the optimum configuration of this NRHES is determined. The capabilities of VNS algorithm of NOMAD and random initial guess generator, integrated and harnessed in HESOPT tool, led to reliable performance of HESOPT in capturing the global optimum for the non-convex MINLP problem studied in this work.

References 1. Eftichios, K., Dionissia, K., Antonis, P., Kostas, K.: Methodology for optimal sizing of standalone photovoltaic/wind-generator systems using genetic algorithms. Sol. Energy 80, 1072–1088 (2006) 2. Deshmukh, M.K., Deshmukh, S.S.: Modelling of hybrid renewable energy systems. Renew. Sustain. Energy Rev. 12, 235–249 (2008) 3. Mohamed, M.A., Eltamaly, A.M., Alolah, A.I.: PSO-based smart grid application for sizing and optimization of hybrid renewable energy systems. PLoS ONE 11(8), e0159702 (2016) 4. The proposed levelised generic tariff for various renewable energy technologies for 2016–17, Central Electricity Regulatory Commission, India. http://cercind.gov.in/2016/orders/sm_3.pdf. Last accessed 1 August 2019 5. Le Digabel, S.: Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm. ACM Trans. Math. Softw. 37(4), 1–15 (2011) 6. Photovoltaic Geographical Information System, PVGIS 5. http://re.jrc.ec.europa.eu/pvg-tools/ en/tools.html [19.023399°N, 72.919568°E]. Last accessed 15 Mar 2019 7. Draft national electricity plan, Government of India, ministry of power, Central electricity authority, December 2016. http://www.cea.nic.in/reports/committee/nep/nep_dec.pdf. Last accessed 1 August 2019 8. AEEE. http://aeee.in/wp-content/uploads/2016/12/Delhi-Mumbai-Load-Profile-1024x350.jpg. Last accessed 1 August 2019 9. Adani Multi-crystalline solar PV modules. https://loopsolar.com/datasheet/adani-solar/datash eet-adani-solar-72-cell-300-330-Wp-India.pdf. Last accessed 1 August 2019

Exergy Analysis and Cost Optimization of Solar Flat Pate Collector for a Two-Stage Absorption Refrigeration System with Water-Lithium Bromide as a Working Pair Abhishek Verma, S. K. Tyagi, and S. C. Kaushik

Nomenclature A COP DERC FPC G HP HPA HPG HPSHX HPSTV HVARS LP LPA LPG LPSHX LPSTV N P Q RTV s T VCRS W

Area Coefficient of performance Delhi electricity regulatory commission Flat plate collector Solar energy falling on earth High pressure High-pressure absorber High-pressure generator High-pressure solution heat exchanger High-pressure solution throttling valve Half effect vapour absorption refrigeration system Low pressure Low-pressure absorber Low-pressure generator Low-pressure solution heat exchanger Low-pressure solution throttling valve Number of flat plates Pressure Heat transfer Refrigerant throttling valve Specific entropy Temperature Vapour compression refrigeration system Work

A. Verma · S. K. Tyagi (B) · S. C. Kaushik Indian Institute of Technology, Delhi, New Delhi 110016, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_56

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1 Introduction In the past few decades, there is an intensive demand for the use of energy, and it is increasing day by day. RAC has wide applications almost in every sector such as commercial, residential and industrial. More than 15% of the total electricity generated worldwide is consumed in RAC according to report prepared by the International Institute of Refrigeration (IIR), Paris [1]. Thus, their operation causes huge electricity demand globally. The increasing demand for RAC due to population growth challenges the refrigeration scientists and industry to make RAC equipment more energy efficient to conserve the energy. The traditional VCR systems indirectly contribute to global warming as 80% of the total electricity is being generated by exhausting fossil fuels, contributing noteworthy greenhouse gas emissions [2]. Besides the huge consumption of energy, climate changes have become an important issue. Therefore, the twin problem of energy degradation and energy crisis could be solved by using alternative refrigeration technologies which not only uses eco-friendly refrigerants but also it can be operated by renewable energy sources to some extent. Among all renewable sources of energy, solar energy outlooks on the top of the list being environmentally friendly, free availability, cleanliness and the coincidence that its peak availability is in phase with the peak cooling demand [3]. To produce cooling while utilizing low-temperature hot water, two types of absorption systems, stated as single effect and half effect system are used. The half effect absorption system can further reduce the generator temperature to produce cooling as compared to that of the single effect system. The name “half effect” has been introduced by the COP of the system, which is almost half of the single effect absorption cycle. However, the half effect system can be operated at relatively low-temperature heat origin. Kim [4] Ma and Deng [5] have experimentally investigated the 6 kW absorption chiller operating on half effect cycle. With the hot water at temperature 85 °C, the evaporator temperature of 7 °C can be achieved. Sumathy et al. [6] have tested the 100 kW absorption chiller functioning on half effect cycle. To achieve the chilled water of 9 °C, the generation temperature was varied from 65 to 75 °C. Gomri [7] have done the simulation of a half effect absorption chiller of 10 kW. Hot water was obtained by using the flat plate collector to run the system. Arora et al. [8] have presented the thermodynamic (energy, exergy) analysis of half effect absorption chiller and obtained the optimum intermediate pressure corresponding to the maximum COP and exergetic efficiency under various operating conditions. The maximum COP and exergetic efficiency (ηex ) achieved were in the range of 0.415–0.438 and 6.96–13.74%, respectively, for various operating temperatures. In the present work, the thermodynamic analysis based on energy and exergy has been carried out for a half effect system which has been driven by the hot water obtained from FPC. The area of FPC for the two generators of half effect system has been calculated for an optimum generator temperature, which is considered as the running cost of HVARS, and then it is compared with the VCRS to calculate the payback period of the half effect system.

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2 System Description The two-stage absorption system or half effect system comprises two water-lithium bromide solution circuits; one is at high pressure while the other is at low pressure. Both the two circuits include an absorber, a solution heat exchanger and a generator. Thus, the half effect system comprises a condenser, an evaporator, HP and LP absorbers, HP and LP generator, HP and LP solution heat exchangers, a refrigerant throttling valve and two solutions throttling valve. The cycle comprises three pressure levels, i.e. low pressure (LP), high pressure (HP) and intermediate pressure (IP) level. The HP generator and the condenser are at high-pressure level, evaporator and LP absorber are at the low-pressure level while the LP generator and HP absorber are at the intermediate pressure level. The refrigerant (i.e. water) vapour is generated at the HP generator and LP generator when the heat is supplied at low temperature by the flat plate collectors FPC-2 and FPC-1, respectively. The refrigerant vapour from HP generator is condensed in the condenser, expanded in refrigerant throttling valve and further evaporation takes place in the evaporator. The refrigerant vapour exiting evaporator is absorbed by the water-Libr solution within the LP absorber. Now the water-Libr solution with high Libr concentration is pumped to the LP generator through LPSHX, and the refrigerant vapour is further generated from this solution by the heat supplied by the FPC-1 (Fig. 1). The remaining high Libr concentration solution is expanded through the solution throttling valve. At LP generator, part of refrigerant (water) is generated and absorbed in the HP absorber. At HP absorber, the water-Libr solution with low Libr concentration is formed and pumped to the HP generator through an HPSHX.

Fig. 1 Half effect vapour absorption refrigeration system (HVARS)

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3 Thermodynamic Analysis of HVARS 3.1 Assumptions The subsequent points are assumed to analyse the half effect system [9] • • • • • •

All the components are assumed to be under steady-state condition. Kinetic, chemical and potential exergies are ignored. Pressure drop is neglected in pipelines and other components. The refrigerant (water) is in saturated state at the exit of condenser and evaporator. The operation of the half effect system ensures no crystallization. Water is utilized for cooling, heating and refrigerating processes having temperature T 0 (25 °C) and 1 atmospheric pressure (P0 ). • The pumping operation is considered to be isentropic. • Entropy change in throttling valve is neglected, and the temperature is assumed to be constant across the solution throttling valve.

3.2 System Model The analysis of the two stages half effect absorption cycle includes mass conservation, concentration balance, energy conservation and exergy balance for each component and is written as follows:   m˙ e = 0 (1) m˙ i −  

Q˙ −

m˙ i x˙i −



W˙ =

 

m˙ e x˙e = 0 m˙ e h˙ e −



(2) m˙ i h˙ i

(3)

Performance parameter based on the first law of thermodynamics COP =

Q˙ Evap. Q˙ HPG + Q˙ LPG + W˙ P−1 + W˙ P−2

(4)

The rate of exergy flow for a stream on each state point is calculated as follows [10]: E˙ = m[(h ˙ − h 0 ) − T0 (s − s0 )]

(5)

For a steady-state process, the exergy destruction rate in a component is given as follows:

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Table 1 Energy and exergy equations for HVARS Components LPG HPG

Energy equations Q˙ LPG + m 3 h 3 = m 4 h 4 + m 17 h 17

Exergy equations .

E D = E˙ 3 + E˙ 18 − E˙ 4 − E˙ 7 − E˙ 19 LPG .

Q˙ HPG + m 9 h 9 = m 10 h 10 + m 13 h 13

E D = E˙ 9 + E˙ 20 − E˙ 10 − E˙ 13 − E˙ 21

HPG .

LPA

Q˙ LPA + m 1 h 1 = m 6 h 6 + m 16 h 16

HPA

Q˙ HPA + m 7 h 7 = m 12 h 12 + m 17 h 17

Evaporator

Q˙ Evap. = m 16 (h 16 − h 15 )

E D = E˙ 6 + E˙ 16 + E˙ 26 − E˙ 1 − E˙ 27 LPA .

E D = E˙ 12 + E˙ 17 + E˙ 24 − E˙ 7 − E˙ 25 HPA .

E D = E˙ 15 + E˙ 28 − E˙ 16 − E˙ 29

Evap.

Condenser

Q˙ Cond. = m 13 (h 13 − h 14 )

LPSHX

Q˙ LPSHX = m 3 (h 3 − h 2 )

HPSHX

.

E D = E˙ 13 + E˙ 22 − E˙ 14 − E˙ 23

Cond. .

E D = E˙ 2 − E˙ 3 + E˙ 4 − E˙ 5

LPSHX .

Q˙ HPSHX = m 9 (h 9 − h 8 )

Pump-1

W˙ P−1 = m 2 (h 2 − h 1 )

Pump-2

W˙ P−2 = m 8 (h 8 − h 7 )

E D = E˙ 8 − E˙ 9 + E˙ 10 − E˙ 11

HPSHX .

E D = E˙ 1 + W˙ P−1 − E˙ 2 P−1 .

E D = E˙ 7 + W˙ P−2 − E˙ 8 P−2

LPSTV HPSTV RTV

.

h6 = h5

E D = E˙ 5 − E˙ 6

LPSTV .

h 12 = h 11

E D = E˙ 11 − E˙ 12

HPSTV .

h 15 = h 14

E D = E˙ 14 − E˙ 15

RTV

.

ED =



E˙ i −



E˙ e +



   T0 ˙ − W˙ Q 1− T

(6)

The exergetic efficiency for HVARS is given as follows [11] (Table 1): 

.

ED ηex = 1 − E˙ 18 − E˙ 19 + E˙ 20 − E˙ 21 + W˙ P−1 + W˙ P−2

(7)

3.3 Input Parameters For the thermodynamic analysis of HVARS, a computer code is written in Engineering Equation Solver (EES) with the given input parameters (Table 2).

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Table 2 Input parameters for half effect absorption refrigeration system

Parameters

Value

Refrigeration capacity, Q˙ Evap. (kW)

25

Environment temperature, T 0 (°C)

25

Environment pressure, P0 (kPa)

101.3

Evaporator temperature, T Evap. (°C)

7

HPG temperature, T HPG (°C)

80

LPG temperature, T LPG (°C)

80

Condenser temperature, T Cond. (°C)

38

HPA temperature, T HPA (°C)

38

LPA temperature, T LPA (°C)

38

Effectiveness of HP SHX, 1HPSHX

0.7

Intermediate pressure, Pi (kPa)

4.953

Effectiveness of LP SHX, 1LPSHX

0.7

The heat input to the two generators is found out by the thermodynamic analysis of the system with the above input parameters given. The amount of heat required in two generators is the basis for the calculation of the area of the flat collector. The flat plate collector has a dimension (2.00 mm × 1.50 mm), gross area, AG (3 m2 ) and cost (Rs. 7500) per unit. The heat energy absorbed by the solar flat plate collector (FPC) is given by [12] Q = ηthFPC × G × A

(8)

where ηthFPC is the efficiency of FPC (ηthFPC = 0.45), G is the average solar radiation heat falling on earth surface is 5.2 kwh/m2 /day (i.e. 210 W/m2 ) [13], and A is the area of FPC. Efficiency of global system is ηglobal=COP × ηth FPC

(9)

Number of FPC required: N = A/A G

(10)

The heat required in HP and LP generators is Q˙ H P G (27.48 kW) and Q˙ L P G (32.65 kW), respectively, the area of FPC required at HP and LP generator is AHP (291 m2 ) and ALP (346 m2 ), respectively, and the respective number of FPC is N HP (97) and N LP (116).

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3.4 Model Validation The performance parameters of the half effect system based on energy and exergy analysis have been compared with similar input parameters provide in Arora et al. (2016). The simulation results indicated that the variation of COP and exergetic efficiency (ηex ) with the generator temperature (T G = 60–80 °C), which follows a similar trend as shown in Figs. 2 and 3. The deviation of 2.44% in maximum COP and 1.78% in exergetic efficiency (ηex ) of the system was found, which is under the acceptable limit as per the literature. The generator temperature corresponding to the maximum COP and exergetic efficiency was not provided in Arora et al. (2016). Therefore, in the presented study, the generator temperature is approximated, on the basis of the range provided by Arora et al. (2016). The deviation in results is due to the approximation of generator temperature corresponding to the maximum COP and exergetic efficiency (ηex ) in the present study (Table 3). Fig. 2 Variation of COP and ηex with T G (Arora et al. 2016)

Fig. 3 Variation of COP and ηex with T G (present study)

0.3 0.5

COP

η ex

0.25

0.4

0.15

0.2

0.1

0.1

0.05

0 60

64

72

68

TG (°C)

76

0 80

η ex

COP

0.2 0.3

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Table 3 Comparison of performance parameters of half effect system with numerical data given in Arora et al. (2016) (parameters: T G = 60–80 °C, T C = 37.8 °C and T E = 7 °C, mass flow rate of refrigerant = 1 kg/s, effectiveness of solution heat exchanger(s) = 0.7)

Parameters

Arora et al. (2016)

Present study

Deviation (%)

COP

0.41

0.4

2.44

Exergetic efficiency (ηex )

9.5%

9.33%

1.78

3.5 Cost Analysis of the System Assumptions: • The cost of two systems is calculated on the basis of the office space required cooling for 8 h per day. • The cost of flat plate collector installation is the only cost considered for the calculation of payback period of HVARS. • The costs of the component of the two systems are considered to be same. • The cost of the compressor of VCRS is compensated by the cost of generator used in HVARS. Maintenance cost is negligible. • The rate of electricity is taken from the Delhi Electricity Regulatory Commission [14]. The proposed solar-driven half effect system is compared with the conventional vapour compression refrigeration system (VCRS) with the same input parameters. The cost of the VCRS system is due to the cost of electricity, while the cost associated with the HVARS system is only the cost of flat plate collectors. The payback period of half effect solar-driven absorption unit (HVARS) is calculated by comparing the cost of HVARS system with the conventional VCRS system running for the same input parameters, i.e. condenser temperature, evaporator temperature, cooling capacity, etc. The generator of HVARS is replaced by the compressor of the conventional VCRS which runs on electricity, while the rest of the components of the two systems are same. Payback period = cost of HVARS/expenditure per year of VCRS.

(11)

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4 Results and Discussion 4.1 Results from HVARS COP and exergetic efficiency: Figures 4 and 5 show the effect of HP and LP generator temperature (T G ) on COP and exergetic efficiency for different condenser temperatures. The COP and exergetic efficiency values increase with generator temperature and decrease with condenser temperature (T C ). There exists a minimum generator temperature for which the COP is maximum for a specific condenser temperature. Further increment in generator temperature does not contribute to any significant increase in COP. On the contrary, it decreases the exergetic efficiency. For T G = 80 °C, T C = 38 °C and T E = 7 °C, the maximum COP, exergetic efficiency and global efficiency obtained are 0.416, 7.36% and 18.7%, respectively. Fig. 4 Effect of T G on COP at various T C

0.5

0.4

0.3

o

COP

Tc=30(oC) o

Tc=34(oC) o

Tc=38(oC)

0.2

o

Tc=42(oC)

0.1

0 55

60

65

70

75

80

85

90

85

90

TG

Fig. 5 Effect of T G on exergetic efficiency at various T C

0.12

Exergetic Efficiency

0.1

0.08

0.06

o

Tc=30( C) o

Tc=34( C) o Tc=38( C) o Tc=42( C)

0.04

0.02

0 55

60

65

70

75

TG

80

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Area of FPC at HP&LP stages: Figures 6 and 7 show the effects of HP and LP generator temperature on FPC area for HP and LP stages, respectively. There is less area required for both the stages as the condenser temperature decreases. The area of FPC required on the HP stages is less than that for the LP stage. This is due to the fact that more amount of heat is desired to generate the refrigerant vapour from the LP stage as compared to the HP stage. For an optimum generator temperature, the area required is minimum for both the stages. The optimum generator temperature obtained is 80 °C. For this generator temperature, 7 °C evaporator temperature and 38 °C condenser temperature, the minimum area required oh HP stage is AHP (291 m2 ) and LP stage is ALP (346 m2 ). The area required for the two stages is proportional to the cost of the system. Fig. 6 Effect of T G on the area of FPC at HP side for various T C

305

300 o

Tc=30( o C)

295

Tc=34(o C)

290

Tc=42(o C)

o

o

A HP

Tc=38(o C) o

285

280

275

270 55

60

65

70

75

80

85

90

TG

Fig. 7 Effect of T G on the area of FPC at HP side for various T C

550

500

o

Tc=30( o C) o

Tc=34( o C) o

Tc=38( o C)

A LP

450

o

Tc=42( o C)

400

350

300 55

60

65

75

70

TG

80

85

90

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4.2 Results from Cost Analysis The power consumed by the compressor in VCRS (P) is 15.2 kW, which is used for 8 h per day. Energy consumption per year (E) is 44,471.6 kWh. The cost of this energy is on the basis of DERC (fixed charges (per month) = Rs. 250 per kW, energy charges = Rs. 7.25 per kWh.). Thus, the expenditure per year is Rs. 397,419 that is the running cost of VCRS for a year is Rs. 4 lakhs approx. The cost of flat plate collectors at HP and LP generator side is Rs. 727,500 and Rs. 870,000, respectively. Thus, the total cost of FPC required is Rs. 16 lakhs approx. This is the only cost which is considered to cool the given space. Therefore, the payback period is 4 years.

5 Conclusions The inferences from the results are concluded below: • The COP of the system increases from the high evaporator and generator temperature. As the generator temperature (T G ) increases, the refrigerant generation increases up to a certain limit and then remains constant. • Further increment in generator temperature does not contribute to any significant increase in COP. On the contrary, it decreases the exergetic efficiency. For T G = 80 °C, T C = 38 °C and TE = 7 °C, the maximum COP, exergetic efficiency and global efficiency obtained are 0.416, 7.36% and 18.7%, respectively. • The area of FPC required is minimum for an optimum generator temperature, which comes out to be 80 °C. Therefore, the cost of the system is minimum for this generator temperature. • For this generator temperature, 7 °C evaporator temperature and 38°C condenser temperature, the minimum area required at HP stage is AHP (291 m2 ) and LP stage is ALP (346 m2 ). The area required for the two stages is proportional to the cost of HVARS. • For HVARS, there exists a minimum generator temperature (T G ) below which the system will stop working. In the present study, it is found to be 67.5 °C for intermediate pressure of 4.935 kPa. (when T E = 7 °C, T C = 38 °C and T G = 80 °C). • The proposed solar-driven half effect system is compared with the vapour compression refrigeration system (VCRS) with the same input parameters. The vital goal in the long term is to reduce the consumption of high-grade energy used in refrigeration and air conditioning. • The running cost of VCRS for a year is Rs. 4 lakhs approx. The cost of flat plate collectors at HP and LP generator side is Rs. 727,500 and Rs. 870,000, respectively. Thus, the total cost of FPC required is Rs. 16 lakhs approx. This is the only cost which is considered to cool the given space. Therefore, the payback period is 4 years.

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References 1. Kalkan, N., Young, E.A., Celiktas, A.: Solar thermal air conditioning technology reducing the footprint of solar thermal air conditioning. Renew. Sustain. Energy Rev. 16(8), 6352–6383 (2012) 2. Fong, K.F., et al.: Solar hybrid air-conditioning system for high temperature cooling in subtropical city. Renew. Energy 35(11), 2439–2451 (2010) 3. Domínguez-Inzunza, L.A., et al.: Comparison of the performance of single-effect, half-effect, double-effect in series and inverse and triple-effect absorption cooling systems operating with the NH3 –LiNO3 mixture. Appl. Therm. Eng. 66(1–2), 612–620 (2014) 4. Kim, D.S.: CA infante ferreira. Solar refrigeration options–a state-of-the-art review. Int. J. Refrig. 31(1), 3–15 (2008) 5. Ma, W.B., Deng, S.M.: Theoretical analysis of low-temperature hot source driven two-stage LiBr/H2 O absorption refrigeration system. Int. J. Refrig 19(2), 141–146 (1996) 6. Sumathy, K., Huang, Z.C., Li, Z.F.: Solar absorption cooling with low grade heat source—a strategy of development in South China. Sol. Energy 72(2), 155–165 (2002) 7. Gomri, R.: Solar energy to drive half-effect absorption cooling system. Int. J. Therm. Environ. Eng. 1(1), 1–8 (2010) 8. Arora, A., Dixit, M., Kaushik, S.C.: Computation of optimum parameters of a half effect water lithium bromide vapour absorption refrigeration system. J. Therm. Eng. 2(2), 683–692 (2016) 9. Herold, K.E., Radermacher, R., Klein, S.A.: Absorption Chillers and Heat Pumps. CRC Press (2016) 10. Dincer, I., Rosenm M.A.: Exergy: Energy, Environment and Sustainable Development. Newnes (2012) 11. Manoj, D., Arora, A., Kaushik, S.C.: Thermodynamic and thermoeconomic analyses of two stage hybrid absorption compression refrigeration system.Appl. Therm. Eng. 113, 120–131 (2017) 12. Bajpai, V.K.: Design of Solar Powered Vapour Absorption System. In: Proceedings of the World Congress on Engineering, vol. 3 (2012) 13. Analyze the Solar Irradiance at New Delhi, Delhi, India. http://www.synergyenviron.com/tools/ solarirradiance/New+Delhi%252CDelhi%252CIndia. Last accessed 10 Dec 2018 14. Delhi electricity regulatory commission, http://www.derc.gov.in/. Last accessed 22 Dec 2018

Characterizing the Helical Vortex Frequency of HAWT Ojing Siram and Niranjan Sahoo

1 Introduction Efficient modelling of wind farm is greatly influenced by downstream characteristic of wind turbine wake which is conventionally composed of atmospheric turbulence, vorticity effects due to blade, turbulence contribution originating from the wake generated shear and finally the meandering movement of the wake. The investigation will help in wake modelling of wind turbine so that interference of wakes from neighbouring turbine can be minimized, at the same time enhance wind farm efficiency and minimize the land uses. Characterizing wind turbine wakes requires in-depth knowledge of vortex shedding, wake expansion and wake meandering effect downstream of turbine. So, in this regard present experimental investigation focuses on vortex frequency response and its stability analysis using hot-wire anemometer. In present study, ambient turbulence is neglected since it mainly contains high-frequency turbulence as detected through HWA, and the range of frequency for WT under interest often falls in low frequency range ( 60° unstable frequency response. Velocity field measurement was performed in downstream (z/R = 0.5), and it has been observed that there is a gradual decrease in velocity deficit on increasing the upstream velocity; this behaviour could be attributed to the fact that as wind speed increases rotor rpm also increases subsequently drop in velocity field in observed within near wake regime. In the later stage of research, further detail about the wake expansion will be carried out that will enhance our understanding and will help in wind farm modelling. Acknowledgements The authors would like to acknowledgement Dr. Vinayak Kulkarni, Associate Professor, Department of Mechanical Engineering IITG, for providing necessary instrument/facility like wind tunnel to carry out the research work.

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References 1. Jensen, N.O.: A note on wind generator interaction, Technical Report Risø-M-2411, Risø National Laboratory (1984) 2. Larsen, G.C.: A Simple Wake Calculation Procedure; Report Risø-M-2760; Risø National Laboratory: Roskilde, Denmark, pp. 1–53 (1988) 3. Frandsen, S.: On the wind speed reduction in the centre of large clusters of wind turbines. J. Wind Eng. Ind. Aerodyn. 39, 251–265 (1992) 4. Göçmen, T., Laan, P.V.D., Réthoré, P.E., Diaz, A.P., Larsen, G.C., Ott, S.: Wind Turbine wake models developed at the technical university of Denmark: a review. Renew. Sustain. Energy Rev. 60, 752–769 (2016) 5. Vermeer, L.J., Sorensen, J.N., Crespo, A.: Wind turbine wake aerodynamics. Prog. Aeros. Sci. 39, 467–510 (2003) 6. Medici, D., Alfredsson, P.H.: Measurements on a wind turbine wake: 3D effects and bluff body VORTEX shedding. Wind Energy 9, 219–236 (2006) 7. Medici, D., Alfredsson, P.H.: Measurements behind model wind turbines: further evidence of wake meandering. Wind Energy 11, 211–217 (2008) 8. Vermeer, N.J.: Velocity Measurements in the Near Wake of a Model Rotor (in Dutch). In: Fourth Dutch National Wind Energy Conference, pp. 209–212, Noordwijkerhout, The Netherlands (1988) 9. Vermeer, N.J.: Velocity Measurements in the Near Wake of a Model Rotor. In: European Wind Energy Conference 1989, pp. 532–535, Glasgow, UK (1989) 10. Vermeer, N.J., Van Bussel, G.J.W.: Velocity Measurements in the Near Wake of a Model Rotor and Comparison with Theoretical Results. In: Fifteenth European Rotorcraft Forum, pp. 20/1–20/14, Amsterdam, Netherlands (1989) 11. Okulov, V.L., Sørensen, J.N.: Stability of helical tip vortices in rotor far wake. J. Fluid Mech. 576, 1–25 (2007) 12. Okulov, V.L., Sørensen, J.N.: Application of 2D helical vortex dynamic, Theor. Comput. Fluid Dyn. 24, 395–401 (2010) 13. Whale, J., Bareiss, R., Wagner, S., Anderson, C.G.: The wake structure of a wind turbine rotor comparison between PIV measurements and free-wake calculations. In: Zervos, A. (ed.) Proceedings of the 1996 European Wind Energy Conference, pp. 695–698. Goteborg, Sweden: H.S. Stephens & Associates (1996) 14. Ebert, P.R., Wood, D.H.: The near wake of a model horizontal-axis wind turbine: Part 1: experimental arrangements and initial results. Renew. Energy 12, 225–243 (1997) 15. Ebert, P.R., Wood, D.H.: The near wake of a model horizontal-axis wind turbine: Part 2: general features of the three-dimensional flow-field. Renew. Energy 18, 513–534 (1999) 16. Ebert, P.R., Wood, D.H.: The near wake of a model horizontal-axis wind turbine: Part 3: properties of the tip and hub vortices. Renewable Energy 22, 461–472 (2001) 17. Snel, H.: Review of the present status of rotor aerodynamics. Wind Energy 1, 46–69 (1998) 18. Sørensen, J.N., Wen, Z.S.: Numerical modelling of wind turbine wakes. J. Fluids Eng. 124, 393–399 (2002) 19. Zhang, W., Corey, D., Corey, D.: Near-wake flow structure downwind of a wind turbine in a turbulent boundary layer. Exp. Fluids 52, 1219–1235 (2012) 20. Bruun, H.H.: Hot-wire Anemometry-Principle and Signal Analysis. Oxford University Press, UK (1995) 21. Gupta, S.V.: Measurement Uncertainties: Physical Parameters and Calibration of Instruments. Springer, Berlin (2012) 22. Fisichella, C.J.: An improved prescribed wake analysis for wind turbine rotors. Ph.D. Thesis, University of Illinois, Urbana, Illinois, USA (2001, April) 23. Nemes, A., Sherry, M., Lo Jacono, D., Blackburn, H.M., Sheridan, J.: Evolution and breakdown of helical vortex wakes behind a wind turbine. J. Phy. Conf. Ser. 555, 012077 (2014)

Design and Development of Concentrated Solar Cooker with Parabolic Dish Concentrator Susant Kumar Sahu , Natarajan Sendhil Kumar , and K. Arjun Singh

1 Introduction 1.1 Solar Parabolic Dish A parabolic dish is a point focusing system, having certain advantages compared to other solar collectors in terms of perpetual facing to sun, high thermal and optical efficiency, high concentration ratio range (600–2000), high temperature achievement up to 1500 °C, and absence of cosine losses [1].

1.2 Concentrator Solar Cooker 4 kg cooking capacity of a non-tracking solar cooker with width-to-length ratio of the reflector was 1 designed and a box-type solar cooker was also developed for comparative analysis by Nahar [2]. The stagnation temperatures of non-tracking solar cooker and box-type solar cooker were measured 118.5 and 108 °C, respectively. The average efficiency of the former was estimated 27.5%. Moreover, it has been claimed and tested that same system was able to cook several items such as rice, beans, lentils, and cauliflowers within a time span of 2 cooking hours whereas S. K. Sahu (B) Raghu Engineering College, Visakhapatnam 531162, India e-mail: [email protected] N. Sendhil Kumar · K. Arjun Singh National Institute of Technology Puducherry, Karaikal 609609, India e-mail: [email protected] K. Arjun Singh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_58

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3 cooking hours were to be spent particularly for hard food cooking in Jodhpur climatic conditions [2]. Kedar et al. [3] did a thermal analysis on a solar cooker using a parabolic reflector with glass mirror intended to save electrical energy. Also, extensive study has been carried out based on thermal loss and optical losses while designing the parabolic solar cooker. Moreover, it has been cited during comparative study that overall efficiency and heat carrying capacity of parabolic cooker were higher than that of box-type cooker. However, it has also been revealed that the LPG has the highest rank whereas chulha got the lowest rank but in this contest solar parabolic cooker would manage to occupy in fifth position as far utility is concerned [4]. Mauricio et al. have proposed a successful methodology by integrating spring-type mechanical equipment with cooker. This study was processed and monitored in the area of Meseta Purepecha in Michoacán, Mexico [5]. Ibrahim has designed a parabolic dish integrated with a solar thermal cooker with an aperture diameter 1.8 m, depth 0.29 m, and focal length 0.698 m, respectively. The cooker was developed to cook food equivalent to 12 kg of dry rice per day. The maximum cooking time period was found to be 90–100 min for 3.0 kg of rice [6]. Another solar parabolic square dish cooker was designed by El Kassaby [7] and the same was simulated with certain computer program analytically. A total efficiency of 17.46% was reported corresponding to an average solar irradiance of 930 W/m2 . Similarly, a manual tracking parabolic solar disc concentrator was fabricated by Yogesh et.al. [8] meant for domestic cooking application with an aperture area of 0.628 m2 in which concentrating solar radiation was reflected onto the focal plane. The thermal performance of a paraboloidal mirror-based concentrator with an aperture area 0.58 m2 was evaluated by Mullick et.al. [9]. An experimental investigation based on convective and radiative heat losses analysis of a solar parabolic concentrator cooker was carried out by Dasin [10]. It was reported that the convection heat losses from absorber to the surrounding air is 0.244 W, cooking pot cover to the surrounding air is 0.096 W, and the air gap between the pot cover was 0.005 W. It was accomplished that convective heat transfer losses are high for this particular type of cookers [10]. For the quality control assurance certification purpose, some of the experiments were conducted in Delhi climatic conditions using both parabolic concentrating solar cooker and boxtype solar cooker without a specific load (lit./m2 ) and also some standard tests were performed on both case of solar cooker using suitable standard measuring instruments. It has been recommended that appropriate ranges of performance indicators and accuracies of measuring instruments must be defined properly while following the standard procedure of experimentations [11]. Instrumentation error analysis of a paraboloid concentrator-type solar cooker was studied in depth by Ishan et.al. [12] and the measurement of associated climatic and operating parameters of cooker has a significant effect on thermal performance parameters. Hasan [13] designed a low-cost parabolic-type solar cooker (SPC) and 2-E analysis was carried out experimentally. The testing time period was chosen from 10:00 am to 02:00 pm of solar time. The energy output of the SPC was varied between 20.9 W and 78.1 W during period of experimentation. The temperature of water in the cooker pot was reported to be 333 K. The difference between temperature of water in the cooking pot and ambient air temperature was recorded 31.6 K. The exergy value varied from 2.9 to 6.6 W

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was reported [13]. A solar parabolic dish concentrated cooker was tested in South Africa, the cooking performance was analyzed by following international standard procedures and a utility efficiency of 17% was reported. The energy efficiency of the device was 0.05% and the average time period is 3.32 min/kg [14]. Gavisiddesha et al. [15] Investigated a solar parabolic concentrator cooker during summer season at Hubli city situated in Karnataka, India. The designed specifications of the dish were mentioned as 1.4 m of aperture diameter, depth 0.4 m, and a focal length of 0.30 m. The testing procedure was carried under no-load and full-load conditions. The optical efficiency aspect and heat loss factor were studied experimentally, and simultaneously the cooking capacity along with standard cooking powers were also calculated. Paul [16] evaluated the international standard procedure for testing solar cookers and reported the performance of solar cooker under common environmental conditions. The cooking power curve was proposed for such type of device and which is useful for interpreting the capacity and heat retention ability of the cooker.

2 Experimental Setup The setup consists of a parabolic dish concentrator with manual dual-axis tracking system, a cooker, solar power meter, hot-wire anemometer, and temperature sensors. The details of components are discussed in the following subsections.

2.1 Design and Development of Concentrator Solar Parabolic Dish with Cooker A parabolic dish concentrator system with manual dual-axis tracking system is designed based on literature review [17] and the same has been modeled using suitable design software SOLID WORKS shown in Fig. 1. The detailed design specifications of the solar parabolic dish concentrator system are portrayed in Table 1. Likewise, a support system provision has also been provided at focus of dish in order to carry the cooker. A two-axis manual tracking system is utilized to follow the sun during day time to allow the concentration of solar irradiance where intended. During the design of manual tracking system, altitude angle and azimuth angle are taken into consideration in order to make it more efficient. The effective rim angle of the dish is taken as 45° for evaluating the focal length.

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Fig. 1 Design of the parabolic solar dish cooker

Table 1 Design specifications of solar parabolic dish concentrator S. No.

Parameter

Notation

Mathematical model

Value

01 02

Diameter

Dd

–

0.92 m

Aperture area

Ad

π 4

03

Focal distance

f

d2 16 h

04

Rim angle

Ψ

06

Depth

(d)2

1

d 2h 8h − d

0.66 m2 0.56 m 

45°

h

d2 16 f

0.095 m 28.76 0.95

07

Geometric concentration ratio

Cg

Ad Aγ

08

Reflectivity of mirror

R

–

2.2 Development of Solar Parabolic Dish The dish concentrator along with tracking system is fabricated using the conventional fabrication process. The material selected for the body structure of dish and the tracking system is mild steel sheet of 0.3 mm thickness and MS flat of (22 × 4) mm.

Design and Development of Concentrated Solar Cooker … Table 2 Technical specifications of pressure cooker

625

S. No.

Technical parameter

Detail

01

Manufacturer

Hawkins

02

Model

Classic

03

Material

Aluminum

04

Color

Silver

05

Certification

Indian standards institute

06

Ideal cooking

1–3 persons

07

Base flat diameter

134 mm

08

Base thickness

3.2 mm

The surface of the dish was covered with 2 mm thick square glass mirrors (5 mm × 5 mm) of reflectivity 0.9. The glasses were pasted onto the dish using silicone gel adhesive.

2.3 Cooker A pressure cooker of 2-liter capacity was manufactured by Hawkins; the classic model was chosen for experimentation purpose and same was purchased from local market commercially available. The technical specification of the cooker as per Bureau of Indian Standards (The National Standard Body of India) mentioned by the manufacturer is mentioned in Table 2. The entire body of the cooker was painted with black enamel paint to absorb maximum heat energy out of solar energy incident on it.

2.4 Measuring Equipment A solar power meter manufactured by MECO Instruments Pvt. Ltd was employed to measure the global horizontal radiation (GHR) incident on the concentrator. Hotwire anemometer supplied by HTC instruments was utilized to measure the wind velocity. Total two temperature sensors of Mini LCD thermometer type supplied by Ap Techdeals were employed to measure the ambient air temperature and cooker base temperature. Briefly, the technical specifications of the measuring equipment are mentioned in Table 3.

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Table 3 Technical specifications of measuring equipment S. No.

Sensor

Technical parameter

Details

01

Solar power meter (high sensitivity silicon photodiode)

Spectral response

0–2000 W/m2 (0~634 BTU/ft.2 h)

Accuracy (at 23 °C, 60% RH)

±10 W/m2 (±3BTU/ft.2 h) or ±5% whichever is greater

Resolution

0.0–99.99 W/m2 : 0.01 W/m2

Angular accuracy

Cosine corrected < 7% (angle 0 ∧ βyi > 0 ∧ τ yi > 0) ∧ (ηyi > 0 ∧ ηyi ≤ 0) then Determine max{H (yi )} Set maximum {T (H )} and it is produced when choosing the value (α, τ, β) > 0 m. Or else if α = β = τ ≤ 0 then set T (H ) = 0 n. (α > 0 ∧ β > 0 ∧ τ > 0) ∧ (η > 0 ∧ η ≤ 0) o. End if

p. Create the set Q = Q k1,1 , . . . , Q kji q. Decision of Response tissue integration D(yi , t) is engendered r. When D(yi , t + 1) > D(yi , t) then s. Response Q (yi ,t+1) is chosen t. Or else when D(yi , t + 1) < D(yi , t) then u. Response Q (yi ,t) is chosen / Q then v. Or else if Q (yi ,t) ∈ w. Fix Q (yi ,t) = 0 x. End if y. Fitness value Fi will be computed z. When Fi < Fi∗ then aa. Intelligent phenotype E i will be produced bb. Or else if Fi > Fi∗ then cc. Reanalyze Pi and fix Fi = Fo dd. End if ee. Define the solidity of Z (yi ) of E i ff. When Z (yi ) ≥ Z yi∗ then gg. Fix Z (yi ) = Z ∗ (yi )   ∗ hh. Or else  if∗ Z (yi )∗≤ Z yi then ii. Fix Z yi = Z (yi ) jj. End if   kk. Determine the present best solution O yi∗ ll. End while mm. Output the results and stop.

Bamboo Plant Intellect Deeds Optimization … Table 1 Constraints of control variables

Table 2 Constrains of reactive power generators

669

Variables type

Minimum value (PU)

Maximum value (PU)

Generator voltage

0.95

1.1

Transformer tap

0.9

1.1

VAR source

0

0.20

Variables

Q minimum (PU)

Q maximum (PU)

1

−140

200

2

−17

50

3

−10

60

6

−8

25

8

−140

200

9

−3

9

−150

155

12

4 Simulation Study In standard IEEE 57 Bus system [18] projected Bamboo Plant Intellect Deeds Optimization Algorithm (BPD) has been evaluated. Table 1 shows the constraints of control variables, Table 2 shows the limits of reactive power generators and comparison results are presented in Table 3. Figure 1 shows the comparison of Real Power Loss and Fig. 2 Indicates the Real power loss reduction in percentage.

5 Conclusion Bamboo Plant Intellect Deeds Optimization Algorithm (BPD) successfully solved the optimal reactive power problem. With reference to variations in the environmental conditions, Bamboo Plants have the capability to alter its actions and it will regulate its morphology, physiology, and phenotype. Proposed Bamboo Plant Intellect Deeds Optimization Algorithm (BPD) has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss efficiently.

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K. Lenin

Table 3 Simulation results of IEEE 57 system Control variables

Base case MPSO [19] PSO [19] CGA [19] AGA [19] BPD

VG 1

1.040

1.093

1.083

0.968

1.027

1.010

VG 2

1.010

1.086

1.071

1.049

1.011

1.008

VG 3

0.985

1.056

1.055

1.056

1.033

1.012

VG 6

0.980

1.038

1.036

0.987

1.001

1.010

VG 8

1.005

1.066

1.059

1.022

1.051

1.017

VG 9

0.980

1.054

1.048

0.991

1.051

1.020

VG 12

1.015

1.054

1.046

1.004

1.057

1.022

Tap 19

0.970

0.975

0.987

0.920

1.030

0.910

Tap 20

0.978

0.982

0.983

0.920

1.020

0.919

Tap 31

1.043

0.975

0.981

0.970

1.060

0.913

Tap 35

1.000

1.025

1.003

NR*

NR*

1.012

Tap 36

1.000

1.002

0.985

NR*

NR*

1.021

Tap 37

1.043

1.007

1.009

0.900

0.990

1.010

Tap 41

0.967

0.994

1.007

0.910

1.100

0.910

Tap 46

0.975

1.013

1.018

1.100

0.980

1.027

Tap 54

0.955

0.988

0.986

0.940

1.010

0.932

Tap 58

0.955

0.979

0.992

0.950

1.080

0.937

Tap 59

0.900

0.983

0.990

1.030

0.940

0.948

Tap 65

0.930

1.015

0.997

1.090

0.950

1.052

Tap 66

0.895

0.975

0.984

0.900

1.050

0.910

Tap 71

0.958

1.020

0.990

0.900

0.950

1.027

Tap 73

0.958

1.001

0.988

1.000

1.010

1.024

Tap 76

0.980

0.979

0.980

0.960

0.940

0.939

Tap 80

0.940

1.002

1.017

1.000

1.000

1.019

QC 18

0.1

0.179

0.131

0.084

0.016

0.136

QC 25

0.059

0.176

0.144

0.008

0.015

0.147

QC 53

0.063

0.141

0.162

0.053

0.038

0.108

PG (MW)

1278.6

1274.4

1274.8

1276

1275

1272.31

QC (Mvar)

321.08

272.42

272.27

276.58

309.1

304.4

Reduction in PLoss (%) 0

15.4

14.1

9.2

11.6

23.96

Total PLoss (MW)

23.51

23.86

25.24

24.56

21.139

27.8

Bamboo Plant Intellect Deeds Optimization … Fig. 1 Comparison of real power loss (X axis-methods; Y axis-real power loss (MW))

30

671

Real Power Loss

25 20 15 10 5 0 Base MPSO PSO case

CGA AGA BPD

Real Power Loss Fig. 2 Real power loss reduction in percentage (X axis-methods; Y axis-percentage of real power loss reduction)

Reduction in PLoss (%) 30 25 20 15 10 5 0 Base MPSO PSO CGA AGA BPD case Reduction in PLoss (%)

References 1. Lee, K.Y.: Fuel-cost minimization for both real and reactive-power dispatches. Proc. Gener. Transm. Distrib. Conf. 131(3), 85–93 (1984) 2. Deeb, NI.: An efficient technique for reactive power dispatch using a revised linear programming approach. Electr. Power Syst. Res. 15(2), 121–134 (1998) 3. Bjelogrlic, M.: Application of newton’s optimal power flow in voltage/reactive power control. IEEE Trans Power Syst. 5(4), 1447–1454 (1990)

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4. Granville, S.: Optimal reactive dispatch through interior point methods. IEEE Trans. Power Syst. 9(1), 136–146 (1994) 5. Grudinin, N.: Reactive power optimization using successive quadratic programming method. IEEE Trans. Power Syst. 13(4), 1219–1225 (1998) 6. Mei, R.N.: Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl. Soft Comput. 59, 210–222 (2017) 7. Chen, G.: Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints. Appl. Soft Comput. 50, 58–70 (2017) 8. Naderi, E.: A novel fuzzy adaptive configuration of particle swarm optimization to solve largescale optimal reactive power dispatch. Appl. Soft Comput. 53, 441–456 (2017) 9. Heidari, AA.: Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Appl. Soft Comput. 57, 657–671 (2017) 10. Morgan, M.: Benchmark studies on optimal reactive power dispatch (ORPD) based multiobjective evolutionary programming (MOEP) using mutation based on adaptive mutation adapter (AMO) and polynomial mutation operator (PMO). J. Electr. Syst. 12–1 (2016) 11. Ng, Rebecca.: Ant Lion optimizer for optimal reactive power dispatch solution. J. Electr. Syst. (AMPE2015), 68–74 (2016) 12. Anbarasan, P.: Optimal reactive power dispatch problem solved by symbiotic organism search algorithm. Innov. Power Adv. Comput. Technol. (2017) 13. Gagliano, A.: Analysis of the performances of electric energy storage in residential applications. Int. J. Heat Technol. 35,(1), S41–S48 (2017) 14. Caldera, M.: Survey-based analysis of the electrical energy demand in Italian households. Math. Model. Eng. Probl. 5(3), 217–224 (2018) 15. Basu, M.: Quasi-oppositional differential evolution for optimal reactive power dispatch. Electr. Power Energy Syst. 78, 29–40 (2016) 16. Yamada, T.: The leaf development process and its significance for reducing self-shading of a tropical pioneer tree species. Oecologia 125(4), 476–482 (2000) 17. Honda, H.: Tree branch angle: maximizing effective leaf area. Science 199(4331), 888–890 (1978) 18. IEEE.: The IEEE test systems. http://www.ee.washington.edu/trsearch/pstca/ (1993) 19. Hussain, AN.: Modified particle swarm optimization for solution of reactive power dispatch. Res. J. Appl. Sci. Eng. Tech. 15(8), 316–327 (2018)

Actuator Fault Detection and Isolation for PEM Fuel Cell Systems Using Unknown Input Observers Vikash Sinha

and Sharifuddin Mondal

Nomenclature Afc AT CD CP DW F I st J M R T V W nd P V X  ω

Active fuel cell area (cm2 ) Valve opening area (m2 ) Throttle discharge coefficient Specific heat (J kg−1 K−1 ) Membrane diffusion coefficient (cm2 /s) Faraday’s number (Coulombs) Stack current (A) Rotational inertia (kg m2 ) Molecular mass (kg/mol) Gas constant or electrical resistance () Temperature (K) Volume (m3 ) Mass flow rate (kg/s) Electro-osmotic drag coefficient Pressure (Pa) Vapour or voltage (V) Mass fraction or system state vector Relative humidity Rotational speed (rad/s)

V. Sinha (B) · S. Mondal Department of Mechanical Engineering, National Institute of Technology, Patna, Bihar 800005, India e-mail: [email protected] S. Mondal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_63

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Subscripts A An Atm Ca Cm Cp Fc In Membr Out Sm Rm

Air Anode Atmospheric Cathode Compressor motor Compressor fuel cell Inlet Across membrane Outlet Supply manifold Return manifold

1 Introduction Proton exchange membrane fuel cell systems (PEMFCSs) use an electro-chemical process to directly convert chemical energy into electrical energy. These devices find applications in power grids, vehicles, electronics, etc. Being non-linear complex systems, abrupt parameter changes are tough to identify in these systems. These are used for mobile and stationary power [1] and very useful in distributed and backup generation and automobiles. The power generation devices and less emission vehicles have made PEMFCSs alluring due to their quick start and high power density [1]. In spite of many literatures on PEMFCS modelling, only a few exist for their control and observation. The model transition phase includes the compressor and manifolds flow and inertia dynamics with membrane humidity affecting the fuel cell efficiency, voltage as well as power [2, 3]. The measurement of all the system signal inputs is not possible and so for such systems, an observer can be used as a state estimator if unknown inputs are present. The significance of unknown input observers in advanced control engineering is immense. Researchers have developed unknown input observers (UIOs) for different linear and non-linear systems [4–12]. From [4], several estimators have been designed for the linear as well as non-linear systems [5–12]. Darouach et al. [9] presented a Luenberger observer for the linear systems having unknown inputs. A linear parameter varying (LPV) model-based fault detection and isolation (FDI) technique for PEMFCS is illustrated in [10]. The model-based FDI is based on comparing the online behaviour of the PEMFCS using actuators with an estimate from its mathematical model. If a significant error occurs between the outputs and the signals of the actuator, a fault is detected [11]. If this type of fault occurs in a system, the overall effect on the system is like having an extra control input, which may make the system partially uncontrollable.

Actuator Fault Detection and Isolation for PEM …

675

In this work, the UIO based actuator FDI technique for PEMFCS is presented. A number of UIOs are designed and then residuals are calculated. A residual, in this context, is defined as the difference between the plant outputs and the observer predicted outputs. Furthermore, an FDI algorithm is formulated based on residuals. Then, the FDI step is carried out in such a way that the first fault is detected with the isolation of faulty zone and second the faulty parameter is isolated. This FDI technique is advantageous because the second step is carried out only when a fault occurs in a single actuator in order to reduce the fault isolation problem [11]. The effectiveness of the proposed scheme and its online implementation are shown with simulated results. In Sect. 2, a general PEMFCS model is presented. The FDI algorithm using UIOs assuming single actuator fault scenario is derived in Sect. 3. The simulated results are shown in Sect. 4 with some concluding remarks in Sect. 5.

2 PEMFCS Model This work presents a PEMFCS model including the major sub-systems shown in Fig. 1 assuming constant stack temperature. This assumption holds true because it varies slowly in comparison to the transition dynamics of the developed model. Another assumption is that the temperature and humidity are controlled by perfectly designed cooling and humidification sub-systems. Here, the modified linear PEMFCS model [3] which includes air compressor, hydrogen tank and supply and return manifold is used. These contain cathode and anode channel between which an electrolyte exists inside the PEMFCS as shown in Fig. 1 [3]. The basic working with proton flow through the electrolyte is explained in detail in our previous work

Fig. 1 PEMFCS model [3]

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[1]. The development and validation of the mathematical model used here has been done by Sinha, and Mondal [1], Pukrushpan et al. [2]. The PEMFCS includes the cathode and anode flow model as follows.

2.1 Cathode Flow Model The air compressor, the supply manifold and the cathode dynamics are included in this model. The following equations are used to describe the cathode flow model. Equation (1) represents a lumped model used for the dynamic behaviour of the compressor. Equation (2) represents the supply manifold model including pipe and stack manifold. Equations (3)–(5) give the states as oxygen (O), nitrogen (N) and vapour partial pressures, respectively. dωcp c p Tatm K cp kt kv kt = −ηcm ωcp + ηcm vcm − dt Rcm Jcp Rcm Jcp ηcp



psm patm

γ −1/γ

 −1 Φ (1)

γ Ra Tcp γ Ra Tsm ksm,out γ Ra Tsm ksm,out d psm,ca =− psm + kcp Φωcp + pca (2) dt Vsm Vsm Vsm    R O Tca x O,in kca,in d pO x O,out kca,out  po + p N + pv,ca =− + dt Vca 1 + ωca,in 1 + ωca,out Ro Tca xo2,in kca,in n R O Tca x O,out kca,out R O Tca Ist (3) + psm + prm − MO Vca 1 + ωca,in Vca 1 + ωca,out Vca 4F    d pN R N Tca 1 − x O,in kca,1 (1 − x O,out )kca,out  =− + po + p N + pv,ca dt Vca 1 + ωca,in 1 + ωca,out R N Tca (1 − x O,out )kca,out R N Tca (1 − x O,in )kca,1 psm + prm Vca 1 + ωca,in Vca 1 + ωca,out    Rv,ca Tca ωca,in kca,1 d pv,ca ωca,out kca,out  p O + p N + pv,ca =− + dt Vca 1 + ωca,in 1 + ωca,out Rv,ca Tca ωca,in kca,1 Rv,ca Tca ωca,out kca,out + psm + prm Vca 1 + ωca,in Vca 1 + ωca,out   Rv,ca Tca + Mv n 1 + 2 A f c n d Ist + Vca 2F +

(4)

(5)

Actuator Fault Detection and Isolation for PEM …

677

2.2 Anode Flow Model This model is identical to cathode flow model with an assumption that hydrogen gas in pure form is supplied. The following equations are used to describe the anode flow model. Equation (6) represents the anode supply manifold model. Equations (7) and (8) denote the states as the hydrogen (H) and vapour partial pressure followed by (9) showing the return manifold model.    R H Tan R H Tan k1 k1 + k H,out pH − + k H,out pv,an 1 + ωan,in Van 1 + ωan,in Van  R H Tan  n R H Tan Ist + k H,out MH p O + p N + pv,ca − Van Van 2F R H Tan k1 + psm,an 1 + ωan,in Van

d pH = − dt



  R H Tan k1 dp H = − + k H,out pH dt 1 + ωan,in Van   R H Tan k1 − + k H,out pv,an 1 + ωan,in Van  R H Tan  p O + p N + pv,ca + k H,out Van n R H Tan k1 R H Tan Ist + MH psm,an − Van 2F 1 + ωan,in Van   Rv,an Tan ωan,in k1 d pv,an = − + kv,an,out pv,an dt 1 + ωan,in Van   Rv,an Tan ωan,in k1 − + kv,an,out pH 1 + ωan,in Van   Rv,an Tan + kv,an,out pv,an p O + p N + pv,ca Van Rv,an Tan Mv A f c n d n ωan,in k1 Rv,an Tan Ist psm,an − + 1 + ωan,in Van Van F   Ra Tr m kca,out dpr m,an C D,r m A T,r mξr m = − pr m + √ dt Vr m RTr m  Ra Tr m kca,out  p O + p N + pv,ca + Vr m where the model parameters and their values are given in the Appendix.

(6)

(7)

(8)

(9)

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3 FDI Algorithm Using UIOs The voltage generated in the stack depends on the cathode and anode pressures thus affecting efficiency as well as power. It is a tedious job to measure these parameters directly [2]. UIOs give a solution to this problem with the assumptions that components and sensors are fault-free. The observed parameters are needed to design a faulttolerant controller. A method of UIO design for PEMFCS is presented in Sect. 3.1. Then, an actuator FDI algorithm is proposed in Sect. 3.2.

3.1 UIO for PEMFCS Let us consider a PEMFCS x(t) ˙ = Ax(t) + Bu(t) + Dw(t)

(10)

y(t) = C x(t)

(11)

where x(t)ε R n ; u(t)ε R m ; w(t)ε R q ; y(t)ε R p are state, input, unknown input and output vectors, respectively, with A, B, C and D being the matrices of known suitable dimensions. The system also satisfies rank(C D) = rank(D)

(12)

Now UIO of (10) and (11) is given as z˙ (t) = N z(t) + L y(t) + Gu(t)

(13)

xˆ = z(t) − E y(t)

(14)

ˆ R n is the dummy and the estimated state vector, respectively, where z(t)ε R n ; x(t)ε with N, L, G and E being the observer matrices to be calculated. The error signal of the state is calculated as e(t) = x(t) − x(t) ˆ = x(t) − z(t) + E y(t)

(15)

then the error dynamics is e˙ = N e + (P A − N P − LC)x + (P B − G)u + P Dv With

(16)

Actuator Fault Detection and Isolation for PEM …

679

P = In + EC

(17)

P D = 0 or (In + EC)D = 0

(18)

P B − G = 0 or G = P B

(19)

P A − N P − LC = 0

(20)

gives e˙ = N e

(21)

  E = −D(C D)+ + Y I p − (C D)(C D)+

(22)

If

Using (18),

where (C D)+ is general inverse of CE and Y is the arbitrary matrix. Using (22) in (17) gives P. The spectral norm of P is assumed to be nonzero (13) is written in the Luenberger form with gain matrix K as N = P A − KC

(23)

K = L + NE

(24)

z˙ = (P A − K C)z + L y + Gu

(25)

e˙ = (P A − K C)e

(26)

Thus, (23) satisfies (20) if

Then, (13) and (16) becomes

The observer gain is calculated using theorem and lemmas presented in [11] here omitted.

3.2 FDI Algorithm Consider again the system described in Sect. 3.1. x(t) ˙ = Ax(t) + Bu(t)

(27)

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y(t) = C x(t)

(28)

If a fault happens in the jth actuator then the fault included system is represented as x(t) ˙ = Ax(t) + Bu(t) + Bu j (t)

(29)

where u j (t) is the fault signal of the jth actuator; j = 1, 2, 3, …, m, (29) can be rewritten as x(t) ˙ = Ax(t) + Bu(t) + D j w j (t)

(30)

where wj (t) is the unknown input signal with Dj satisfying D j w j (t) = Bu j (t)

(31)

As (30) and (28) are sufficient to design an UIO [11], the designed observer estimates the states xˆ j (t) giving the residuals below. r j (t) = y j (t) − yˆ j (t) = y j (t) − C xˆ j (t)

(32)

4 Simulation Results The proposed algorithm is implemented on a PEMFCS [3]. The system model to derive UIO can be written in state-space description using Eqs. (3)–(5) as follows: x(t) ˙ = Ax(t) + Bu(t) + Bu j (t)

(33)

y(t) = C x(t)

(34)

 



where x T = p O p N pv,ca and u T = vcm psm Ist denotes the different states and actuator input signals, respectively. The state matrices and vectors using PEMFCS model parameters given in Table 2 are as follows: ⎡

⎤ −22.961 −22.961 −22.961 A = ⎣ −46.493 −46.493 −46.493 ⎦ −0.3295 −0.3295 −0.3295

Actuator Fault Detection and Isolation for PEM …

681

Table 1 Decision table Residuals Previous obs. r j > εj Decisions

Remarks

r2

Check r 1 and r 3

r1

r3

No

Fault may be in actuator 2

r 2 < ε2

No

Fault may be in actuator 1 or Check r 1 and r 3 actuator 3

r 2 < ε2

Yes

Fault is in actuator 1

r 2 > ε1

No

Fault is in actuator 2

Fault is detected and isolated

r 2 > ε2

Yes

Fault is in actuator 3

Fault is detected and isolated

Fault is detected and isolated

⎡

⎤ 367.5 7.739 −289.668 ⎦ B = ⎣ 367.5 29.104 0 367.5 0.118 −942.225 Now, a fault, which is a step input increment of half the actual magnitude, is introduced in the actuator 1 at t = 50 s. The FDI step is carried out. Three UIOs are designed for the actuator 1 with the matrices D1 = [1, 0, 0]T ; D2 = [0, 1, 0]T ; D3 = [0, 0, 1]T and C 1 = [1, 0, 0; 0, 0, 1]; C 2 = [1, 0, 1; 0, 1, 0]; C 3 = [1, 0, 0; 0, 0, 1]. Table 1 is drawn as shown below to isolate the faulty actuator. It can be interpreted from the table that fault is in actuator 1. The state residuals r 1 , r 2 and r 3 are plotted in Fig. 2. To get the faulty signal, appropriate threshold values εj = [0.1, 0.2]T are chosen. The threshold values depend on input signals, noise and uncertainties [8, 13] and are chosen according to the state error in fault-free scenario. Hence, care must be taken to choose the threshold values. As r 2 stays within ε2 while r 1 and r 3 cross ε1 and ε3 confirms the occurrence of fault (Table 1).

5 Conclusions An UIO based fault diagnosis technique for PEMFCS is presented. The algorithm is based on the assumption of single actuator fault scenario. A number of UIOs are designed and residuals are determined. Then an FDI algorithm is formulated based on the residuals. The efficacy of the proposed UIO based FDI algorithm is shown with simulated results of the cathode pressure model of PEMFCS.

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Fig. 2 State residuals Table 2 PEMFCS model parameters Symbol

Variable

Value

ρ m, dry

Membrane dry density

0.002 kg/cm3

M m, dry

Membrane dry equivalent weight

1.1 kg/mol

tm

Membrane thickness

0.01275 cm

n

Number of cells in stack

381

Afc

Fuel cell active area

280 cm2

dc

Compressor diameter

0.2286 m

J cp

Compressor and motor inertia

5 × 10−5 kg m2

V an

Anode volume

0.005 m3

V ca

Cathode volume

0.01 m3

V sm

Supply manifold volume

0.02 m3

V rm

Return manifold volume

0.005 m3 (continued)

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Table 2 (continued) Symbol

Variable

Value

C D, rm

Return manifold throttle discharge coefficient

0.01240

AT, rm

Return manifold throttle area

0.002 m2

k sm, out

Supply manifold outlet orifice constant

0.3629 × 10−5 kg /(s Pa)

k ca, out

Cathode outlet orifice constant

0.2177 × 10−5 kg /(s Pa)

kv

Motor electric constant

0.0153 V/(rad/s)

kt

Motor torque constant

0.0153 N-m/A

Rcm

Compressor motor circuit resistance

0.816 

ηcm

Compressor motor efficiency

98%

References 1. Sinha, V., Mondal, S.: Recent development on performance modelling and fault diagnosis of fuel cell systems. Int. J. Dyn. Control. 6(2), 511–528 (2018) 2. Pukrushpan, J.T., Peng, H., Stefanopoulou, A.G.: Control-oriented modelling and analysis for automotive fuel cell systems. Trans. ASME J. Dyn. Syst. Meas. Control. 126(1), 14–25 (2004) 3. Kim, E.-S., Kim, C.-J., Eom, K.-S.: Nonlinear observer design for PEM fuel cell systems. In: International Conference on Electrical Machines and Systems, Seoul, Korea, pp. 1835–1839 (2007) 4. Luenberger, D.G.: An introduction to observers. IEEE Trans. Autom. Control. AC 16(6), 596– 602 (1971) 5. Rajamani, R., Ganguli, A.: Actuator fault diagnosis for a class of non-linear systems using linear matrix inequalities. Int. J. Control 77(10), 920–930 (2004) 6. Yang, Q., Autocue, A., Bouamama B.O.: Model based fault detection and isolation of PEM fuel cell. In: Conference on Control and Fault Tolerant Systems, Nice, France, pp. 825–830 (2010) 7. Wang, C., Nehrir, M.H., Gao, H.: Control of PEM fuel cell distributed generation systems. IEEE Trans. Energy Convers. 21(2), 586–595 (2006) 8. Frank, P.M.: On-line fault detection in uncertain nonlinear systems using diagnosis observers: a survey. Int. J. Syst. Sci. 25(12), 2155–2166 (1994) 9. Darouach, M., Zasadzinski, M., Xu, S.J.: Full order observers for linear systems with unknown inputs. IEEE Trans. Autom. Control 39(3), 606–609 (1994) 10. de Lira, S., Vicenc, P., Quevedo, J.: Fault detection and isolation of a real PEM fuel cell using interval LPV Observers. In: 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS) Mexico City, Mexico, pp. 90–95 (2012) 11. Mondal, S., Chakraborty, G., Bhattacharyya, K.: Robust unknown input observer for nonlinear systems and its application in fault detection and isolation. Trans. ASME J. Dyn. Syst. Meas. Control. 130(4), 0445031–0445035 (2008) 12. Wang, D., Lum, K.-Y.: Adaptive unknown input observer approach for aircraft actuator fault detection and isolation. Int. J. Adapt. Control Signal Process. 21(1), 31–48 (2007) 13. Zhong, M., Ding, S.X., Lam, J., Wang, H.: An LMI approach to design robust fault detection filter for uncertain LTI systems. Automatica 39(3), 543–550 (2003)

Analysis of Heating and Cooling Energy Demand of School Buildings Tshewang Lhendup , Samten Lhendup, and Hideaki Ohgaki

1 Introduction Bhutan, situated at the foothills of the Himalayas has an altitude ranging from 100 to 7800 m above mean sea level. The country over the years has observed a major change in the field of education where access to free and quality education is seen as one of the critical steps to achieving Gross National Happiness. Bhutan today has 512 schools [1] out of which more than 100 are located in the temperate climate and few in the alpine region. The school academic session operates for a total of nine months annually and at least five months of the academic year falls in the cold period in a temperate climate and hot period in a tropical climate. Most of these schools do not have an adequate heating and cooling system either in the classrooms or in the hostels. In a temperate climate, the school children are subjected to the cold climate which not only affects their learning in the classroom but also their health. For example, more than 240 students in Thimphu, Paro and Wangdue were reported to be suffering from chilblain which is caused due to prolonged exposure to cold weather [2]. Un-insulated buildings and high infiltration further aggravate the problem. The schools located in the tropical region are subjected to a hot and humid climate for more than 5 months annually. These schools use ceiling fans for cooling but are not effective due to high temperature and humidity. Thus, there is a need for architectural changes in the construction of school buildings to improve its thermal T. Lhendup (B) Centre for Renewable and Sustainable Energy Development, College of Science and Technology, Royal University of Bhutan, Rinchending, Bhutan e-mail: [email protected] S. Lhendup Jigme Namgyel Engineering College, Royal University of Bhutan, Dewathang, Bhutan H. Ohgaki Institute of Advanced Energy, Kyoto University, Uji, Kyoto, Japan © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_64

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performance using locally available materials and technology. One of the ways to minimise heating and cooling energy demand is to add insulation in the external walls or ceiling and roof. Therefore, the goal of this paper is to analyse heating and cooling energy demand and study the impact of adding insulation. The financial analysis is not included in this paper.

2 Methodology 2.1 Building Description The construction of school buildings in Bhutan has been standardised and implemented by the Ministry of Education. It follows a uniform design and is mostly constructed using the same materials irrespective of the location and climate. As per the school design drawings, school buildings in Bhutan are of three categories, 4classrooms (4CR), 6-classrooms (6CR) and 12-classrooms (12CR) [3]. 4CR and 6CR are double-storeyed building with each room having a net lettable area of 48.3 m2 . 12CR is a three-storeyed building with rooms having a net lettable area of 43.5– 48.4 m2 . Figures 1, 2 and 3 show the floor plan of three buildings used for simulation in this paper. As of date, there are no specific regulations and guidelines for an energy-efficient school building construction. Bhutan green building guidelines don’t specifically mention school buildings [4]. School buildings in a temperate climate are constructed with stone masonry walls with concrete slabs and timber floors. The buildings in a tropical climate are also the same design and walls but with the mosaic floors. Three Fig. 1 Floor plan of building 4CR

Fig. 2 Floor plan of building 6CR

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Fig. 3 Floor plan of building 12CR

Table 1 Wall thermal transmittance (W/m2 K)

Climate

External wall

Floor

Ceiling

Temperate

1.996

2.222

2.532

Tropical

1.996

4.106

2.532

buildings were modelled in TRNSYS, a transient simulation software with a modular structure. The buildings were modelled with physical parameters from the building drawings. Standard building material thermal properties were used for simulation. The thermal properties of the walls and floor are presented in Table 1. Windows are made of timber frame with 4 mm single glazing. The ratio of the window frame to glazing is 0.48 which is relatively high. This is due to the traditional window design which has large frames and other supporting structures. The average window to wall ratio is 0.19 and orientation was assumed North-South for all three building models.

2.2 Schedules The energy required to heat and cool school building depends on the heating and cooling seasons, weekly and daily occupancy schedules. The school academic session starts in February and ends by mid-December. In general, school session starts at 8.15 a.m. and finishes at 5 p.m. For simulation, it was assumed that the heating and cooling system will be switched ON at 8 a.m. Saturday is a half-day session. Heating and cooling seasons were determined based on the actual usage in two climate regions from the authors’ experience. The heating season in a temperate climate is from February to April and from September to December. Whereas cooling season in a tropical climate is from April to mid-October.

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2.3 Weather To ensure the simulated results represent near to actual heating and cooling energy demand of school buildings, four regions, Tang, Thimphu, Panbang and Phuentsholing (PLing) were selected based on the availability of measured time series weather data. The weather data for Tang, Thimphu and Panbang was obtained from National Centre for Hydrology and Meteorology [5] and Phuentsholing from College of Science and Technology [6]. Tang and Thimphu are located in temperate regions while Panbang and PLing are located in tropical regions. The Latitude, Longitude and elevation of four locations are presented in Table 2. Tang and Thimphu have similar weather with an average temperature of 10.2 °C and 11.4 °C, respectively. Similarly, Panbang and PLing have similar weather with hot and humid summer. The average temperature of Panbang and PLing is 22 °C and 23.8 °C, respectively.

3 Results and Discussion Three buildings were simulated in TRNSYS 17 using measured time series weather data as input. Table 3 shows the baseline setting used in the simulation. The heating setpoint was assumed to be set at 18 °C and cooling at 24 °C. Building model in TRNSYS assumes that there are unlimited heating and cooling energy source available in the model to maintain a specified temperature [7]. Thus, heating energy demand is the amount of energy required to maintain the classroom temperature at a minimum of 18 °C during the heating period. Similarly, cooling energy demand is Table 2 Latitude, longitude and elevation

Location

33

17

Longitude

Elevation (m)

27°

27° 36 23 N

89° 56 43 E

2300

Panbang

26° 51 59 N

90° 58 59 E

300

26°

59

N

90°

9

Thimphu

50

N

48

Tang

PLing

Table 3 Baseline setting

Latitude

89°

23

E

48

E

2800

360

Heating setpoint

18 °C

Cooling set pint

24 °C

Infiltration

1 ACH

Internal heat gain from lighting

5 W/m2

Heat gain from occupants (seated, very light writing)

120 W/person

No of occupants

30 persons

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the amount of energy required to maintain the classroom temperature at a maximum of 24 °C during the cooling period. The model does not take into account the type of heating and cooling equipment used. Infiltration was assumed as 1ACH based on the findings of Jentsch et al. [8]. Internal heat gain from lighting is 5 W/m2 based on the number of light points in the classroom. The average class size in Bhutan is 30 students. Heat gain from each occupant was considered at 120 W per person based on ISO7730 [9]. It is noteworthy that many students normally occupy school buildings for a short period, and from Monday to Friday. Therefore, the results may vary when compared to a regular residential house which is occupied by a few people mostly throughout the day and night. Heating and cooling energy demand of a building is not only influenced by the building envelope characteristics but is also sensitive to changes in global irradiation, temperature, humidity, wind speed, wind direction, cloud cover and occupant behaviour. Therefore, the results presented in this paper are true for the assumed condition and weather data only.

3.1 Total Heating and Cooling Energy Demand The total annual heating energy demand in Tang is 4.648 kWh, 4.778 kWh and 11.636 kWh for 4CR, 6CR and 12CR, respectively (Table 4). Similarly, it is 4.070 kWh, 4.003 kWh and 9.676 kWh in Thimphu. The variation in number is due to the size of the buildings and it varies logically. As summarised in Table 5, the total annual cooling energy demand in Panbang is 6.368 kWh, 12.712 kWh and 20.554 kWh for 4CR, 6CR and 12CR, respectively. Similarly, it is 5.782 kWh, 11.860 kWh and 18.787 kWh in PLing. The specific heating energy demand varies from 16.5 to 24.1 kWh/m2 and cooling from 29.9 to 43.9 kWh/m2 . A similar study in Iran reported that average annual energy consumption in school buildings is 160 kWh/m2 [10]. Table 4 Summary of heating energy

Tang

Thimphu

4CR

Total energy (kWh)

Annual specific energy demand (kWh/m2 )

Peak load (kW)

4.648

24.1

21.0

6CR

4.778

16.5

25.9

12CR

11.363

20.8

53.6

4CR

4.070

21.1

22.4

6CR

4.003

13.8

30.9

12CR

9.676

17.7

62.1

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Table 5 Summary of cooling energy

Panbang

PLing

Total energy (kWh)

Annual specific energy demand (kWh/m2 )

Peak load (kW)

4CR

6.368

33.0

14.6

6CR

12.712

43.9

17.3

12CR

20.554

37.0

37.1

4CR

5.782

29.9

14.9

6CR

11.860

40.9

18.3

12CR

18.787

34.3

38.2

Pereira et al. [11] reported a comprehensive review of energy consumption of schools in developed countries. Im and Haberl [12] present a review of highperformance schools in terms of energy efficiency in the United States. These papers discuss the total energy consumption and not exclusively heating and cooling energy demand. Therefore, it is not worthwhile to draw comparisons with the current study. The average energy consumption in the case of the current study is much lower than reported by the above authors as it doesn’t account for the energy needed for other applications such as lighting, hot water and computers. There is a difference in the specific heating and cooling energy demand of four buildings for a given weather input. This could be attributed to the floor plan of the buildings. For example, 4CR has two rooms each on each floor where all four walls are exposed to the external weather. 6CR has three rooms on each floor and only 8 walls out of 12 are exposed to external weather. Similarly, three walls of two rooms and two walls of the other two rooms of 12CR are exposed to external weather. Thus, 4CR has the highest specific heating energy demand and 6CR the lowest. Conversely, the room that is least exposed to external weather will have the highest cooling load. Thus, 4CR has the lowest specific cooling load and 6CR the highest. The results are quite consistent in both the climates. Based on the simulation, the building shape 4CR and 6CR results in the least cooling and heating energy demand in the tropical and temperate climate, respectively. Therefore, building shape 4CR is best suitable in the tropical climate and 6CR in the temperate climate.

3.2 Impact of Insulation on Heating Energy Building 12CR has the highest total annual heating and cooling energy demand among the three buildings. Therefore, the impact of insulation was explored only on building 12CR. In order to analyse the impact of insulation on heating and cooling energy demand, four options were investigated by modifying the ceiling, walls and infiltration.

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15000 Cool

Total

12000 Heat

Energy demand (kWh)

Energy demand (kWh)

Heat 12000 9000 6000 3000 0

Base case

Ceiling

Wall

Ceiling and Ceiling, wall wall and ventilation

Cool

Total

9000

6000

3000

0

Base case

Ceiling

Wall

Ceiling and Ceiling, wall wall and ventilation

Fig. 4 Variation of heating energy demand in a Tang and b Thimphu

The first option is to add a 100 mm mineral wool on the ceiling. Mineral wool has a low thermal conductivity at 0.16 kJ/h mK. This results in a decrease in overall ceiling thermal transmittance (U-value) from 2.532 to 0.316 W/m2 K. Figure 4 shows the heating energy demand of the building 12CR in Tang and Thimphu, respectively. Addition of 100 mm mineral wool on the ceiling has the least impact on the total heating energy demand at 1.3 and 7.2% in Tang and Thimphu, respectively. This is because of the ceiling and the roof mainly affects the top floor of the building. In the current study, the heating demand is only during the day time when there is good sunshine. The top floor rooms would gain more heat without insulation. It is also noteworthy that this is only true for school buildings as the heating demand and sunshine hours coincide. For residential and commercial buildings, it may differ based on its heating and cooling load characteristics. The second option is to add a 100 mm mineral wool on the external wall. This results in a decrease in overall external wall U-value from 1.996 to 0.306 W/m2 K. This has a significant impact on total heating energy demand. As shown in Fig. 4, the total heating energy demand has reduced by more than 50% in both Tang and Thimphu. This is similar to the findings of Yousefi et al. [13]. The authors reported that by adding 5 cm polystyrene to walls could save heating loads up to 42%. It is noteworthy that while there is a reduction in heating energy demand, this has increased the cooling energy demand by more than 5 times in both locations. In fact cooling energy demand exceeds that of heating for Thimphu. The increase in cooling energy demand is more in Thimphu than in Tang due to the higher ambient air temperature in Thimphu during the summer months compared to Tang. The increase in cooling energy demand due to wall insulation can be attributed to the building occupancy characteristics. Most of the cooling load is due to the heat gain from the occupants which coincides with the sunshine hours. Due to the wall insulation, heat transfer from inside the rooms to the ambient is restricted. Therefore, more energy is required to maintain thermal comfort in the building. This leads to an increase in cooling energy demand. Thus, the addition of insulation in the walls with the intent to reduce the heating energy demand of school building needs further strategies.

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R2

R3

R4

R1

Energy (kWh)

R2

R3

R4

Energy (kWh) 1000

210

161

298

L3

L2

9

0

0

0

L2

L1

5

0

0.23

5

R1

R2

R3

R4

L1

L3

774

858

637

989

L3

L2

192

134

40

95

L2

800.0

320.0

240.0

160.0

80.00

L1

131

96

182

373

R1

R2

R3

R4

0.000

L1

Building Level

361

Building Level

L3

Building Level

Building Level

400.0

600.0

400.0

200.0

0.000

Fig. 5 The cooling energy demand of building 12CR in Tang a without insulation, b with insulation

The third option is by adding 100 mm mineral wool on both the ceiling and the external walls. This has an almost similar impact as that of the second option. The heating energy demand is decreased at the expense of an increase in cooling energy demand. Figure 5 shows the cooling energy demand of each room in building 12CR in Tang. It can be seen from Fig. 5a the cooling energy demand of building 12CR without insulation is less than 1100 kWh and occurs only on the top floor. However, when insulation is added to the ceiling and walls of the building, all rooms of the building need cooling. Thus, the cooling energy demand increases from less than 1100 kWh without insulation to more than 4400 kWh with insulation. The impact is also similar in Thimphu. The fourth option is similar to the third option but with the addition of ventilation in summer to reduce the cooling energy demand. By adding 3 ACH ventilation, there is a potential to reduce the heating energy demand by 54 and 57% in Tang and Thimphu without substantial increase in cooling energy demand.

3.3 Impact of Insulation on Cooling Energy Same options as in Tang and Thimphu were explored in Panbang and PLing. By adding a 100 mm insulation on the ceiling, there is a potential to reduce cooling energy demand by more than 7% in both the locations. The other three options have a negative impact on the cooling energy demand. By placing the insulation on the wall, the cooling energy demand is increased by 72 and 78% in Panbang and PLing as shown in Fig. 6. Zomorodian and Nasrollah [10] also reported that adding wall insulation has no tangible impact on the cooling energy demand in the hot and dry climate of Iran. Similarly, Kim and Moon [14] also concluded that wall insulation has minimal impact on cooling energy consumption. Figure 7 shows the cooling energy demand of each room in building 12CR in Panbang. It can be seen from Fig. 7a the cooling energy demand of building 12CR without insulation is less than 20.500 kWh which increases to more than 39.100 kWh with insulation on ceiling and walls as shown in Fig. 7b. Unlike in temperate climate,

Analysis of Heating and Cooling Energy Demand of School Buildings Heat

Cool

40000 30000 20000 10000 0

50000

Total

Heat

Energy demand (kWh)

Energy demand (kWh)

50000

Base case

Ceiling

Wall

693 Cool

Total

40000 30000 20000 10000 0

Ceiling and Ceiling, wall wall and ventilation

Base case

Ceiling

Wall

Ceiling and Ceiling, wall wall and ventilation

Fig. 6 Variation of cooling energy demand in a Panbang and b PLing R2

R3

R4

R1

Energy (kWh)

2592

2349

2262

2599

L3

L2

1472

1090

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1196

L2

L1

1605

1250

1440

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R1

R2

R3

R4

L1

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1583

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R2

R3

R4

Energy (kWh) 4000

Building Level

L3

Building Level

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2600

L3

3739

3786

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L2

3037

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L2

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3231

3471

R1

R2

R3

R4

905.0

L1

3600

Building Level

R1

3200

2800

2400

2000

Fig. 7 The cooling energy demand of building 12CR in Panbang a without insulation, b with insulation

adding ventilation during the building occupancy period further increases the cooling energy demand as shown in Fig. 6. Increasing outside airflow into the rooms in cooling dominated load increases the cooling load which is also reported by other authors. For example, Abanomi and Jones [15] reported that by reducing infiltration from 3 to 0.5 ACH results in 66 and 88.9% reduction in heating and cooling energy demand.

4 Conclusions The purpose of this paper was to analyse the impact of adding insulation on heating and cooling energy demand of school buildings. The buildings were simulated with the transient simulation program TRNSYS. From the simulation, the minimum annual specific heating energy demand is 13.8 kWh/m2 for 6CR and maximum is 24.1 kWh/m2 for 4CR. The minimum annual specific cooling energy demand is 29.9 kWh/m2 for 4CR and maximum is 43.9 kWh/m2 for 6CR. While the addition of insulation in the external wall was found to have a positive impact on the heating energy demand, it substantially increased cooling energy demand. The net decrease in thermal energy is not significant. It can be concluded that the building

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floor plan 4CR is the best option in a tropical climate and 6CR in a temperate climate as these buildings require the least cooling and heating energy. The findings indicate that classrooms having most walls exposed to external weather has the least cooling energy demand. Conversely, the classrooms with minimum exposure to external weather has least heating energy demand. The results presented in this paper do not include the effects of different insulation thickness, heating and cooling temperature settings, infiltration and orientation of buildings which will be the focus of future research.

References 1. Policy and Planning Division: Ministry of Education, Royal Government of Bhutan. Annual Education Statistics (2018) 2. Wangdi, T.: Chilblain Outbreak in Thimphu, Paro and Wangdue schools. In: Kuensel (ed.). Kuensel, Thimphu (2017) 3. SPBD: School Building Drawing. In: Dorji, C. (ed.) School Planning and Building Division, Ministry of Education. Royal Government of Bhutan, Thimphu (2018) 4. Department of Engineering Services: Bhutan Green Building Guidelines. Ministry of Works and Human Settlements, Thimphu, Bhutan (2013) 5. NCHM: Weather Data. In: Lhendup, S. (ed.) Thimphu: National Centre for Hydrology and Meteorology, Royal Government of Bhutan (2018) 6. CST: Weather Data for Phuentsholing. In: Lhendup, T. (ed.) Phuentsholing: College of Science and Technology, Royal University of Bhutan (2018) 7. Klein, S.A., et al.: TRNSYS 17 a TRaNsient System Simulation Program. Solar Energy Laboratory, University of Wisconsin-Madison (2017) 8. Jentsch, F. M., et al.: Field study of the building physics properties of common building types in the inner himalayan valleys of Bhutan. Energy Sustain. Dev. (38), 48–66 (2017) 9. ISO Standard 7730. Moderate thermal environments— determination of the PMV and PPD indices and specification of the conditions for thermal comfort (1995) 10. Zomorodian, Z.S., Nasrollahi, F.: Architectural design optimization of school buildings for reduction of energy demand in hot and dry climates of Iran. International J. Architect. Eng. Urban Plann. 23(1 & 2), 41–50 (2013) 11. Pereira, L.D., Raimondo, D., Corgnati, S.P., Silva, M.G.: Energy consumption in schools—A reviewpaper. Renew. Sustain. Energy Rev. 40(2014), 911–922 (2014) 12. Im, P., Haberl, J.S.: A survey of high performance schools. In: Symposium on Improving Building Systems in Hot and Humid Climates, Orlando, Florida (2006) 13. Yousefi, Y., Yousefi, S., Yousefi, Y.: Energy efficiency in educational buildings in Iran: Analysis and measures. In: Presented at the International Building Performance Simulation Association, Hyderabad, India. 7–9 Dec (2015) 14. Kim, J.J., Moon, J.W.: Impact of insulation on building energy consumption. In: Presented at the Eleventh International IBPSA Conference, Glasgow, Scotland. 27–30 July (2009) 15. Abanomi, W., Jones, P.: Passive cooling and energy conservation design strategies of school buildings in hot, arid region: Riyadh, Saudi Arabia. In: Passive and Low Energy Cooling for the Built Environment, Santorini, Greece (2005)

Thermodynamic Performance Analysis of Adsorption Cooling and Resorption Heating System Using Ammoniated Halide Salts Rakesh Sharma, K. Sarath Babu, and E. Anil Kumar

1 Introduction The solid–gas sorption reaction process can be used for efficient and long term thermal energy storage. Generally the adsorption of gas by solid releases thermal energy, whereas it is needed to be supplied for desorption of gas. The ammonia adsorption and desorption reactions with halide salts produce cooling effect and these reactions have been studied earlier [1]. Halide salt and ammonia gas combination are suitable for heat production, heat upgradation and cold production at a wide range of temperatures [2]. The advantage of using halide salts and ammonia pair is the zero ODP and GWP. In spite of that, it is having a disadvantage of low COP. Several researches have proposed advanced solid sorption systems for improving the performance of sorption systems [1–3]. The advanced sorption systems are based on the exchange of mass or thermal energy or both between the halide salt beds to enable the heat recovery between the salt beds. Many experimental and theoretical studies have been conducted on advanced sorption systems using halide salts and ammonia [4, 5]. But these studies are done considering the same thermodynamic properties during ammonia adsorption and desorption. However, there exists a pressure hysteresis and hence the properties are different for ammonia adsorption and desorption processes [6, 7]. In earlier works, it is assumed that the adsorption capacity of salt bed is constant throughout the cycle but in actual case it varies with temperature. The adsorption capacity variation with the temperature is incorporated in the present analysis. In this work, different categories of halide salts and ammonia tanks are coupled to achieve both resorption and

R. Sharma Indian Institute of Technology Indore, Indore 453552, India K. Sarath Babu · E. Anil Kumar (B) Indian Institute of Technology Tirupati, Tirupati 517506, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_65

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adsorption processes among them to produce heating and cooling effects simultaneously. The performance analysis of the proposed ACRHS is evaluated using the measured P-T relations and estimated thermodynamic properties.

2 Equilibrium P-T Lines of Ammonia Adsorption/Desorption of Halide Salts The measurement of ammonia sorption properties of chosen halide salts has been done in our earlier works [8, 9]. The adsorption pressures of CaCl2 and SrCl2 at room temperature are nearly equal but higher than that of MnCl2 and FeCl2 which have approximately equal adsorption pressure at room temperature. Moreover, the above salts are incapable to release ammonia at room temperature. The ammonia is completely desorbed at minimum desorption temperatures of 80, 90, 120 and 150 °C, from CaCl2 , SrCl2 , MnCl2 and FeCl2 , respectively. Whereas NaBr can easily desorb ammonia at temperatures lower than room temperature. Thus, the halide salts are classified according to their ammonia desorption temperature. MnCl2 and FeCl2 are classified into high-temperature salts (HTS), CaCl2 and SrCl2 as medium temperature salt (MTS) and NaBr as low-temperature salt (LTS). Figure 1 represents the pressure–temperature equilibrium lines of ammonia adsorption/desorption of halide salts. Hysteresis has been observed during ammonia adsorption/desorption process due to these two separate pressure–temperature equilibrium lines (one for adsorption and other for desorption). The van’t Hoff plots are used to determine ammonia adsorption/desorption reaction enthalpies.

Fig. 1 Pressure temperature equilibrium lines of ammonia adsorption/desorption of halide salts

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3 Adsorption Cooling and Resorption Heating System The ACRHS is an advanced solid sorption system that produces both heating and cooling effect using single heat input. The schematic of such an advanced system is given in Fig. 2. The ACRHS consists of each pair of HTS and MTS reactors and two ammonia tanks which act as either evaporator or condenser. A single cycle of ACRHS consists of two half-cycles where multiple effects (cooling in evaporator and heating in HTS) are simultaneously obtained in each half cycle. From Fig. 2, it is observed that the useful cooling effect (QC ) is produced in evaporator1 in the first half cycle by the evaporation of ammonia. The ammonia vapour from evaporator1 is adsorbed by the HTS1 and releases heat of adsorption (QA ). Heat recovery is established between HTS1 and MTS1 through heat recovery process by supplying heat of adsorption (QA ) from HTS1 to MTS1 . MTS1 utilizes the recovery heat and releases the ammonia which is simultaneously condensed in the condenser2 . Meanwhile, ammonia is desorbed from HTS2 using heat energy from the available heat source at heat source temperature (T H ). Ammonia vapour from HTS2 is adsorbed by MTS2 . Adsorption of ammonia by MTS2 , releases adsorption heat as a useful heating effect (QH ). The cooling effect in the first half cycle is produced in evaporator1 and heating effect is produced in MTS2 . Following the first half cycle, MTS1 and HTS1 are sensibly heated and HTS2 and MTS2 are sensibly cooled to change the working operation of each salt reactor. In the

Fig. 2 Schematic diagram of adsorption cooling and resorption heating system (ACRHS)

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next half cycle, the reactor which earlier adsorbed ammonia undergoes desorption and vice versa. One more cooling effect (QC ) is produced in evaporator2 by the evaporation of ammonia. The ammonia vapour release from the evaporator2 is adsorbed by the HTS2 releases the adsorption heat (QA ). Recovery of heat is observed between HTS2 and MTS2 , i.e. adsorption heat of HTS2 is supplied to MTS2 . At the same time, heat (QHS ) from the available heat source is supplied to HTS1 to desorb ammonia which is eventually adsorbed by MTS1 to produce useful heating effect (QH ). After completion of the first full cycle, both the reactors HTS1 and MTS1 are sensibly cooled and HTS2 and MTS2 are sensibly heated to repeat the cycle. In ACRHS, one cooling effect and one heating effects are produced adsorption and resorption, respectively, by consuming single heat input at once in each half cycle.

4 Thermodynamic Analysis The thermodynamic cycle of ACRHS is constructed using the pressure–temperature equilibrium lines of halide salts during adsorption and desorption of ammonia. The energy analysis of the systems has been done and the performance is evaluated using measured thermodynamic properties.

4.1 Thermodynamic Cycle of ACRHS Figure 3 shows the thermodynamic cycle of ACRHS. Evaporation of ammonia in the evoparator1 produces cooling effect at T C . The ammonia is then adsorbed by HTS1 (Process 1–2), releases the heat of adsorption (QA ) at T A . During the heat recovery process (Process 2–5), ammonia is desorbed from the MTS1 by consuming recovery heat (QHR ) at T REC and is condensed in the condenser2 (Process 5–6). Heat recovery is established between HTS1 (MnCl2 ) and MTS1 (CaCl2 ). At the same time, HTS2 (MnCl2 ) desorbs ammonia (process 3–4) by taking heat input (QHS ) at T HT from external heat source and ammonia is adsorbed by MTS2 (CaCl2 ) at T H releasing useful heat (QH ). Processes 1–6, 2–3 and 5–4, represents sensible heating of NH3 tank1 , HTS1 and MTS1 from temperatures T C to T AMB , T A to T HT and T REC to T H , respectively, while the processes 6–1, 3–2, and 4–5 represents the sensible cooling of NH3 tank2 , HTS2 and MTS2 from temperatures T AMB to T C , T HT to T A and T H to TREC . When the salt beds attain the new temperatures, the second half cycle of ACRHS will begin. In second half cycle, each process is repeated but with different sets of NH3 tank or salt reactors. Cooling effect is produced by evaporator2 (process 1–2), while heat recovery process (2–5) is achieved between HTS2 (MnCl2 ) and MTS2 (CaCl2 ) and MTS2 desorbs ammonia to condenser1 through process 5–6. Heating effect is produced in the process 3–4 by MTS1 (CaCl2 ). For the next cycle, the system has to come to its initial state by sensible heating of NH3 tank2 , HTS2

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Fig. 3 Thermodynamic cycle of adsorption cooling and resorption heating system (ACRHS)

and MTS2 through processes 1–6, 2–3 and 5–4, respectively, and sensible cooling of NH3 tank1 , HTS1 and MTS1 through processes 6–1, 3–2 and 4–5, respectively.

4.2 Energy Analysis The energy analysis of ACRHS includes the estimation of useful cooling effect, useful heating effect and performance of the system. The useful cooling effect (QC ) is produced by evaporation of ammonia in evaporator by consuming latent heat of evaporation, useful heating effect is produced by adsorption of ammonia in MTS by releasing heat of adsorption. Equation 1 represents the cooling load of ACRHS. Q C,ACRHS = n · Hv,NH3 − [{(m · C p )NH3 + (m · C p ) R,NH3 }(TAMB − TC )]

(1)

where n · Hv,NH3 = total   latent heat  of evaporation of ammonia     m · C p NH3 + m · C p R,NH3 (TAMB − TC ) = sensible heat load Equation 2 represents the useful heating effect (QH ) produced due to adsorption of ammonia by MTS in Adsorption Cooling and Resorption Heating System (ACRHS). Q H = n · Ha,MTS − [{(m · C p )MTS + (m · C p )NH3 + (m · C p ) R,MTS }(TH − TREC )]

(2)

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where n · Ha,MTS = ammonia adsorption heat    of MTS and      m · C p MTS + m · C p NH3 + m · C p R,MTS (TH − TREC ) = Sensible heat load. Only once heat is supplied (QHS ) to high-temperature salt in ACRHS is given by Eq. 3 Q H S = n · Hd,HTS + [{(m · C p )HTS + (m · C p )NH3 + (m · C p ) R,HTS }(THT − T A )] (3) Equations 4 and 5 represents adsorption heat of HTS and recovery of MTS, respectively Q A = n · Ha,HTS + [{(m · C p )HTS + (m · C p )NH3 + (m · C p ) R,HTS }(TH T − T A )] (4) Q H R = n · Hd,MTS + [{(m · C p )MTS + (m · C p )NH3 + (m · C p ) R,MTS }(TH − TREC )]

(5)

The performance of ACRHS is defined in terms overall coefficient of performance (COPoverall ) which considers both the cooling and heating effect and is given by Eq. 6. COPoverall,ACRHS =

Q C,ACRHS + Q H QHS

(6)

It is assumed that the ambient and cooling temperatures are 35 °C and 10 °C, respectively. The mass ratio is defined as the ratio of masses of reactor to the halide salt, which is assumed to be 6 [10]. It is also assumed that the amount of ammonia adsorbed is completely desorbed from material in isothermal conditions. Although the thermodynamic cycle is shown only for the combination of NH3 –CaCl2 –MnCl2 , the thermodynamic analysis is performed using combinations NH3 –SrCl2 –MnCl2 , NH3 –CaCl2 –FeCl2 and NH3 –SrCl2 –FeCl2 for ACRHS. Table 1 shows the values of thermodynamic properties of halide salts used in the present analysis. The adsorption temperature of HTS (MnCl2 or FeCl2 ) is selected based on the desorption temperature of MTS (CaCl2 or SrCl2 ). This is because of desorption heat Table 1 Halide salt’s thermodynamic properties Halide salt

Adsorption enthalpy, ΔH a (kJ mol−1 )

Desorption enthalpy, ΔH d (kJ mol−1 )

Specific heat, C p (kJ kg−1 K−1 )

Specific heat of reactor (stainless steel), C p,R (kJ kg−1 K−1 )

CaCl2

−53.16

56.59

0.65

0.5

SrCl2

−54.32

66.60

0.12

0.5

MnCl2

−60.17

65.62

0.61

0.5

FeCl2

−66.87

92.98

0.31

0.5

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of MTS is taken from the HTS. Desorption temperature of MTS is selected such that desorption pressure must be more than the saturation pressure of ammonia so that the ammonia can easily transfer from MTS to condenser/evaporator. At 35 °C, the saturation pressure of ammonia is 13.62 bar. The minimum temperatures at which ammonia is completely desorbed from CaCl2 and SrCl2 are observed from the measured PCIs as 80 °C and 90 °C, respectively, corresponding to which the equilibrium desorption pressures are 6.77 bar and 11.85 bar, respectively, which are not adequate to transfer ammonia to NH3 tank. The desorption temperature of MTS (CaCl2 or SrCl2 ) is chosen as 100 °C to transfer ammonia from MTS to condenser/evaporator. The corresponding equilibrium pressures of CaCl2 and SrCl2 are 19.03 bar and 21.42 bar, respectively. Thus, the adsorption temperature of HTS is set at 110 °C to facilitate the heat recovery process between HTS and MTS to desorb ammonia from MTS at 100 °C. Due to monovariant behaviour of ammoniated halide salt, the temperature and pressure are interdependent on each other, which means that the equilibrium pressure increases by increasing the temperature and vice versa. So in order to obtain useful heating effect at high temperature, the adsorption pressure of MTS should be sufficiently high which depends upon the desorption pressure of HTS. To observe the useful heating effect at temperature of 120 °C which is higher than the temperature of heat release by HTS, i.e., 110 °C, the desorption temperature of HTS (MnCl2 and FeCl2 ) is estimated to be 210 °C. The solid sorption systems performance depends on the capacity of storage of ammonia in halide salt. It is assumed that the amount of ammonia is adsorbed by halide salt during adsorption is completely desorbed during desorption. The ammonia storage capacity of halide salts at different temperatures is estimated on the basis of their respective measured pressure–concentration isotherms. The adsorption capacities of MnCl2 and FeCl2 at 110 °C are assessed as 27 wt% and 20 wt%, respectively. The adsorption capacities of CaCl2 and SrCl2 are estimated at 120 °C as 30 and 32 wt%. Operating conditions and parameters for thermodynamic analysis of sorption systems are shown in Table 2. 10 ml of ammonia is taken as cycled quantity in analyzing the sorption system. The latent heat of evaporation and specific heat of ammonia are taken as 1370 kJ kg−1 and 4.73 kJ kg−1 K−1 , respectively.

5 Discussions of Results Obtained from Thermodynamic Analysis Figure 4 represents the heat liberated from HTS and desorption heat required by MTS during heat recovery process at different mass ratios. It is clear from Fig. 4 that the adsorption heat from the HTS is higher than the desorption heat required by the MTS. It indicates that the heat recovery between HTS and MTS is attainable. It is noticed that even though the adsorption enthalpy of MnCl2 is less than desorption

NH3 cyclic quantity (mol)

10

Sorption system

ACRHS

560

530

630

MnCl2 850

FeCl2

Mass of HTS (g)

CaCl2

SrCl2

Mass of MTS (g)

Table 2 Operating conditions and parameters for thermodynamic analysis of sorption systems

110

T A (°C)

100

T HR (°C)

120

T H (°C)

210

T HS (°C)

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Fig. 4 Adsorption heat released by HTS and desorption heat required by MTS during heat recovery process at different mass ratio

enthalpy of SrCl2 , total heat released during ammonia adsorption by MnCl2 is higher than heat required for ammonia desorption by SrCl2 due to the effect of sensible heat load. As the temperature swing of MnCl2 (210–110 °C) is more than that of SrCl2 (120–100 °C), hence during the heat recovery process the sensible heat extracted from MnCl2 is quite high. During the cooling phase in the basic adsorption cooling system, the ammonia is adsorbed by HTS and the adsorption enthalpy is rejected to the atmosphere. Whereas in ACRHS, the useful heating effect is also produced in addition to the cooling effect by utilizing the adsorption heat of HTS. Thus, the heat which is released to the atmosphere as waste is transformed into useful heat available at high temperature. For different combinations of salts utilized in ACRHS, the useful heating effect is observed by adsorption enthalpy of MTS (CaCl2 or SrCl2 ) at 120 °C when input heat is given to the system, i.e. HTS (MnCl2 or FeCl2 ) at 210 °C. Because of an additional heating effect is observed in ACRHS in comparison to the basic adsorption system, the performance of ACRHS is better than basic sorption systems. It is observed that under the same operating conditions, the system using MnCl2 as HTS has better performance in comparison to FeCl2 , which is mainly due to low desorption enthalpy and higher adsorption capacity of MnCl2 . Even though the adsorption enthalpy of MnCl2 and FeCl2 are quite comparable, the desorption enthalpy of FeCl2 is fairly high because of hysteresis. The variation in the performance of the ACRHS with respect to CaCl2 or SrCl2 is observed to be negligible because of approximately the same adsorption enthalpy and equivalent amount of respective salt required to adsorb specific amount of ammonia. Table 3 illustrates the performance values of ACRHS for different salt combinations while considering ratios of mass of reactor and material as 6. It is observed from Fig. 5 that the increase in the mass ratio decreases the performance of the ACRHS. Ammonia transfer rate between the beds can be increased by increasing the driving pressure difference. The driving pressure difference between the beds can be increased by increasing desorption temperature.

704 Table 3 Overall coefficient of performance of ACRHS for different salt combinations at mass ratio of 6

R. Sharma et al. Combination of salts

COPoverall

NH3 –CaCl2 –MnCl2

0.70

NH3 –SrCl2 –MnCl2

0.72

NH3 –CaCl2 –FeCl2

0.52

NH3 –SrCl2 –FeCl2

0.54

Fig. 5 Overall coefficient of performance of ACRHS for various combinations at different mass ratio

But increasing the desorption temperature increases the heat input to the system which eventually decreases the performance of the ACRHS. Decreasing the cooling temperature of evaporator can also decrease the ammonia transfer rate. This is because of a decrease in the driving pressure difference between the evaporator and HTS. By using multiple halide salt pairs, the performance of ACRHS can be optimized. Also, the sensible heat loss contributes much to the performance loss of the system, it is required to suitably optimize the size and mass of reactor. In addition to the development of multi-effect system, it is also necessary to enhance heat transfer [11, 12]

6 Conclusions In this work, configuration of gas–solid sorption system employing resorption and adsorption process is made to realize simultaneous heating and cooling effects. The thermodynamic analysis of Adsorption Cooling and Resorption Heating System (ACRHS), has been performed. It leads to the following conclusions. • Heat is rejected to the atmosphere as waste during the cooling phase in a basic adsorption cooling system, whereas, the ACRHS can provide an additional heating

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effect at high temperature along with the cooling effect utilizing the recovery of heat between HTS and MTS. • For different combinations of salts utilized in ACRHS, the useful heating effect is observed by adsorption enthalpy of MTS (CaCl2 or SrCl2 ) at 120 °C when input heat is given to the system i.e. HTS (MnCl2 or FeCl2 ) at 210 °C. • The overall COP of ACRHS for combination NH3 –SrCl2 –MnCl2 is observed to be as maximum as 0.72 for mass ratio of 6. • The COP of ACRHS is higher for MnCl2 based sorption systems. This is due to the higher ammonia adsorption capacity and lower desorption enthalpy of MnCl2 over FeCl2 . FeCl2 based systems have lower COP because of high desorption enthalpy, which causes higher heat input to the system.

References 1. Neveu, P., Castaing, J.: Solid-gas chemical heat pumps: Field of application and performance of the internal heat of reaction recovery process. Heat Recovery Syst. CHP 13(3), 233–251 (1993) 2. Spinner, B.: Ammonia-based thermochemical transformers. Heat Recovery Syst. CHP 13(4), 301–307 (1993) 3. Meunier, F.: Solid sorption heat powered cycles for cooling and heat pumping applications. Appl. Therm. Eng. 18(9–10), 715–729 (1998) 4. Lu, Y., Wang, Y., Bao, H., Yuan, Y., Wang, L.W., Roskilly, A.P.: Analysis of an optimal resorption cogeneration using mass and heat recovery processes. Appl. Energy 160(15), 892– 901 (2015) 5. Wang, L.W., Wang, R.Z., Lu, Z., Chen, C., Wu, J.: Comparison of the adsorption performance of compound adsorbent in a refrigeration cycle with and without mass recovery. Chem. Eng. Sci. 61(11), 3761–3770 (2006) 6. Zhong, Y., Critoph, R., Thorpe, R., Tamainot-Telto, Z., Aristov, Y.: Isothermal sorption characteristics of the BaCl2 –NH3 pair in a vermiculite host matrix. Appl. Therm. Eng. 27(14–15), 2455–2462 (2007) 7. Trudel, J., Hosatte, S., Ternan, M.: Solid-gas equilibrium in chemical heat pumps: the NH3 – CoCl2 system. Appl. Therm. Eng. 19(5), 495–511 (1999) 8. Sharma, R., Anil Kumar, E.: Measurement of thermodynamic properties of ammoniated salts and thermodynamic simulation of resorption cooling system. Int. J. Refrig 67, 54–68 (2016) 9. Sharma, R., Anil Kumar, E.: Performance evaluation of simple and heat recovery adsorption cooling system using measured NH3 sorption characteristics of halide salts. Appl. Therm. Eng. 119, 459–471 (2017) 10. Jiang, L., Wang, R.Z., Wang, L.W., Roskilly, A.P.: Investigation on an innovative resorption system for seasonal thermal energy storage. Energy Convers. Manag. 149, 129–139 (2017) 11. Fujioka, K., Hatanaka, K., Hirata, Y.: Composite reactants of calcium chloride combined with functional carbon materials for chemical heat pumps. Appl. Therm. Eng. 28(4), 304–310 (2008) 12. Mauran, S., Prades, P., L’Haridon, F.: Heat and mass transfer in consolidated reacting beds for thermochemical systems. Heat Recovery Syst. CHP 13(4), 315–319 (1993)

Correlating Partial Shading and Operating Conditions to the Performance of PV Panels S. Gairola, M. K. Sharma, and J. Bhattacharya

1 Introduction Photovoltaic (PV) systems generate electricity upon exposure to the sunlight that is directly proportional to the number of photons received. Partial shading over the panel surface reduces the power output of the panel as well as contributes to the temperature loss. Partially shaded panels are exposed to the lower amount of sunlight with either uniform or non-uniform distribution depending on the coverage pattern of shade. The power loss due to a reduction in the exposed area of the solar panel is termed as shading loss. Previous literature reveals that the current from the whole panel is limited by the solar cell with the lowest current in the entire circuit [1]. In such a case, shading of even a small portion of the solar panel drastically reduces the overall power output of the panel. On the other hand, shading material absorbs sunlight which is completely converted to heat as opposed to the brighter portions of the panel where only a fraction of the solar energy contributes to the heat generation. Therefore, the shaded portions are more heated than the illuminated part which leads to local hotspots that may increase the average temperature of panel by 15 °C. This increment in temperature reduces the power output of the panel that is termed as temperature loss. Temperature loss can be predicted with the temperature coefficient which represents the change in power output of the solar panel per unit rise in temperature. Therefore, a lower magnitude of temperature coefficient is desirable for better performance of solar panels. Most panels have their temperature coefficient between −0.2 and −0.5%/°C [2]. Different solutions are being applied to reduce shading and temperature losses. However, it is sensible to first estimate the percentage of various losses and implement solutions accordingly. The quantitative estimation of various losses will help S. Gairola · M. K. Sharma · J. Bhattacharya (B) Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_66

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design solution strategies while minimizing the price of solar panel installations. In the current study, we perform a set of experiments where the different loss generating factors towards the partially shaded panels are carefully separated and thereby isolate the contributions due to the shading loss, temperature loss, and mismatch loss. We also estimate the contribution of shading and miscellaneous loss in the overall loss, which helps in designing and implementing solution strategies judiciously. Further, we try to generate empirical formulae to formulate predictive tools for the enumeration of the losses which can potentially improve the accuracy of estimating the operational efficiency under partially shaded conditions. The knowledge of the effects of shading and temperature on the overall performance of solar cells may help to design more efficient solar energy systems.

2 Methodology The solar panels are placed on the rooftop of the building. Each panel [3] is placed parallel to the east–west line in order to keep consistency during the experiment. Solar panels are kept horizontal. A pyranometer, which measures the global solar irradiance, is placed parallel to the panel surface to ensure an identical angle of incidence as that on the panel. We also develop code in the Lab VIEW software for measuring the voltage, current, power, and temperature of solar panels [4]. The current and voltage of the solar panel are measured with NI 9227 [5] and NI 9228 [6] modules, respectively, while the panel temperature is measured with K-type thermocouple [7] and NI 9213 module [8]. Data stored in modules are acquired through National Instruments Data Acquisition hardware (NI DAQ) [9]. In order to calculate various losses, we create several different setups of solar panels with respect to calculate shading loss, temperature loss, and mismatch loss.

2.1 Calculation of Shading and Miscellaneous Losses We consider four panels as panel 1, panel 2, panel 3 and panel 4 and calibrate them. We place the panels on the rooftop of the Northern Laboratories at Indian Institute of Technology, Kanpur. Panels 3 and 4 are covered with a glass sheet before the start of the experiment as shown in Fig. 1. We, therefore, term it as modified panel 3 and modified panel 4. Panel 1 is kept clean and dust-free. To study the effect of shading, panel 2 and modified panel 3 are covered with paper chips. Each panel is connected to a 35 W tungsten light bulb. NI DAQ records the voltage and current in each circuit. LabVIEW software is used for data acquisition. An algorithm developed in LabVIEW calculates the power output of the panel. The experiment is repeated for varying paper chip size.

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Fig. 1 Setup for shading and miscellaneous losses

Power consumed by load 1, load 2, load 3, and load 4 in this setup are P1, P2 , P3 , and P4 , respectively. The difference between P2 and P2 gives shading and miscellaneous loss. The difference between P4 and P4 gives power loss due to the glass. The difference between P3 and P3 provides shading loss. The miscellaneous losses are obtained by subtracting P2 and shading loss from P1. The experiment is repeated for different shading configurations. 

2.2 Calculation of Temperature Loss A hollow acrylic flow chamber is fabricated with inlet and outlet valves at the diagonals. A groove is made on the upper face of the chamber to fit copper plate into it. The copper plate is used because of high thermal conductivity that is able to maintain at flowing fluid temperature. Oil bath circulator pumps the fluid (oil) at constant set temperature through the pipe that controls through the Julabo software [10]. Solar PV cells are kept at the copper surface under irradiation and connected with NI DAQ across its terminals to find the open-circuit voltage (Voc) and short circuit current (Isc) of the cell. We set the cell temperature at 25 °C and record the power output of the cell at intensities of 120 W/m2 , 240 W/m2 , 360 W/m2 , respectively. Further, oil temperature increases in steps of 5 °C until the cell reaches a temperature of 60 °C. Now, we know the power output of the cell at different temperatures and a particular intensity. The difference in power output gives the temperature loss at that intensity and is calculated from Eq. 1. (TL)T,I = (CP)25,I − (CP)T,I

(1)

where (TL)T,I is temperature loss of cell at temperature T °C and irradiation I, (CP)25,I is power output of a cell at temperature 25 °C and at irradiation I, and (CP)T,I is power output of cell at temperature T °C and at irradiation I.

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2.3 Calculation of Mismatch Loss We calculate the mismatch loss with the same flow chamber that is used in calculation of temperature loss. We calculate the Voc and Isc of cell A and cell B for intensities of 120 W/m2 , 240 W/m2 , 360 W/m2 , respectively, at 25 °C. Now, both these cells (cell A and cell B) are connected in series to make a new cell (cell C). Voc and Isc of cell C are recorded for the same intensities and at temperature of 25 °C. Mismatch loss is, then, calculated at a particular intensity with Eq. 2. (ML)T,I = (CP) A,T,I + (CP) B,T,I − (CP)C,T,I

(2)

where (ML)T,I is Mismatch loss for combination of cells at particular temperature and intensity, (CP) A,T,I is power output of cell A at particular temperature and intensity, (CP) B,T,I is power output of cell B at a particular temperature and intensity and (CP)C,T,I is power output of cell C at particular temperature and intensity.

3 Results and Discussions The efficiency of the solar panel depends critically on the shading pattern of the solar panels and the increased temperature of the panel surface due to this shade. The present study examines the effect of shading and the temperature on the power output of the solar panel. The study investigates the relation between panel output and the varying partial shading of the solar panels. Not only the shade but also the shading distribution pattern over the solar panels can significantly alter the shading and temperature losses. Therefore, different paper distribution configuration over the solar panel is used to investigate these effects. We explain the results for the set of experiments performed using different methodologies in which different loss generating factors towards the partially shaded solar panels are carefully separated and hence isolate the contributions due to the shading loss, temperature loss, and mismatch loss. We also generate empirical formulae to formulate predictive tools for the enumeration of losses.

3.1 Shading Loss In the present study, total loss is the sum of the shading loss and the miscellaneous loss which includes temperature and mismatch losses. Table 1 shows the variation of total loss with the alteration of the paper chip distribution pattern. We observe that for the fixed covered area of the panel surface, total loss decreases as the paper chip distribution over the panel becomes more uniform for a fixed intensity of 728 W/m2 . We also observe that the relative contribution of the shading loss is more than the

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Table 1 Variation of different losses with paper distribution at an intensity of 728 W/m2 Paper distribution

Panel power (W)

Total loss (W)

Shading loss (W)

Miscellaneous loss (W)

Shading loss % in total loss

Miscellaneous loss % in total loss

Less uniform 25.3

9.29

5.45

3.84

59

41

Uniform

25.3

2.09

1.52

0.57

73

27

More uniform

25.3

1.26

1.15

0.11

91

09

Fig. 2 Variation of total loss percentage with intensity

miscellaneous loss in the total loss for any particular shadow distribution. Therefore, the shading loss is the dominant component of the total loss. In addition to that, we can observe that the dominance of the shading loss over the miscellaneous loss increases as the shadow distribution becomes more uniformly spaced. Figure 2 shows that the percentage of total loss in power output decreases with intensity for a partial shading configuration. It is observed from Fig. 3 that shading loss is a dominant loss at any intensity. We also observe that the percentage of shading loss decreases with an increase in irradiance over the solar panel while the miscellaneous loss contributes approximately 3% of the naked panel power output. Therefore, we can conclude (from Fig. 3) that shading loss is the dominant factor in total loss for any shaded configuration.

3.2 Miscellaneous Loss Next dominant loss is miscellaneous loss that is the summation of mismatch and temperature losses. This attributes to differences in current of individual cells connected in series. Figure 4 shows that mismatch loss increases as the intensity over the cells increases. This increment is ascribed to the higher difference in current with higher intensity.

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Fig. 3 Variation of shading and miscellaneous loss percentage with intensity

Fig. 4 Variation of mismatch loss with intensity for a pair of cell

Next component of miscellaneous loss is temperature loss. The temperature loss is calculated from Eq. 1 and shown in Fig. 5. We observe that the temperature loss increase with the increase in intensity as well as with the increase in temperature. Therefore, during the daytime, when both temperature and intensity over the solar panel increase, an increase in temperature loss is observed.

3.3 Correlation for Temperature Loss Previous literature reveals that the current varies exponentially with the temperature of the solar cell. [11] We measure the current output of the cell with varying temperatures. Figure 6 shows the variation of current with temperature for different intensities. Indeed, we find an exponential dependence of current on temperature.

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Fig. 5 Temperature loss variation with intensity at different solar cell temperatures

Fig. 6 Variation of current (j) with temperature (T ) for different intensities

In regression analysis, we obtain the value coefficient of regression (r 2 ) close to 1 which signifies the exponential curve fits the data points perfectly. As clear from the observed trends, the pre-exponential factor changes with the intensity of light. However, the exponential part remains the same. Thus, a general expression for the current as a function of intensity and temperature can be written as in Eq. (3): j = f 1 (I ) · e0.003T

(3)

Now, we evaluate the pre-exponential function f 1 (I) for various intensities of light. The variation of f 1 (I) is shown in Table 2. Indeed, the variation of f 1 is marginal with temperature.

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Table 2 Variation of pre-exponential function f 1 (I ) with intensity of light for a wide temperature range Intensity (W/m2 )

f1 40 °C

45 °C

50 °C

55 °C

Average (f 1 )

120

0.0435

0.0435

0.0434

0.0433

0.0434

240

0.0669

0.0668

0.0664

0.0669

0.0668

360

0.1095

0.1089

0.1094

0.1096

0.1093

Furthermore, we assume the quadratic dependence of light intensity on the f 1 and solve the equation for different values of intensities as illustrated in Table 2. We find the correlation among solar cell current, cell temperature and light intensity as given in Eq. (4).   j = 6.69 × 10−7 I 2 − 4.645 × 10−5 I + 3.978 × 10−2 e0.003T

(4)

Similarly, previous literature also reveals that voltage varies linearly with the temperature of the solar cell. [11] Therefore, we measure the voltage output with temperature and plot in Fig. 7 that shows that the variation of voltage with temperature is linear and we obtain a relationship among voltage, current, and temperature as shown in Eq. 5. Now, we evaluate the function f 2 (I ) for various intensities of light as illustrated in Table 3. (5) v = −0.0016T + f 2 (I ) We observe that at particular intensity, function f 2 gives the same value irrespective of cell temperature which means f 2 is not a function of temperature. Therefore, we assume that f 2 is a quadratic function of light intensity. We, further, find the values of unknown coefficients using Table 3 and obtain the final correlation of cell voltage as given in Eq. 6. Fig. 7 Variation of voltage with temperature for different intensities

Correlating Partial Shading and Operating …

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Table 3 Variation of constant f 2 with intensity for different cell temperature Intensity (W/m2 )

f2 40 °C

45 °C

50 °C

55 °C

Average (f 2 )

120

0.5640

0.5648

0.5653

0.5655

0.5648

240

0.5794

0.5796

0.5795

0.5792

0.5794

360

0.5964

0.5972

0.5977

0.5968

0.5970

  v = −0.0016T + 1.052 × 10−7 ∗ I 2 + 8.375 × 10−5 ∗ I + 0.554

(6)

Furthermore, we find a correlation for cell power in Eq. (7) by multiplying the cell current and cell voltage as calculated in Eq. (4) and in Eq. (6), respectively. Equation (7) reveals that the power of cell is the function of intensity and cell temperature.    P(I, T ) = −0.0016(T − 273) + 1.052 × 10−7 I 2 + 8.375 × 10−5 I + 0.554   (7) × 6.69 × 10−7 I 2 − 4.645 × 10−5 I + 3.978 × 10−2 × e0.003T where P(I, T ) is the power output of cell at particular intensity and temperature (in Kelvin).

4 Conclusions Shadow over panel decreases the irradiation received by its surface. The shadow may be of nearby trees, buildings, dust, etc., decreases the irradiation received and as a result, the power output by the system also decreases. In addition to this power loss, shades render risk of hotspots in photovoltaic solar panels which can overheat and burn out the system. Following are the major findings of our study. For a fixed covered area of the panel surface, total loss decreases with an increase in irradiance over the solar panel. The shading loss is the major contributor to the total loss. Not only the fraction of the partial shading but also the shading pattern has a crucial bearing on the performance of the panel. For an equal fraction of the partial shading, the more uniform shade shows the reduction in total loss. We observe that shading loss varies from 60 to 90% of the total loss at 728 W/m2 due to changes in shading pattern. Mismatch in the cells is inherent. Two solar cells of the same power rating and under the same operational conditions can demonstrate variation in the performance up to 5%. This leads to a mismatch in generation characteristics like current and voltage output of a solar cell. The overall mismatch loss for a combination of cells is approximately 10% of the sum of the individual power output of the cells, which is significant. This loss, further, increases with an increase in intensity. It is validated with our experiments that current varies exponentially with the change in temperature and voltage varies linearly with the temperature of the solar

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cell. The voltage of the cell decreases with increase in temperature while output current slightly increases with temperature and the decrease in voltage dominates the increase in current, therefore, the power output of cell decreases with an increase in temperature. The temperature loss increases with the increase in intensity as well as with the increase in temperature. Therefore, during the daytime when both temperature and intensity over the solar panel increase, the temperature loss increases consistently. The temperature loss varies from 20 to 40% of the rated power. There are several solutions to minimize the negative effects of high temperatures which include installation of panels a few inches above the roof to allow convective air flow to cool the panels down. Panels can be constructed with light-colored materials to reduce heat absorption. Furthermore, predictive empirical formulae as obtained from the current thesis for the miscellaneous loss can be employed to judiciously utilizing the resources to circumvent the dominant losses.

References 1. Maghami, M.R., Hizam, H., Gomes, C., Radzi, M.A., Rezadad, M.I., Hajighorbani, S.: Power loss due to soiling on solar panel: a review. Renew. Sustain. Energy Rev. 59, 1307–1316 (2016) 2. Jacobson, M.Z., Delucchi, M.A.: Providing all global energy with wind, water, and solar power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials. Energy Policy 39(3), 1154–1169 (2011) 3. TATA Solar Panel Homepage. Available: https://www.amazon.in/Tata-Watt-Silicon-SolarPanel/dp/B01CE8X7Q4. Last accessed 2019/02/05 4. LabVIEW Homepage. Available: http://www.ni.com/download/labview-development-system2015/5314/en/. Last accessed 2019/02/05 5. NI 9227 Homepage. Available: http://www.ni.com/pdf/manuals/375101e.pdf. Last accessed 2019/02/05 6. NI 9228 Homepage. Available: http://www.ni.com/pdf/manuals/376502a_02.pdf. Last accessed 2019/02/05 7. Thermocouples Homepage. Available: https://www.omega.com/techref/pdf/z204-206.pdf. Last accessed 2019/02/05 8. NI 9213 Homepage. Available: http://www.ni.com/pdf/manuals/374916a_02.pdf. Last accessed 2019/02/05 9. NI DAQ 9185 Homepage. Available: http://www.ni.com/pdf/manuals/376610a.pdf. Last accessed 2019/02/05 10. Julabo oil bath Homepage. Available: https://www.julabo.com/en/products/wireless-commun ication-software/easytemp-software-products. Last accessed 2019/02/05 11. Chikate, B.V., Sadawarte, Y.A.: The factors affecting the performance of solar cell. Int. J. Comput. Appl. 975–8887 (2015)

Engineering of O2 Electrodes by Surface Modification for Corrosion Resistance in Zinc–Air Batteries Imran Karajagi, K. Ramya, Prakash C. Ghosh, A. Sarkar, and N. Rajalakshmi

1 Introduction Metal-air batteries are promising sustainable energy storage devices due to its use of free oxygen from the atmosphere. These batteries are advantageous over other types of batteries due to their better rechargeability, cost-effectiveness and safety [1–4]. The development of secondary zinc–air batteries is limited by their poor stability and durability [5]. A critical challenge to commercialize the secondary zinc–air batteries is to develop a stable, high performance and cost-effective oxygen electrode that requires low overpotentials for oxygen evolution and reduction reaction. The performance of rechargeable zinc–air batteries are majorly influenced by the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) [6]. Precious metal catalysts (e.g. Pt, Au and Ru) are proven for their excellent ORR and OER catalytic performance. However, due to their high cost, these noble metal-based catalysts are I. Karajagi · K. Ramya (B) · N. Rajalakshmi Centre for Fuel Cell Technology (CFCT), International Advanced Research Centre for Powder Metallurgy and New Materials (ARCI), IIT-M Research Park, 2nd Floor, Phase-1, 6, Kanagam Road, Taramani, Chennai 600113, India e-mail: [email protected] I. Karajagi e-mail: [email protected] I. Karajagi Centre for Research in Nanotechnology and Science (CRNTS), Indian Institute of Technology Bombay, Powai, Mumbai 400076, India P. C. Ghosh Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India A. Sarkar Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_67

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replaced by non-noble-based transition metal oxides as bifunctional catalysts [7]. The poor electrical conductivity of transition metal oxides is improved by the addition of conductive carbon as support material which also possesses high specific surface area and porosity. However, the carbon undergoes corrosion during charging by highly oxidative electrochemical potentials and by the oxygen species generated in OER [7–9]. This leads to electrode degradation resulting in poor stability [10]. In alkaline medium, carbon corrosion occurs through the following reaction [11, 12]: − C + 6OH− → CO2− 3 + 3H2 O + 4e

(1)

This carbon corrosion can be evidenced visually from the change in colour of electrolyte from transparent to brown during repeated charge–discharge cycles due to dissolved carbonate ions in the electrolyte [2]. This can change the porosity and hydrophobicity of the carbon support and thereby increasing the diffusion resistance of oxygen through the air electrode and also increase in electrical resistance [13]. This process will lead to the degradation in the performance of zinc–air battery as well as cyclic stability. Several studies have been reported that the carbon with high specific surface area are more susceptible to corrosion and are easily oxidized during OER [14, 15]. To alleviate the problem of carbon corrosion and low electrical conductivity, we have developed an oxygen electrode with highly conductive nickel metal layer between porous carbon support and catalyst layer. The interlayer of Ni between the catalyst and porous carbon support provides protection to the underlying carbon layer, an improved pathway for electron conduction thus increasing the specific activity and modification in surface roughness of porous support. To identify the performance and durability of developed corrosion resistive electrode, we have carried out the electrochemical evaluation of carbon-based air electrode for zinc–air battery.

2 Experimental 2.1 Preparation of Air Electrodes The preparation of electrode involves three steps: (i) Carbon coating on SS mesh: Carbon powder (15 mg) and 10 wt% PTFE was mixed and mechanically stirred in 1:2 ratio of ethyl alcohol and water for 2 h to form carbon slurry. The prepared slurry was brush coated on SS mesh and dried at room temperature. (ii) Electroless plating of Ni: 2.26 g of nickel sulphate heptahydrate (0.5 g Ni) was taken as precursor for electroless plating of Ni on the carbon-coated mesh. The precursor solution was stirred for 2 h continuously with 0.95 g of Sodium hypophosphite. The pH of the solution was adjusted to 8.5 using ammonia. The temperature of the solution was increased to 55 °C and then the carbon-coated mesh was immersed for various durations 1, 2 and 3 h. The samples were dried at room temperature and denoted as

Engineering of O2 Electrodes by Surface …

719

C-Ni-1 h, C-Ni-2 h and C-Ni-3 h, respectively, depending on the immersion time. (iii) Silver deposition: The Ni-coated electrodes were immersed in 30 ml of silver nitrate solution (1 ml of 0.04 M AgNO3 +1 ml of 0.04 M KOH+28 ml ethylene glycol) and were irradiated with microwaves with a power of 720 W for 1 min with 2 min pause intervals for three cycles. The samples were removed and dried overnight under vacuum oven at 50 °C.

2.2 Material Characterizations Physical characterization The XRD analysis of the sample was carried out using Rigaku-Smart Lab. The sample was scanned from 10° to 90° using Cu Kα radiation. Optical profilometer images were collected using Zeta 20 3D Optical Profiler (Zeta instruments). Electrochemical characterization Electrochemical characterizations were carried out using multichannel solartron analytical (1400 cell testation). The cyclic voltammogram, impedance and potentiodynamic curves were measured using a threeelectrode setup with platinum disc as counter, Ag/AgCl as reference and developed hybrid electrodes were used as working electrode in 0.1 M KOH electrolyte solution. Fabrication of Zinc–Air Battery (ZAB) and testing A zinc–air battery was fabricated with Zn as anode, silver reduced nickel-coated electrode as cathode and 6 M KOH as electrolyte. Performance test of ZABs was carried using solartron analytical (1400 cell testation).

3 Results and Discussion 3.1 Physical Characterization The presence of nickel and silver on carbon electrodes are confirmed from the XRD patterns of C-Ni and C-Ni-Ag electrodes as shown in Fig. 1a. The peak occurring at 26.1° corresponds to (002) plane of carbon. The peaks centred at about 43.5, 50.7 and 74.6° are corresponds to (111), (200) and (220) planes of Ni present in both C-Ni and C-Ni-Ag electrodes. The Ag peaks were observed at 38.4, 44.5, 64.7 and 77.7° corresponding to (111), (200), (220) and (311) planes in C-Ni-Ag electrode. Optical profilometer images for Ni-coated electrode were recorded and are shown in Fig. 1b, c. The thickness of 1, 2 and 3 h Ni coating were found to be around 98 μm, 173 μm and 231 μm, respectively. The Ni-coated electrodes had lower roughness of around 54 μ, 45 μ and 39 μ for 1 h, 2 h and 3 h immersion time, respectively, when compared to 59 μ for the bare carbon electrode.

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Fig. 1 a X-ray diffraction patterns of C-Ni and C-Ni-Ag electrodes. b Optical profilometry images of Ni-coated electrode. c Image showing the surface roughness and thickness of Ni coating

3.2 Electrochemical Performance Typical cyclic voltammogram of C, C-Ni-3 h and C-Ni-3 h-Ag electrodes in 0.1 M KOH solution, between the potential region of −0.2 and 1 V versus Ag/AgCl at 10 mVs−1 are shown in Fig. 2a. The redox behaviour of C, Ni and Ag can be clearly seen in the cyclic voltammogram, the peak at 0.3 V in C-Ni electrode corresponds to

(a) 4 0 -4

60

54%

40

-0.2

0.0

0.2

0.4

0.6

0.8

0

1.0

Potential E vs Ag/AgCl

32%

C-Ni-3h C-Ni-2h C-Ni-1h C

20

(c)

0

5000

(d)

10000

15000 Time (s)

20000

Z'' (ohms)

-3 -4 -5

C-Ni-1h C-Ni-2h C-Ni-3h

-6 -7

25000

C C-Ni-1h C-Ni-2h C-Ni-3h

-15

-2

Current density mA cm-2

82%

80

-8

-12

98%

100

Relative current (%)

Current density mA cm-2

(b)

C C-Ni-3h C-Ni-3h-Ag

8

-10

-5

0 -1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

E vs Ag/Agcl

0.0

0.2

0.4

0

10

20 Z' (ohms)

30

40

Fig. 2 a Cyclic voltammogram for C, C-Ni-1 h, C-Ni-2 h and C-Ni-3 h. b Electrochemical potentiostatic for C, C-Ni-1 h, C-Ni-2 h and C-Ni-3 h air electrode. c Electrochemical potentiodynamic for C, C-Ni-1 h, C-Ni-2 h and C-Ni-3 h air electrode. d Impedance spectroscopy for C, C-Ni-1 h, C-Ni-2 h and C-Ni-3 h

Engineering of O2 Electrodes by Surface …

721

activity of nickel. The anodic peak at 0.6 V and cathodic peak at 0.2 V corresponds to redox behaviour of Ag in C-Ni-Ag electrode. To explore the electrochemical corrosion of carbon, the potentiostatic experiment was carried out on bare carbon- and nickel-coated carbon electrode at the potential where oxygen evolution and carbon oxidation occur simultaneously. The current transient response of the carbon- and nickel-coated electrode at a fixed potential of 1.8 V versus RHE. The bare carbon electrode retains 32% of its original current density after 25,000 s of continuous operation. The Ni-coated electrodes retains 98, 82 and 54% of its original current density for C-Ni-3 h, C-Ni-2 h and C-Ni1 h, respectively. The electroless deposition of metal interlayer over porous carbon support leads to part occlusion of the pores of carbon. The electrode with 3 h nickel coating was proven to be stable for longer time in highly oxidative environment compare to 1 and 2 h electrodes as shown in Fig. 2b. Electrochemical potentiodynamic studies were carried out for the nickel-coated electrodes. The corrosion current and potential for 1, 2 and 3 h nickel-coated electrode were found to be I o = 0.000239 mA and E o = −0.715, I o = 0.000249 mA and E o = −0.505, I o = 0.000324 mA and E o = −0.354, respectively. As the thickness of metal layer increases from 1 to 3 h the corrosion rates were decreased as shown in Fig. 2c. The problem of carbon corrosion is mitigated by depositing a metal interlayer over the carbon support. This resulted in reduced surface roughness and porosity with significant improvement in specific activity. Electrochemical impedance spectroscopy for nickel-coated carbon electrodes was performed in the frequency range from 100 kHz to 100 MHz with 5 mV of AC signal amplitude. The formation of metal interlayer within porous carbon support provides additional pathway for electron conduction and lowered the charge transfer resistance at the electrocatalysts–electrolyte interface. The charge transfer resistance decreases as the Ni interlayer coating increases from 1 to 3 h due to its increase in conductivity. The C-Ni-3 h electrode had lowest charge transfer resistance compared to C-Ni-2 h, C-Ni-1 h and bare carbon as shown in Fig. 2d.

3.3 Zinc–Air Cell Assembly and Testing Electrodeposited zinc on mild steel mesh was used as anode electrode. Carboncoated stainless steel mesh interlayered with nickel coating between carbon support and catalyst was used as air electrode at the cathode. The active area of anode and cathode electrodes was 1 cm2 . For battery charge–discharge testing, 6 M KOH was used as electrolyte. Figure 3a shows the schematic illustration of zinc–air battery, which consists of zinc (anode) dipped in 6 M KOH + ZnO (electrolyte) and stainless steel mesh coated with prepared catalyst (air cathode) dipped in 6 M KOH and both the electrodes were separated by separator. Air was fed at the cathode side during discharging. The open-circuit potential was found to be around 1.40 V for all synthesized electrocatalysts.

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Fig. 3 a Zinc–air schematic. b Charge–discharge cycling of zinc–air battery with C-Ag and C-Ni3 h-Ag electrodes with current density of 10 mA cm−2

Rechargeable zinc–air batteries fabricated with the prepared electrodes and their performances are shown in Fig. 3b. The cycling durability of developed Ni interlayered (C-Ni-3 h-Ag) electrode in rechargeable zinc–air battery found to be stable over 500 cycles compared to bare carbon electrode which started degrading after 40 cycles of charge–discharge. The initial charge and discharge potential was found to be ~2.03 and ~1.26 V and these values were stable for initial cycles. The voltaic efficiency was obtained by dividing the discharge voltage by charge voltage and their initial values at 10th cycle were found to be 62%. However, after 500 cycles, the charge–discharge potential were found to be ~2.1 and ~1.24 V. Also, the voltaic efficiency at 500th cycle dropped to 59% for three hours of Ni-coated electrode. These results prove that the nickel modified electrode exhibits excellent charge–discharge performances in rechargeable zinc–air battery.

4 Conclusions The present work mitigates the problem of carbon corrosion and low electrical conductivity by depositing a metal interlayer. This resulted in reduced porosity and surface roughness with improved cyclic stability of ZABs. We attributed the increase in stability is mainly due to Ni interlayer which protects the carbon surface. The improved specific activity is due to the enhanced electrical conductivity of the electrode. Further optimization of Ni coating in terms of thickness exhibited high cyclic stability due to its low corrosion resistance, thereby avoiding the corrosion of carbon at higher oxidative potentials. By tuning the oxidation characteristic of catalyst overlayer through electrocatalyst-support interaction exhibited high activities for both the ORR and OER. The C-Ni-Ag electrode was incorporated as an O2 electrode in a zinc–air battery and demonstrated much higher stability over 500 charge–discharge

Engineering of O2 Electrodes by Surface …

723

cycles compared to an analogous device with no interlayer nickel between carbon support and catalyst. Acknowledgements The authors would like to thank Dr. G. Padmanabham, Director, ARCI, and Dr. R. Gopalan, Associate Director, ARCI, for their constant support and encouragement and Technical Research Centre (TRC) (AI/1/65/ARCI-2014), Department of Science and Technology, Government of India, for financial assistance. Imran Karajagi thanks ARCI fellowship under which this work was carried out.

References 1. Li, X., Xu, N., Li, H., Wang, M., Zhang, L., Qiao, J.: 3D hollow sphere Co3 O4 / MnO2 CNTs: its high-performance bi-functional cathode catalysis and application in rechargeable zinc–air battery. Green Energy Environ 2(3), 316–328 (2017) 2. Pang, H., Gu, P., Zheng, M., Zhao, Q., Xiao, X., Xue, X.H.: Rechargeable zinc–air battery: a promising way to green energy. J. Mater. Chem. A 5, 7651–7666 (2017) 3. Park, D.W., Kim, J.W., Lee, J.K., Lee, J.: Rechargeable zinc–air energy storage cells providing high power density. Appl. Chem. Eng. 23(4), 359–366 (2012) 4. Li, B., Ge, X., Goh, F.W.T., Hor, T.S.A., Geng, D., Du, G., Zong, Y.: Co3 O4 nanoparticles decorated carbon nanofiber mat as binder free air-cathode for high performance rechargeable zinc–air batteries. Nanoscale 7(5), 1830–1838 (2015) 5. Reddy, LD.: TB Handbooks of batteries. McGraw-Hill, NewYork (2001) 6. Li, P.C., Hu, C.C., You, T.H., Chen, P.Y.: Development and characterization of bi-functional air electrodes for rechargeable zinc–air batteries: effects of carbons. Carbon 111, 813–821 (2017) 7. Velraj, S., Zhu, J.H.: Cycle life limit of carbon-based electrodes for rechargeable metal–air battery application. J. Electroanal. Chem. 736, 76–82 (2015) 8. Lam, E., Luong, J.H.T.: Carbon materials as catalyst supports and catalysts in the transformation of biomass to fuels and chemicals. ACS Catal 4(10), 3393–3410 (2014) 9. Ross, P.N., Sattler, M.: The corrosion of carbon black anodes in alkaline electrolyte III. The effect of graphitization on the corrosion resistance of furnace blacks. J. Electrochem. Soc. 135(6), 1464–1470 (1988) 10. Wang, X., Li, W., Chen, Z., Waje, M., Yan, Y.: Durability investigation of carbon nanotube as catalyst support for proton exchange membrane fuel cell. J. Power Sources 158(1), 154–159 (2006) 11. Sumboja, A., Ge, X., Zheng, G., Goh, F.W.T., Hor, T.S.A., Zong, Y., Liu, Z.: Durable rechargeable zinc–air batteries with neutral electrolyte and manganese oxide catalyst. J. Power Sources 332, 330–336 (2016) 12. Kinoshita, K.: Electrochemical Oxygen Technology. Wiley, Hoboken (1992) 13. Koninck, M.D., Manseau, P., Marsan, B.: Preparation and characterization of Nb-doped TiO2 nanoparticles used as a conductive support for bifunctional CuCo2 O4 electrocatalyst. J. Electroanal. Chem. 611(1–2), 67–79 (2007) 14. Neburchilov, V., Wang, H., Martin, J.J., Qu, W.: A review on air cathodes for zinc–air fuel cells. J. Power Sources 195(5), 1271–1291 (2010) 15. Shepard, V.R., Smalley, Y.G., Bentz, R.D.: Bifunctional metal–air electrode. US Patent 5,306,579 (1994)

Energy Farming—A Green Solution for Indian Cement Industry Kapil Kukreja, Manoj Kumar Soni, B. N. Mohapatra, and Ashutosh Saxena

1 Introduction Cement sector in India is playing an important role in overall development and infrastructure. The cement industry has an irreplaceable position in the Indian economy. At present, Indian cement industry has the second position in terms of cement production and consumption, in the world after China. Currently, there are about 143 integrated large cement plants, 102 grinding unit and 62 mini cement plant [1]. Present government is more inclined towards infrastructure development, and the demand of cement is expected to be increased drastically in next five years with increased allocation to infrastructure projects in Union Budget 2018–19 [2]. Coal is the main fuel for the manufacture of cement in India, given the high cost and inadequate availability of oil and gas. The consumption of coal in dry process system ranges from 20 to 25% of clinker production. That means 0.20–0.25 tonne of coal is consumed to produce one tonne of clinker [3]. Another fuel required to operate the cement plant is diesel. It is required for drilling machine (in mines for blasting), for earth moving machines and in clinker production process for emergency power from diesel generator, kiln initial light up, various material handling vehicles, etc. Lot of research is being done to reduce coal consumption in cement plant by replacing the coal through alternative fuels like shredded tyre chips, plastic waste, refused derived fuel from MSW, agrowaste, etc. [4]. Research for reducing the energy consumption is also in advance stage where Bureau of Energy Efficiency (BEE) has made the scheme for Mandatory Energy Audit of cement plants [5]. Cement industry still has not focused on saving diesel consumption as the consumption of diesel is less as compared to main fuel (Coal). However, it is well K. Kukreja (B) · B. N. Mohapatra · A. Saxena National Council for Cement and Building Materials (NCB), Ballabgarh, Faridabad, India e-mail: [email protected] M. K. Soni Birla Institute of Technology and Science, Pilani, Jhunjhunu, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_68

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relevant to specify here the rise in diesel price in India in last five years is alarming for the cement industry. This paper highlights the saving in diesel cost by introducing energy farming (EF) concept in place of green belt area which is statutory requirement for obtaining environmental clearance for cement plant and mines area.

2 Land Requirement for Setting up a Cement Plant For setting up a cement plant, land is required, where the cement is being produced from the raw materials, and for limestone mining, where the limestone exploitation is being done for supply to the cement plant. Land requirement for mines, mainly, depends on various factors like plant capacity, limestone reserves in the selected mines, limestone quality, expected plant life, future expansions plan, etc. Table 1 indicates the tentative land area for some of the cement plants of various capacities. Land requirement for cement plant mainly depends on various factors like shape of land, equipment and technology selected, green area provision, future expansions plan, future energy saving project, etc. Table 2 indicates the tentative land area for some of the cement plants of various capacities: Table 1 Tentative land area for some of the limestone mines in India [6] S. No.

Plant name

Limestone production (MTPA)

Land allotted (ha)

1

M/s Lafarge India Pvt. Ltd. Tehsil-Nimbahera, District-Chittorgar, Rajasthan

2.60

602.00

2

M/s BMM Cements Limited at Gudipadu village, Yadiki Mandal, District-Anantapur

1.00

454.59

3

M/s Jaiprakash Associate Ltd., located at village-Thanghatia, Bihri, Jhopa, Kothari, Argat and Jigna, Tehsil-Ramnagar, District-Satna

1.20

363.07

4

M/s Ambuja Cements Ltd located at villages Nadikudi, Alugumallepadu, Gogulapadu, Tehsil-Dachepalli, Gurazala, District-Guntur

2.60

673.73

5

M/s Gujarat Sidhee Cement Limited located at village(s) Preshnavada and Morada, Tehsil-Sutrapada, District-Junagarh

1.53

253.85

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727

Table 2 Tentative land area for some of the cement plants of India [7] S. No.

Plant capacity (MTPA cement basis)

Land (ac)

Land (ha)

1

1.00

245–250

99.1–101.1

2

2.00

315–320

127.5–129.5

3

3.00

450–455

182.1–184.1

4

5.00

480–485

194.2–196.2

a Land

area without railway siding

Table 3 Tentative green area requirement for a 1.00 MTPA cement plant S. No.

Description

Total land area (ha)

1

Limestone mines

253.85 @ 1.53

2

Cement plant

99.10b

Total green area requirement

CFa

Green area (ha) @ 33% of total land 83.77 32.70 116.47

a Clinkerisation

factor indicates the requirement of limestone for 1 tonne of clinker production b Minimum area considered from Table 2 for tentative estimation green area

3 Green Area Obligation for Cement Plant The main objective of the green belt is to provide a barrier between the source of pollution (impact zone) and the surrounding environment in cement plant. The green belt helps to capture the fugitive emissions and to attenuate the noise generated apart from improving the aesthetics. Environmental clearance is must to get the land for limestone mines lease and cement plant. A time bound progressive green belt development plan is must to get environment clearance for cement plant and mines indicating the linear and quantitative coverage, plant species and time frame. Thick green belt should be developed in 33% area of cement plant as well as mines lease each. The plant specifies selection for green belt should be according to guideline of CPCB [8]. Table 3 indicates the tentative green area requirement for plant and mines for a 1.00 MTPA cement plant.

4 Energy Consumption and Bottlenecks with Conventional Fuel Cement production requires various forms of energy, i.e. thermal energy for chemical reaction in cement kiln to convert raw material into clinker, electrical energy for crushing and grinding of raw material, clinker and fuel, diesel for heavy earth movers and vehicle in mines and plant as well as initial start-up of the kiln operation. Average energy consumption of cement industry is 725 kcal/kg clinker (thermal energy) and 80 kWh per tonne cement (electrical energy) [9].

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Price (Indian Rupees) Based on Delhi Rate

Fig. 1 Diesel price (Indian rupees) based on Delhi rate. Source www.iocl.com

Coal is the main source of thermal energy and electrical energy (captive power plant) in Indian cement plants. In India, a Fuel Supply Agreement (FSA) exists to supply the coal to cement companies, generally at economical rates. However, demand for coal with a high calorific value, primarily from the cement sector, combined with the doubling of clean environment cess has resulted in a shift in coal imports into India from Indonesia, Australia and South Africa [10]. For production of limestone in opencast mine, diesel is the main fuel to operate various mining equipment and vehicle. Estimated diesel consumption for production of 1.00 t of lime stone is 0.53 L, hence for a cement plant of 1.00 MTPA clinker production capacity, limestone requirement will be 1,530,000 t @ 1.53 clinker factor basis and diesel requirement will be 810,900 L per annum [11]. Diesel is also being consumed in cement plant for emergency diesel generator and initial light up. However, these consumption is very low as compared to diesel consumption for limestone production in opencast mine, hence not considered for this paper. Diesel consumption area is still untouched and needs equal attention with respect to increasing trend of diesel price in India as indicated in Fig. 1. Above trends of diesel price clearly indicate the continuous increase in the diesel price in India. As cement industry is also one of the major diesel consumers in India, hence, a potential of saving can be worked out by implementing energy farming concept in cement industry.

5 Energy Farming and Biodiesel Scenario in India Energy farming is defined as the growing of crops specifically for production of fuel/energy. Energy farming mainly uses the crops whose final extracted oil is nonedible like Jatropha, Pongamia Pinnata (Karanja), etc. Fuel produced from energy crops is known as biofuel. Indian Government introduced National Policy on biofuels on 24 December 2009 with an aspirational target of 20% blending of biofuels by 2017. Jatropha was identified as the most suitable inedible oil seed for diesel production to achieve the target [12].

Energy Farming—A Green Solution for Indian Cement Industry

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Table 4 Realistic cost of biodiesel per litre in India by CII [14] S. No.

Heads

Cost (|/L)

1

Cost of Jatropha seeds

17.97

2

Freight for Jatropha seeds transportation

3

Oil extraction cost

2.32

4

Depreciation of oil extraction equipment and infrastructure

0.25

5

Administrative cost

0.60

6

Interest on working capital

0.33

Total straight vegetable oil (SVO) cost

23.37

1.90

7

Freight inward for straight vegetable oil to biodiesel processing plant

8

Trans-esterification and refining cost

6.59

9

Depreciation on the refining investment

0.87

10

Administrative cost for the above set-up

0.73

11

Interest on working capital cost of biodiesel

0.50

Price in |/L

1.01

33.07

Indian aim for 20% biodiesel blending failed due to various reasons such as policymakers overestimated Jatropha yield potential without consulting Indian farmers who traditionally grew Jatropha as a fence crop and knew of its growth limitations, biodiesel purchase policy 2006 fixed a price of |26.50/L of biodiesel in India and empowered the biodiesel manufacturers and oil marketing companies to trade it at this price. Unfortunately, the framework for setting up the above price did not attract the investors to kick start the journey of biodiesel in India, Farmers who were convinced into Jatropha cultivation—mainly those who were unfamiliar with it—entered buyback contracts, and most were abandoned when yields proved disappointing, Jatropha cultivation in some places resulted in the reduction of local food production (e.g. groundnut in the state of Tamil Nadu) [13]. A study on “Realistic Cost of Biodiesel in India” has been done by Confederation of Indian Industry (CII). Study clearly estimates the realistic price of biodiesel derived from Jatropha seeds at |33.07/L taking into account a credit of |0.34/L for glycerine and subsequently its numerous benefits on “Social-Environmental-Economic” front in the Indian context (as discussed in the last section) [13] (Table 4). With consideration of current GST (Source https://www.biofuelsdigest.com) on Biodiesel in India @ 12% the Total Price of Biodiesel for 1 L would be |37.04.

6 Concept of Energy Farming in Cement Industry Above data and facts indicate that all the disadvantages or reason for biofuel policy failure may be avoided if producer and consumer for biodiesel are same. It is possible for cement industry to cultivate Jatropha plant in 33% area of cement plant and

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limestone mines under green belt area obligation of environmental clearance. An oil extraction plant, trans-esterification and refining and blending of biodiesel with procured diesel can be done at plant level with maintaining the BIS guidelines. Cement plant may also use the oil cake as an alternative fuel which has attractive calorific value ~ 7000 kcal/kg. Glycerine which is a by-product can be sold to the cosmetic and pharmaceutical industry. Figure 2 indicates the flow chart of energy farming concept in cement plant. Major advantages of Energy Farming in cement industry are: • Producer and consumer are same, hence, least uncertainty about demand and supply. • Freight inward for straight vegetable oil to biodiesel processing plant is nil. • Better utilization of 33% green belt area by introducing energy crop. • Contribution in thermal substitution rate by using oil cake as an alternative fuel. • Reduction in carbon footprint and SOx generation from cement plant. • Normally a cement plant project takes 24–30 months from the date of inception to commissioning and Jatropha plants also take 30–36 months time to get matures for biodiesel production [14]. Also, cost of horticulture staff is optimized.

Jatropha Seeds from Source

Cultivate in Cement Plant Green area (33% of the Plant land) Jatropha Fruits

Cultivate in Mines Green area (33% of the mine land) Jatropha Fruits

Bio-Diesel Processing Unit

Bio-Diesel

Uses in Mining Locomotives/Equipment.

Protein Rich Cake

To Cement Plant Alternative Fuel PreProcessing Area

Firing to Pre-Calciner Fig. 2 Flow chart of energy farming concept in cement plant

Glycerine

Trading in Market

Energy Farming—A Green Solution for Indian Cement Industry

731

• As the cement plant will consume the biodiesel for their own purpose and not selling to the market, hence, they may approach to Government of India to waive off/or to reduce the taxes levied on biodiesel.

6.1 Potential of Deliverables from Jatropha [15] Different literature provides the different value and data about the potential deliverables from Jatropha which clearly indicates the different experiences of researchers. Following data is being considered here to evaluate the Jatropha viability in Indian cement industry [14]. Potential of high yielding Jatropha seed

3–4 metric tonne (MT)/hectare

Biodiesel potential

1.0 MT/hectare/year from 3rd year onwards

Protein-rich cake

1.9 MT/hectare (CV = 7000 kcal/kg)

Glycerine

0.095 MT/hectare

With the consideration of above data, Table 5 provides the potential of biodiesel production from 33% of green belt land of cement plant and limestone mines. Table 6 indicates the saving by use of protein-rich cake as alternative fuel for coal replacement. Table 7 indicates the saving by blending of biodiesel. Hence, Introduction of energy farming may generate |51.86 Lakh per annum with substantial reduction in CO2 emission, i.e. 1164.62 MT. Table 5 Potential of biodiesel production from 33% of greenbelt land S. No. Parameters

Value

Unit

Remarks From Table 3

1

Total land available for 116.47 green belt development for 1.0 MTPA cement plant

Hectares

2

Biodiesel potential

MTPA

116.47

138,654.76 Litre/year 3

Diesel replacement potential with blending of biodiesel

~17.00

@0.84 gm/cm3 density considered

Percentage/year Total diesel for 1.00 MTPA cement plant 810,900 Litre/year

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K. Kukreja et al.

Table 6 Saving by use of protein-rich cake as alternative fuel S. No.

Description

Value

Remarks

1

Total oil cake production (MT)

221.293

1.9 times of total land available in hectare [15]

2

Total thermal energy potential (kcal)

15.49 × 108

7000 kcal/kg [15]

3

Potential to replace the coal (MT)

344

Calorific value of coal considered 4500 kcal/kg

4

Saving per annum in | (Lakhs)

17.20

Coal price considered |5000/MT (Source https://markets.businessi nsider.com/commodities/coalprice)

5

CO2 reduction potential (MT)

818.72

Considered 2.38 kg CO2 /kg for bituminous (Source https://www. engineeringtoolbox.com/co2-emi ssion-fuels-d_1085.html)

Table 7 Saving by blending of biodiesel S. No. Description

Value

Remarks

1

Total biodiesel production per year

138,654.76 L

From Table 6

2

Saving per annum in | (Lakhs) by replacing diesel from biodiesel

34.66 Lakh

Approximate | 25 saving per litre of diesel

3

CO2 reduction potential (MT)

345.9 @ density of 0.792 from Table 8

Considered 3.15 kg CO2 /kg of diesel combustion (Source https://www.engine eringtoolbox.com/co2-emi ssion-fuels-d_1085.html)

7 Standards Available for Biodiesel and Suitability for Earthmovers and Vehicles of Cement Plant/Limestone Mining Biodiesel is produced in a pure form (referred to as “B100” or “neat biodiesel” as per IS 15607:2016 [16]) and is typically blended with petroleum-based diesel fuel. Such biodiesel blends are designated as BXX, where XX represents the percentage by volume of pure biodiesel contained in the blend (e.g. “B6,” “B20”). In India, Bureau of Indian Standard (BIS) adopted a technical standard, i.e. for biodiesel as blending stock. Technical standard IS16531-2016 is also available which provides the guidelines and specification of biodiesel, diesel fuel blend (B6-B20) [17]. A lot of research has been done for the impact of biodiesel on the internal combustion (IC) engines which concludes that the biodiesel can be used as a 20% blend in

Energy Farming—A Green Solution for Indian Cement Industry Table 8 Typical properties of biodiesel produced from JCO [21]

Property

Diesel

733 Biodiesel (methyl ester)

Flash point (°C)

65

128

Viscosity at 40 °C (C.S)

2.86

4.82

Specific gravity 29 °C

0.792

0.84

Calorific value (MJ/kg)

44.34

42.80

most diesel operated equipment with no or only minor modifications. Biodiesel is most commonly used as a blend with petroleum diesel at concentrations of up to 5 vol.% (B5) in conventional diesel fuel. Results show that the diesel blended with biodiesel reduces the CO emission significantly. However, it marginally increases the NO emission but within the acceptable limit [18–20]. Table 8 indicates the typical properties of biodiesel produced from JCO as compared to diesel.

8 Conclusion In this present study, it is concluded that the production of biodiesel by energy farming in place of 33% green belt area which is statutory requirement for obtaining environmental clearance for cement plant and associated limestone mines has great potential of monetary saving as well as environmental sustainability by reduction of CO2 emission. Although introduction of energy farming is mainly limited to the new cement plants and associated limestone mine, however potential may also be explored with existing limestone mines inline of their mines closure plan. This study highlights that the introduction of energy farming may generate |51.86 Lakh per annum with substantial reduction in CO2 emission, i.e. 1164.62 MT for a 1.00 MTPA capacity cement plant. Nowadays, cement players are preferring to install 2.00 MTPA or 3.00 MTPA capacity cement plant having large area, which further makes the energy farming more lucrative. In this present study, it is concluded that the energy farming is technically possible in cement plant and mines. Decision of implementations of energy farming concept requires detailed study related to financial viability with respect to capital investment, plant sizing, payback, etc., and various statuary requirements need to be analysed which includes approval from Regulatory Authority, installation of laboratory, permission to blend the biodiesel with conventional diesel and other health and safety regulations. Possibilities may also be worked out where government and cement industry work together for the national interest.

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References 1. The Compendium Titled.: The Cement Industry India 2018. National Council for Cement and Building Materials, Hyderabad (2018) 2. Indian Cement Industry Report. India Brand. Equity Found. (2019) 3. Quality of coal for Indian Cement Industry. Indian Cem. Rev. (2014) 4. Achieving 25% Thermal Substitutions Rate in Indian cement Industry by 2025. An Approach Paper published by Confederation of Indian Industry 5. PAT scheme Normalization Document and Monitoring and Verification Guidelines for Cement Sector Published by Bureau of Energy Efficiency, Government of India, March 2015 6. Reply of Lok Sabha Unstarred Question No. 244 on 26.04.2016 regarding Environmental Clearance for Mining by Ministry of Environment, Forest and Climate Change, Government of India. Link: http://www.indiaenvironmentportal.org.in/files/file/Environmental%20C learance%20for%20Mining_0.pdf 7. Bench Marking Report for Cement Projects and Cement Grinding Units for M/S Industrial Promotion & Investment Corporation of Odisha Ltd. In July 2013, National Council for Cement and Building Materials 8. Guidelines for Developing Green belts by CPCB India (2000) 9. Fonta, P., Sar, E., Kiran Ananth, P.V., Chaturvedi, S.K., Pahuja, A., Bhargava, R., Rao, K.N., Rajasekar, L., Herwadkar, S.V., Shrivastava, S., Krishnamoorthy, S.: GHG reduction potentials in the Indian cement industry—Upscaling implementation. In: 14th NCB International Seminar on Cement and Building Materials 10. India: Cement industry demand raises coal imports. An Article published on www.cemnet. com on 12th Sept 2016. Link: https://www.cemnet.com/News/story/160088/india-cement-ind ustry-demand-raises-coal-imports.html 11. Sahoo, L.K., Bandyopadhyay, S., Banerjee, R.: Benchmarking energy efficiency of opencast mining in India. In: IVth International Conference on Advances in Energy Research Indian Institute of Technology Bombay 12. Gain Report on Indian Biofuel 2017 Published by USDA Foreign Agriculture Services on 27th June 2017 13. Lima, M.G.B.: An Institutional analysis of biofuel policies and their social implications lessons from Brazil, India and Indonesia. Occasional Paper Nine Social Dimensions of Green Economy and Sustainable Development (2012) 14. Realistic Cost of Biodiesel in India by Confederation of Indian Industry (CII) 15. Arif, M., Ahmed, Z.: A book on bio-diesel jatropha curcas (A Promising Source) published by Satish Serial Publishing House, Delhi 16. Biodiesel (B100)-Fatty Acid Methyl Esters (Fame)-Specification IS 15607:2016, Bureau of Indian Standards (BIS) 17. Biodiesel-Diesel Fuel blend (B6-B20) Specification IS 6531:2016, BIS 18. Dhakad, S., Parashar, U., Singh Dandotiya, D., Dhakad, V., singh Gurjar, J.: Practical performance of internal combustion engine using jatropha oil as a bio-fuel. Int. J. Emerg. Technol. Adv. Eng. 3(10) (2013) 19. Ilag, P.L., Kahtal, S.A., Mhaske, S.S., Prabhu, U.S., More, V.: The impact of biofuel on IC engine and the environment. Int. Res. J. Eng. Technol. (IRJET) 05(03) (2018) 20. Xue, J., Grift, T.E., Hansen, A.C.: Effect of biodiesel on engine performances and emissions. Renew. Sustain. Energy Rev. 15, 1098–1116 (2011) 21. Antony Raja, S., Robinson smart, D.S., Lindon Robert Lee, C.: Biodiesel production from Jatropha oil and its characterization. Res. J. Chem. Sci. 1(1) (2011)

Energetic and Exergetic Performance Comparison of a Hybrid Solar Kalina Cycle at Solar and Solar Storage Mode of Operations P. Bhuyan, P. Borah, and T. K. Gogoi

1 Introduction Due to population growth, rapid urbanization and economic growth, demand for energy is increasing day by day. According to a report by the International Energy Agency, the demand for global energy will increase by another 30% in 2040 from its current demand. Most of the countries in the world plan to meet this energy demand from renewable energy. Among the renewable energy resources, solar is undoubtedly the cleanest and the most abundantly available source of renewable energy. For producing power from solar energy, mainly either solar photovoltaic or solar thermal technologies are used. In solar thermal power plants, often parabolic trough collectors (PTCs) are used for trapping solar energy and heating fluids to be used as energy carrier. Sometimes, linear Fresnel reflectors are also used for this purpose. The power tower is another solar thermal power technology that is used for producing steam directly in a central receiver and driving steam turbine. PTC-based solar technology is implemented in several applications to make effective utilization of solar energy for small- to medium-scale power generation through thermodynamic cycles such as organic Rankine cycle (ORC) and KC. Further, absorption cooling systems can also be operated with solar heat trapped through PTC-based solar technology. A number of researches have been carried out to analyse KC that utilizes low- to medium-temperature solar and other waste heat sources. KC very commonly uses a binary mixture of water and ammonia. In many research studies, solar energy-driven KC is investigated. Ganesh and Srinivas [1] proposed an augmented KC for which they obtained higher power output and efficiency over a simple Kalina cycle. Modi and Haglind [2] found better performance in a solar-driven KC over a simple Rankine cycle when it was integrated with a two-tank molten salt storage system. P. Bhuyan · P. Borah · T. K. Gogoi (B) Department of Mechanical Engineering, Tezpur University, Tezpur 784028, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_69

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Performance analysis of waste heat-driven KC was also carried out in some other research studies. Nemati et al. [3] investigated the influence of some significant parameters (compressor pressure ratio, pinch point temperature, degree of superheating, etc.) on performance of KC and ORC with the objective of waste heat recovery from a CGAM cogeneration system. Recently, Zhang et al. [4] proposed a flue gas waste heat-driven KC combined with a refrigeration system to obtain additional refrigeration capacity with reduced heat resource exhaust temperature. From the above literature review, it is found that the works pertaining to exergy analysis on solar hybrid KC are limited. Except the works by Modi and Haglind [2], where a comparative exergy analysis was done for simple Rankine cycle and KC, and Nemati et al. [3], where the exergy analysis was done for combined CGAM/ORC and CGAM/Kalina systems, all other studies were specific to energy analysis only. Energy analysis alone is not sufficient to evaluate system performance under reversible conditions, and also, to have a deeper insight into the system’s operation, combined energy and exergy analysis is a better approach of performance assessment. In this paper, a PTC-based solar hybrid KC is analysed thermodynamically to evaluate its energetic and exergetic performance. The exergy modelling of the solar KC is done in two different operational modes, viz. (i) solar mode during the low solar radiation and (ii) solar storage (SS) mode during high solar radiation time. Through energy and exergy analysis, it is intended to evaluate power and efficiency of the KC under a given set of operating conditions and also to quantify the sources of the exergy destruction in the hybrid system in both the solar and SS modes of operation. The solar radiation calculation is done implementing an ASHRAE model for the geographical location of Jodhpur, Rajasthan, India, considering its longitude and latitude. The solar radiation calculation corresponds to the month of February, and for the solar mode, the global solar radiation is calculated at 7 a.m. in the morning, whereas for the SS mode, the global solar radiation calculation corresponds to 2 p.m. in the afternoon.

2 Description of the Hybrid Solar Kalina Cycle The schematic of the hybrid solar KC is shown in Fig. 1. The PTC consists of 100 parallel rows of solar collectors with 10 heat collector elements (HCEs) in series in each row. In the PTC, the absorber tube is covered with a glass envelope and it is supported in the frame by a number of support brackets. Water is considered as heat transfer fluid (HTF) which is first heated in the PTC and then stored in the thermal storage tank when the system is operated in the SS mode. Using pump2, the hot water from the upper zone of the storage tank is pumped to supply heat subsequently in the vapour generator (VG) of the KC and in the water heater. The HTF after supplying heat finally enters the lower zone of the storage tank. From the storage tank, using pump1, HTF is again circulated through the collector elements of the PTC for heating purpose. In the solar mode however, no storage tank is used and the hot HTF from

Energetic and Exergetic Performance Comparison …

737

Fig. 1 Schematic of the solar Kalina cycle

the PTC directly goes to the VG of the KC and after supplying heat, it is again pumped to make it flow through the collector elements of the PTC. As such, the water heater is not used in the solar mode. The KC consists of the VG, separator, vapour turbine (VT), condenser, pump, regenerator and a mixer. Ammonia–water mixture is considered as working fluid in the KC. After partial evaporation in the VG, the mixture enters the separator (7) at 70 bar where it is separated into lean liquid (8) and rich vapour (1). The rich vapour goes to the VT, while the lean liquid mixture flows through the regenerator and the pressure reducing valve and is finally mixed with the low-pressure vapour from the VT outlet in the mixer. The ammonia–water mixture then goes to the condenser where it is fully condensed. The saturated liquid mixture is then pumped to the VG via the regenerator to complete the cycle.

3 Thermodynamic Modelling and Assumptions It is assumed that the system operates at steady state and the heat loss between the system and surroundings is negligible. The system parameters assumed for modelling of solar collector and KC are given in Table 1. Exergy analysis assumes negligible effects of kinetic and potential energy. Water at temperature and pressure of 25 °C and 1.01325 bar, respectively, is considered as the reference system.

738

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Table 1 Geometrical and other input parameters of the PTC Parameters

Value

PTC Width of collector, W

5.76 m

Collector length per element, L

12.27 m

No. of collector elements in series

10

No. of collector rows

100

Inner diameter of absorber tube, D2

0.066 m

Outer diameter of the absorber tube, D3

0.07 m

Inner diameter of the glass envelope, D4

0.115 m

Outer diameter of glass envelope, D6

0.121 m

Emittance of the glass envelope, εenv

0.15

Emittance of the absorber tube, εabs

0.94

Transmittance of the glass envelope, τenv

0.96

Reflectivity of the mirror, ρenv

0.94

Absorbance of the absorber tube, αabs

0.96

Absorbance of the envelope, αenv

0.02

Intercept factor,γ

0.93

HTF volume flow rate in the solar mode through 0.60 l/s a single row of collector, Q˙ HTF HTF volume flow rate in the SS mode through a 0.65 l/s single row of collector, Q˙ HTF HTF temperature at pump1 inlet

70 °C

Kalina cycle Isentropic efficiency of pump, η p

0.85

Isentropic efficiency of VT, ηVT

0.85

Motor efficiency, ηmotor

0.90

Generator efficiency, ηgen

0.98

Condensation temperature, T4

35 °C

Regenerator effectiveness, εreg

0.75

Separator inlet pressure, P7

70 bar

Separator inlet concentration, X 7

0.90

Separator inlet temperature, T7

25 °C less than dew point temperature corresponding to P7 and X 7

3.1 PTC Modelling The global irradiation for a clear sky is obtained using the following relation from Meleki et al. [5]

Energetic and Exergetic Performance Comparison …

G b = Ibn cos θ Z

739

(1)

where Ibn is solar beam radiation on horizontal surface and θ Z is the solar zenith angle. G b is multiplied with a factor 0.99 to consider the presence of dust particle, water vapour and other impurities [6]. Optical model Optical model is about calculating optical efficiencies of the absorber tube and the glass envelope which are required for calculation of the amount   of solar radiation absorbed by the glass envelope q˙5,SolAbs and the absorber tube q˙3,SolAbs as given below. q5,SolAbs = qi ηenv αenv

(2)

q3,SolAbs = qi ηabs αabs

(3)

qi = G b × W , W is the width of the PTC, ηenv and ηabs are the optical efficiencies of the glass cover and the absorber tube, respectively, and these are calculated by taking into account all the optical properties of the collector and the tracking system [7]. αenv and αabs are the absorbance of the glass cover and the absorber tube. Standard formulae are used to calculate the solar declination (δ), hour angle (ω), solar zenith angle (θ Z ), incident angle (θ ) that are required for calculation of G b , incident angle modifier K (θ ), the optical efficiencies, etc. Thermal model The thermal model is developed by using steady-state energy balance at each surface of the PTC cross section and assuming uniform heat fluxes and thermodynamic properties around the circumference of the PTC elements. The solar energy incident on the PTCelements is absorbedby the glass envelope and the selective coating of absorber q˙5,SolAbs and q˙3,SolAbs . The absorber conducts a by convection portion of this radiation (q˙3−2,cond ) and then transfers it to the HTF   ). The remaining part of the solar radiation is convected q ˙ (q˙2−1,conv 3−4,conv   and radi-  ated q˙3−4,rad back to the glass envelope and lost through conduction q˙cond,bracket in the support brackets.  Further, the energy transmitted to the glass envelope is first conducted through it q˙4−5,cond  and thenfinally lost to the environment by convec  tion q˙5−amb,conv and radiation q˙5−sky,rad . Energy balance equations for the thermal model has been taken from Ref. [7]. In this model, mainly the procedure followed in Ref. [6] is adopted. The temperature T 1 –T 5 is calculated from energy balance equations in an iterative manner, and the HTF outlet temperature at each PTC element is calculated. Tout

    q˙3,Solabs − q˙3−4,conv − q˙3−4,rad − n q˙cond,bracket L = Tin + m˙ col C p +

2 Vin2 − Vout 2C p

(4)

740

P. Bhuyan et al.

In equation (4) is used for this purpose where m˙ col is the HTF flow rate through the PTC, n is the number of conduction brackets and L is the length of each collector element in a single row. Vin and Vout are the inlet and outlet velocities. The outlet conditions of one PTC element are inlet to the following element and so on. Finally, the HTF temperature at PTC outlet is calculated from this model. Thermodynamic properties of the HTF (water) are computed from the International Association for the Properties of Water and Steam (IAPWS) formulation 1997 [8]. The other temperature-dependent properties of water such as dynamic viscosity, specific heat and thermal conductivity are calculated using standard equations. Storage Tank Modelling The storage tank modelling follows the same method of Ref. [9] in solving three simultaneous equations to calculate hot water temperature in the upper and middle zones of the storage tank and at the HTF storage tank inlet (T15 ). The hot water from the upper zone of the storage tank goes to the VG of the KC. Similarly, the temperature of the lower zone of the storage tank is set equal to the water temperature at pump1 inlet (70 °C). Exergy Model The maximum available useful work from solar irradiation (ψ) is calculated using Parrot’s formula with Tsun = 5770 K and the Sun’s cone angle δ as 0.005 rad [10]. ψ =1−

1 Tamb 4 T0 (1 − cos δ)1/4 + 3 Tsun 3 Tsun

(5)

The exergy of the solar irradiation is calculated using the following equation. E˙ xSR = G b Aa ψ

(6)

Aa is the aperture area of PTC. Exergy gained by the HTF and exergy efficiency of the PTC (ηPTC ) are calculated as follows [11]. E˙ xgain

⎡T ⎤  out Tout C (T )dT p ⎦ = m˙ col ⎣ C p (T )dT − T0 T Tin

ηPTC = 1 −

(7)

Tin

E˙ xloss + E˙ xd E˙ xSR

(8)

The exergy loss due to (i) optical errors ( E˙ xloss,opt ) and (ii) heat transfer to the ambient from the support bracket ( E˙ xloss,brac ) and from the outer glass envelope ( E˙ xloss,5−amb ) is calculated following the procedure outlined in Ref [10] to calculate the total exergy loss ( E˙ xloss ) as appeared in Eq. (8). The exergy destruction caused by fluid friction in the absorber tube is also calculated. The exergy destruction due to heat transfer (i) from the Sun to the absorber surface ( E˙ xd,q1 ) and (ii) between the absorber surface and the HTF ( E˙ xd,q2 ) is calculated using equations taken from Ref. [10] to calculate the total exergy destructed as follows.

Energetic and Exergetic Performance Comparison …

E˙ xd = E˙ xd,P + E˙ xd,q1 + E˙ xd,q2

741

(9)

3.2 Modelling of the Kalina Cycle In the Kalina cycle, the thermodynamic properties of ammonia–water mixture, pure ammonia liquid and vapour are calculated using property equations of Ziegler and Trepp [12]. The calculation starts with pressure and ammonia concentration at state 7 which are known input parameters. The temperature at state 7 is considered 25 °C less than the dew point temperature corresponding to P7 and X 7 . Next, ammonia concentrations at the saturated liquid and vapour states are calculated from P7 and X 7 using lever rule. Condenser temperature T4 is taken as 35 °C, and condenser pressure (P4 ) is the bubble point pressure corresponding to T4 and X 4 . Next the enthalpy and entropy at states 4, 5 and 7 are calculated. The temperature at point 5 is calculated iteratively from known values of h 5 , P5 (= P7 ) and X 5 (= X 7 ). Effectiveness method is used to model the regenerator considering 75% effectiveness (εreg ) to calculate temperature, enthalpy and entropy at points 6 and 9. The mass flow rate of ammonia– water mixture in the KC is calculated from heat balance in the VG for both SS and solar modes. The mass of rich vapour mixture at turbine inlet (1) is calculated using lever rule. The temperature at the exit of the throttle valve (T 10 ) is calculated in an iterative manner. Calculation of vapour fraction and temperature at state 2 also involves iteration. Next, the enthalpy and entropy at all relevant points of KC are calculated. Finally, net power of the KC is obtained by using the following relation. W˙ P ˙ = WT × ηgen − ηmotor 

Net PowerKC

(10)

where W˙ T = (h 1 − h 2 ) and W˙ P = W˙ pump1 + W˙ pump2 + W˙ pump3 . The following general exergy balance is applied to calculate irreversible losses occurring in different components of the Kalina cycle.  T0 ˙ ˙ ˙ − W˙ − E˙ xd = 0

E xin − E xout + Q 1 − T

(11)

The exergy at a given state is calculated as follows. E˙ x = (h − h 0 ) − T0 (s − s0 )

(12)

The energy and exergy efficiencies of the KC are defined as follows η I,KC =

Net PowerKC m˙ 13 [h 13 − h 14 ]

(13)

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η I I,KC =

Net PowerKC m˙ 13 [(h 13 − h 0 ) − T0 (s13 − s0 )]

(14)

where h 0 and s0 are the enthalpy and entropy at reference state. T0 is the reference temperature. In terms of the state points, Eqs. (13) and (14) will however be slightly different for system operation in the solar mode.

4 Results and Discussion The results obtained by simulating the solar heat-driven KC are presented in this section. The thermodynamic properties and mass flow rates at various states of the KC and the PTC, at both the SS and solar modes, are presented in Table 2. As can be seen, the property values at the state points of the KC are not changing and these remain invariant with the change in mode of operation. The difference can be seen only in the mass flow rates which is relatively more in the SS mode. The HTF temperature at pump1 inlet and its volume flow rate are adjusted such that a temperature difference of 20 °C is maintained between the hot HTF stream at VG inlet and the binary mixture at VG outlet in both the SS and solar modes of operation. Therefore, the HTF temperature at pump1 inlet is considered as 70 °C in both the cases. But the HTF volume flow rate is 0.65 l/s during SS mode and 0.6 l/s during the solar mode. With the above-mentioned temperature and flow rates, it is possible to maintain a temperature of 164.60 °C in the SS mode and 164.51 °C in the solar mode for the HTF at VG inlet which is approximately 20 °C higher than the binary mixture temperature (144.63 °C) at VG outlet (state 7). Since the mass flow rates are more, more power is obtained from the KC in the SS mode in spite of having three pumps in the system as opposed to two in the system in the solar mode. This is shown in Table 3. With higher global radiation in SS mode, more heat gain and higher PTC efficiency are found while energy efficiency of KC is same in both operational modes. Total pressure drop in the PTC is also more during SS mode. Further, it is possible to integrate a water heater into the system during SS mode that provides additional heating of nearly 104.48 kg/s of water from 15.70 to 45.67 °C. From exergy analysis, it is found that in contrary to the energy efficiency, the exergy efficiency of the KC is more and as obviously, it is also more during the SS mode. The exergy efficiency of the PTC is also found more in SS mode due to change in exergy of the solar irradiation E˙ xSR with the change in mode of operation. The PTC irreversibility is, however, more (55,043.14 kW) in SS mode compared to 38,329.6 kW in the solar mode. This is due to higher HTF mass flow and high pressure drop during the SS mode of operation. From exergy balance, the exergy destruction (or irreversibility) occurred in the KC components is also evaluated which is shown in Fig. 2. As observed, the highest irreversibility occurs in the condenser due to temperature glide during condensation. Hence, condenser is the major source of exergy destruction. The next contributor to irreversibility is

Energetic and Exergetic Performance Comparison …

743

Table 2 Thermodynamic properties at various states of the Kalina cycle and the PTC at both the SS and solar modes State

P (bar)

T (°C)

NH3 conc.

Specific enthalpy, kJ/kg

Specific entropy, kJ/kg K

VF

Flow rate, kg/s (SS mode)

Flow rate, kg/s (solar mode)

1

70.0

144.63

0.9547

1460.16

4.065

1

17.395

15.523

2

12.1

58.99

0.9547

1248.45

4.178

0.9061

17.395

15.523

3

12.1

59.93

0.9

1080.45

3.695

0.7782

20.258

18.078

4

12.1

35.00

0.9

87.15

0.539

0

20.258

18.078

5

70.0

36.98

0.9

98.96

0.547

0

20.258

18.078

6

70.0

50.67

0.9

165.48

0.756

0

20.258

18.078

7

70.0

144.63

0.9

1319.66

3.747

0.8587

20.258

18.078

8

70.0

144.63

0.5678

466.15

1.816

0

2.863

2.555

9

70.0

63.90

0.5678

59.90

0.739

0

2.863

2.555

10

12.1

64.61

0.5678

59.90

0.763

0

2.863

2.555

SS mode 11

20.807

211.91

–

906.40

2.442

–

55.684

–

12

7.287

164.60

–

695.53

1.988

–

55.684

–

13

7.871

164.61

–

695.60

1.988

–

55.684

–

14

7.477

60.67

–

315.33

0.803

–

55.684

–

15

7.241

22.70

–

95.89

0.335

–

55.684

–

16

0.327

70

–

293.07

0.955

–

55.684

–

17

21.456

70.20

–

295.61

0.956

–

55.684

–

20

1.043

17.70

–

74.389

0.263

–

104.48

–

21

1.013

45.67

–

191.34

0.647

–

104.48

–

Solar mode 17

8.224

70.02

–

293.604

0.955

–

–

54.368

18

7.271

164.51

–

695.12

1.987

–

–

54.368

19

6.907

70

–

348.095

0.916

–

–

54.368

the VT. Irreversibility is almost negligible in the separator and mixer because of no heat loss and pressure drop in these two components. Further, it is seen that except in the VG, irreversible losses in the other KC components are more during the SS mode compared to those in the solar mode. The irreversibility of water heater is also evaluated, and it is found to be 4181.36 kW.

744 Table 3 Performance parameters obtained for KC and PTC at both SS and solar modes

P. Bhuyan et al. Performance parameters

SS mode

Solar mode

Global solar irradiation

956.23 W/m2

640.00 W/m2

Collector efficiency of PTC, ηCol

56.41%

51.87%

Heat gain in PTC

3015.55 W/m

1855.87 W/m

Total pressure drop in PTC

64.88 kPa

58.94 kPa

Exergy efficiency of PTC, ηPTC

15.73%

12.33%

PTC irreversibility, IPTC

55,043.14 kW

38,329.6 kW

PTC

Kalina cycle Net power output

3.21 MW

3.00 MW

Pump1 pumping power

157.30 kW

8.61 kW

Pump2 pumping power

4.7 kW

–

Energy efficiency, η I,KC

14.43%

14.43%

Exergy efficiency, η I I,KC

36.09%

34.10%

Fig. 2 Irreversibility associated with different components of Kalina cycle

5 Conclusion A comparative study was carried out to evaluate the energetic and exergetic performance of a solar KC in the SS and solar mode. The solar radiation was calculated for the geographical location of Jodhpur, Rajasthan, India, at two different times for the month of February to represent the two modes of operation. The following conclusions are made based on the findings from this study. • More power could be obtained from the KC with higher exergy efficiency when the system was operated in SS mode. Additional heating can be obtained by integrating a water heater into the system in SS mode. • Although the system performance was better in terms of power and efficiency when it was operated in the SS mode, but the system irreversibility was more. Maximum irreversibility occurred in the PTC. Among the KC components,

Energetic and Exergetic Performance Comparison …

745

maximum irreversibility occurred in the condenser with the VT and the regenerator to follow. • Depending upon the conditions in the KC, the HTF flow rate and temperature at PTC inlet can be adjusted to maintain required heat source temperature at VG inlet of the KC. These two parameters finally control the mass flow rates in the KC and its power output.

References 1. Ganesh, N., Srinivas, T.: Power augmentation in a Kalina power station for medium temperature low grade heat. J. Sol. Energy Eng. 135(3) (2013) 2. Modi, A., Haglind, F.: Performance analysis of a Kalina cycle for a central receiver solar thermal power plant with direct steam generation. Appl. Therm. Eng. 65, 201–208 (2014) 3. Nemati, A., Nami, H., Ranjbar, F., Yari, M.: A comparative thermodynamic analysis of ORC and Kalina cycles for waste heat recovery: A case study for CGAM cogeneration system. Therm. Eng. 9, 1–13 (2017) 4. Zhang, S., Chen, Y., Wu, J., Zhu, Z.: Thermodynamic analysis on a modified Kalina cycle with parallel cogeneration of power and refrigeration. Energy Convers. Manag. 163, 1–12 (2018) 5. Maleki, S., Hizam, H., Gomes, C.: Estimation of hourly, daily and monthly global solar radiation on inclined surfaces: Models re-visited. Energies 10, 134 (2017) 6. Forristal, R.: Heat transfer analysis and modelling of a parabolic trough solar receiver implemented in engineering solver equation, No. NREL/TP-550-34169, National Renewable Energy Laboratory, Golden, CO. (US) (2003) 7. Gogoi, T.K., Saikia, S.: Performance analysis of a solar heat driven organic Rankine cycle and absorption cooling system. Therm. Sci. Eng. Prog. 13 (2019) 8. Wagner, W., Cooper, J.R., Dittmann, A., Kijima, J., Kretzschmar, H.J., Kruse, A.: The IAPWS industrial formulation 1997 for the thermodynamic properties of water and steam. J. Eng. Gas Turbines Power 122, 150–181 (2000) 9. Tzivanidis, C., Bellos, E., Antonopoulos, K.A.: Exergetic, energetic and financial evaluation of a solar driven absorption cooling system with various collector types. Appl. Therm. Eng. 102, 749–759 (2016) 10. Padilla, R.V., Fontalyoa, A., Demirkayab, G., Martineza, A., Quirogaa, G.: Exergy analysis of parabolic trough solar receiver. Appl. Therm. Eng. 67, 579–586 (2014) 11. Bellos, E., Tzivznidis, C.: A detailed exergetic analysis of parabolic trough colletors. Energy Convers. Manag. 149, 275–292 (2017) 12. Ziegler, B., Trepp, C.H.: Equation of state for ammonia-water mixtures. Int. J. Refrig. 7, 101–106 (1984)

Assessment of Different Multiclass SVM Strategies for Fault Classification in a PV System Rahul Kumar Mandal and Paresh G. Kale

1 Introduction The growth of grid-connected PV systems necessitates the employment of monitoring devices for fault detection and diagnosis mechanism [1]. The monitoring mechanism in the PV systems predicts the power profile on a day-ahead basis and compares the predicted power with the actual power for any discrepancies. At any point where the predicted power and actual power differ by a substantial margin, a fault is detected [2]. The severe faults occurring in the PV plant are: short-circuit fault in any module, inverse bypass diode fault, shunted bypass diode fault, and shadowing effect in any module. Each fault reflects changes in the panel voltage and current measurements; short-circuit fault shows a sudden dip in the panel voltage with a spike in current whereas shadowing effects show a consistent low voltage for a long time. Fault classification is an essential step in the fault diagnosis block of the PV monitoring systems. Detection of a particular fault will allow the preventive measures to be implemented by the person-in-charge. Various classification methods existing are Decision Trees [3], Bayesian classifiers [4], Nearest neighbor classifiers [5], Multi-Layer-Perceptron (MLP), Support Vector Machines (SVM) [6], and Ensemble methods like Random Forests [7]. SVM, a novel machine learning technique centered on Statistical Learning Theory [8], is an elegant classification scheme among the kernel-based training methods [9]. Most of the Artificial Neural Network (ANN) techniques minimize the sum of square errors for classification leading a vulnerability to outliers. However, SVM uses a soft margin approach to add robustness to the classification schema. The SVM is, however, initially designed for binary classifications, and extending the technique to a R. K. Mandal · P. G. Kale (B) Department of Electrical Engineering, NIT Rourkela, Rourkela 769008, Odisha, India e-mail: [email protected] R. K. Mandal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_70

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multiclass problem is both computationally complex as well as time-consuming [10]. Thus, different schemes for multiclass classification in non-linear SVMs with soft margin approach are discussed in the subsequent sub-sections. SVM is classified into linear and non-linear SVM based on the separability of the data points. A linear SVM is used for separation in cases when the data points are separable by a single linear boundary, e.g., segregating low-degree polynomial data mappings [11]. The classical SVM algorithm is limited only to linear hyperplane separation of binary classes. For non-separable data, as in the case of a PV system, the non-linear boundary is required. In non- linear SVMs, the sample data is mapped to a feature space of higher dimensionality such that the separating hyperplane between the two classes becomes linear, i.e., we choose a transformation ∅ such that ∅ : R N → H transforms the input data space R N to a higher dimensional space H [12]. Once the linear separation using SVM is implemented in the higher dimensional hyperspace, a reverse transformation can be implemented to bring back the feature dataset to initial data, and now the linear hyperplane in the H space becomes a non-linear boundary in the R N space [6].

2 Different Approaches to Multiclass Classification The non-linear multiclass classification methods include the One versus All (implementing the ‘Winner Takes All’ strategy), One versus One (implementing the MaxWins Voting strategy), the Decision Directed Acyclic Graph, and Adaptive Directed Acyclic Graph. Plat et al. [13] introduced a Decision Directed Acyclic Graph (DDAG), improving the training and evaluation time over the OVA architecture, at a minor expense of efficiency. The Adaptive Directed Acyclic Graph (ADAG) implements a tournament-based architecture, reducing the algorithm’s dependence on the sequence of nodes to be accessed before classifying a data-point to a particular class. The OVA classification strategy implements N binary SVMs. The ith classifier unit is trained using all positive labels for the ith class and negative labels for all the other classes. So for a N class classifier, N different SVMs are employed, each classifying a particular class among the rest. A test point is said to be in a particular class if the point lies inside the segregation boundary of a particular class and has the highest probability associated with the corresponding class. Such a technique is also termed as ‘Winner Takes All’ approach [10]. The OVO, also known as the MaxWin votes algorithm, constructs all the possible permutations of binary classifiers in   N N (N −1) different SVMs a set of two from a set of N classes resulting in or 2 2 [14]. The next step classifies a test sample using all the different classifiers. The classifier achieving the maximum number of votes among all is designated as a test data class. If more than one class obtains the highest tally of votes, a point is assigned to a random class. The disadvantage of such an approach is that the inefficiency in classifying a particular point increases super-linearly with the number of classes.

Assessment of Different Multiclass SVM Strategies …

749

The Decision Directed Acyclic Graph SVM (DDAGSVM) method implements a graph-based node analysis with the depth of the graph the same as the number of classes to be classified, eliminating one class every search. DDAGSVM is completely dependent on the series of checks to be conducted leading to a large possibility of error. ADAGSVM is a reverse triangular structure which aims to reduce the number of computations as well as reducing the dependency on the order of the classes in which the computation takes place [12].

3 Methodology Synthetic data for the classification problem is obtained from the website pvpmc.sandia.gov [15], providing current and voltage measurements corresponding to each fault type. Four different fault types, namely, short-circuit fault in any module, inverse bypass diode fault, shunted bypass diode fault, and shadowing effect in any module are labeled as classes I, II, III, and IV, respectively. Whenever a fault is detected in the PV system, the last five voltage and corresponding current readings are accumulated to form a 10*1 input data vector. An Eigen Value Decomposition based Principal Component Analysis is carried out for reducing higher dimension data into two principal variables, X* and Y *, and a set of SVMs classify the faults. SVM can implement numerous techniques to tackle a multiclass classification problem: OVA, OVO, and DAG (both decision based and adaptive) are the primary multiclass strategies. MATLAB codes of each SVM strategies were executed on a system having specifications as Intel (R) Core (TM) i5-4210U CPU@ 1.70 GHz, 2.40 GHz, 8 GB RAM, 64-bit operating system, and x-64 based processor on Windows 8.1 platform. The objective is to identify the best (C, γ ) for the RBF kernel-based SVM, for the classifier to classify the data vectors with maximum accuracy. C denotes the cost of misclassification and γ is the parameter of a Gaussian Kernel. An advanced version of the training to testing data approach is cross-validation. An n fold cross-validation divides the training set into n different subsets and, one of those n subsets is used for testing whereas the rest n − 1 subsets are used for training of the SVM. The n fold cross-validation approach hence trains all possible combinations of the training set to get the most viable results, preventing the problem of over-fitting [16]. The various combinations of multiclass methods are investigated with the training to testing dataset: 60%:40%, 70%:30%, and 80%:20%. The efficiency of a particular method is the percentage of classes a method correctly classifies. The leading diagonal terms of confusion matrix signify the correctly classified data points.

4 Results and Discussion For cross-validation in the SVM, the grid search method is used, where various combinations of (C, γ ) are experimented and the pair with the best cross-validation

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Table 1 Interclass cross-validation errors for OVO, DDAG, and ADAG classifications Cross-validation error

Class I (%)

Class II (%)

Class III (%)

Class IV (%)

Class I

–

22

0

0

Class II

22

–

12

0

Class III

0

12

–

Class IV

0

0

2

–

results is picked as tabulated in Table 1. The best way to select the hyper-parameters is to try the exponentially rising series of (C, γ ). Thus, C = 2−10 , 2−8 , . . . , 212 and γ = 2−12 , 2−10 , . . . , 22 . The grid search is anticipated to be a time-consuming as all the possible combinations are checked continuously. However, in actual, it is comparable to selective search based on heuristics owing to the lesser number of hyper-parameters in the case of RBF kernel-based SVM. The cross-validation results give errors for classes I, II, III, and IV (for OVA classification) to be 26.67%, 22%, 0%, and 0%, respectively, (C, γ ) = (24 , 2−6 ). Table 2 shows the efficiency and runtime for the various cases of training and test data for different multiclass classification strategies. The DDAG takes the maximum computation time among all the other multiclass classification strategies owing to additional node calculations whereas the ADAG classifier shows the best runtime with efficiency comparable to the OVA algorithm. The OVA classification scheme shows the best efficiency but with the higher runtime that ADAG. Figures 1 and 2 show the boundary segregation of faults using RBF kernel-based SVM with training to testing ratio = 60%:40% and 80%:20%, respectively, for multiclass classification using OVA approach. The fault classes I and II are more difficult to separate than the fault classes III and IV. Class IV fault, shadowing phenomenon in a module, can be evident if the generated power from a module is continuously low for an extended period. Since five readings before the fault occurrence are used to form the input data vector, the class IV fault detection is more straightforward compared to other, making its decision boundary separable from other fault classes. Class III fault or the shunted bypass diode fault also benefits from the input data selection, giving a proper decision boundary. However, the decision boundaries for class II and class III overlap and not wholly separable. The class I data points are too widely scattered to be allotted an utterly separable boundary. Figure 3 shows the interclass boundary segregation using RBF based kernel for 60%:40% for multiclass classification using DAG-based methods and OVO approach. Figure 4 shows the variation of the efficiency and runtime of different multiclass SVM strategies for a different number of total training and test data points for different training to test ratios. The OVA strategy shows the best efficiency among all the multiclass strategies for different training points. The efficiency tends to saturate when the total data points (training and test points combined) come near 280–300. The runtime is least for all cases of DDAG algorithm proving the lowest timing complexity for DDAG. The efficiency of DDAG is at par with the efficiency evaluated for the OVA algorithm. The synthetic dataset has the value for voltage and current generated after

Assessment of Different Multiclass SVM Strategies …

751

Table 2 Confusion matrices of the respective multiclass SVM classification strategies No. of faults Predicted fault class cases = 300 SVM (using RBF kernel) aided by two level PCA (training data + Training set:testing set Training set:testing set testing data) (60:40) = 180 training (70:30) = 210 training data points data points

Training set:testing set (80:20) = 240 training data points

One versus all [winner takes all strategy] Actual fault class

F1

F2

F3

F4

F1

F2

F3

F4

C1

31

12

0

0

39

C2

15

28

0

0

17

C3

0

0

45

0

C4

0

0

0

45

F1

F2

F3

F4

8

3

35

0

0

49

11

0

0

0

7

43

4

0

0

0

52

0

0

0

60

0

0

0

0

52

0

0

0

60

One versus one [max-wins voting strategy] Actual fault class

F1

F2

F3

F4

F1

F2

F3

F4

F1

F2

F3

F4

C1

30

15

0

0

39

13

0

0

43

12

5

0

C2

12

29

4

0

11

41

0

0

10

44

6

0

C3

0

2

43

0

0

6

46

0

0

0

60

0

C4

1

0

0

44

0

0

0

52

0

0

0

60

DDAGSVM Actual fault class

F1

F2

F3

F4

F1

F2

F3

F4

F1

F2

F3

F4

C1

27

18

0

0

36

16

0

0

41

12

7

0

C2

14

26

5

0

9

43

0

0

11

38

11

0

C3

0

5

40

0

0

12

40

0

0

2

58

0

C4

1

0

1

43

0

2

1

49

0

0

1

59

ADAGSVM Actual fault class

F1

F2

F3

F4

F1

F2

F3

F4

F1

F2

F3

F4

C1

28

17

0

0

39

13

0

0

43

10

7

0

C2

9

30

6

0

12

40

0

0

12

42

6

0

C3

0

1

44

0

0

2

50

0

0

0

60

0

C4

0

0

0

45

0

0

0

52

0

0

0

60

every 15 min’ restricting SVM to a 15 min’ window for training and classification. After every reading, the reading and fault type constitutes another instance for the training set, increasing the training set every time a reading is recorded. The saturation of efficiency near the 280–300 points mark shows no further training is required after the SVM is trained with 300 points. However, the recorded data can be scanned for erroneous voltage, and current values and a better training set can be extracted through cross-validation, making the resulting SVM insusceptible to erroneous data vectors. The data sample undergoes a five-fold cross-validation process to achieve maximum possible accuracy for each training to test ratio.

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Fig. 1 Boundary segregation of faults using SVM (RBF kernel with training to testing ratio = 60%:40%) a short-circuit in any module; b inverse bypass diode fault; c shunted bypass diode fault; d shadowing effect in the modules

Figure 4a shows the runtime and efficiency of a multiclass classification based on OVA. The runtime increases super-linearly as the percentage of training data increases since the SVM needs more time to train for extra points. Figure 4b–d reproduces the same trend in training runtime. The runtime of OVA is slightly higher than the ADAG classifier, though the efficiency of OVA is better than ADAG. For a fifteen-minute window incoming data points. OVA multiclass classification strategy is hence the best. However, the performance of the classification models is also influenced by the input parameters and the sample size.

Assessment of Different Multiclass SVM Strategies …

753

Fig. 2 Boundary segregation of faults using SVM (RBF kernel with training to testing ratio = 80%:20%) a short-circuit in any module; b inverse bypass diode fault; c shunted bypass diode fault; d shadowing effect in the modules

5 Conclusion The different multiclass classification strategies using SVM are studied, and the runtime and efficiency of each method are compared. The OVA algorithm shows the highest efficiency equivalent to 88.33% at the expense of higher runtime. OVA and ADAG show better efficiency and runtime as compared to DDAG. The runtime for OVO showing efficiency 85% is comparable to OVA since the number of features in the data is decidedly less, and also, the number of classes is low. The runtime for ADAG (having efficiency 85.42%) is the least of all methods which would outperform all the other multiclass strategies in higher order multiclass data. The efficiency of any particular multiclass algorithm increases as the number of training points increase. However, the synthetic data provided is limited to 300 fault data points

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Fig. 3 Boundary segregation of faults using SVM for two different classes (RBF kernel with training to testing ratio = 60%:40%) a Class I versus Class II; b Class I versus Class III; c Class I versus Class IV; d Class II versus Class III; e Class II versus Class IV; f Class III versus Class IV

Assessment of Different Multiclass SVM Strategies …

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Fig. 4 Efficiency and runtime for different numbers of training points using multiclass SVM strategies: a OVA; b OVO; c DDAG; d ADAG

(not comprising the case where multiple faults occur at once) above which the efficiency cannot be tested. The basis for multiclass classification using different SVM algorithms is established and can be implemented for a real plant in the future.

References 1. Chang, H.-C., Lin, S.-C., Kuo, C.-C., Yu, H.-P.: Cloud monitoring for solar plants with support vector machine based fault detection system. Math. Probl. Eng. 2014, 1–10 (2014) 2. Chao, K.H., Ho, S.H., Wang, M.H.: Modeling and fault diagnosis of a photovoltaic system. Electr. Power Syst. Res. 78(1), 97–105 (2008) 3. Zhao, Y., Yang, L., Lehman, B., de Palma, J.-F., Mosesian, J., Lyons, R.: Decision tree-based fault detection and classification in solar photovoltaic arrays. In: 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition, pp. 93–99 (2012) 4. Kim, H.-C., Ghahramani, Z.: Bayesian classifier combination. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 619–627 (2012) 5. Cunningham, P., Delany, S.J.: K-nearest neighbour classifiers. Tech. Rep. UCD-CSI-2007-4, May 2014, pp. 1–17 (2007) 6. Graf, H.P., Cosatto, E., Bottou, L., Durdanovic, I., Vapnik, V.: Parallel Support vector machines: the cascade SVM. Adv. Neural Inf. Process. Syst., 521–528 (2005) 7. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001) 8. Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999) 9. Schölkopf, B. et al.: Comparing support vector machines with Gaussian kernels to radial basis function networks. IEEE Trans. Signal Proc. 45(1599) (1997)

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10. Rifkin, R., Klautau, A., Org, K.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5, 101–141 (2004) 11. Chang, Y., Lin, C.: Training and Testing Low-degree Polynomial Data Mappings via Linear SVM, vol. 11, pp. 1471–1490 (2010) 12. Kijsirikul, B., Ussivakul, N.: Multiclass support vector machines using adaptive directed acyclic graph. In: Proceedings of the 2002 International Joint Conference on Neural Networks Neural Networks, 2002. IJCNN ’02, vol. 1, January, pp. 980–985 (2002) 13. Platt, J., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: International Conference on Neural Information Processing Systems, pp. 547–553 (2000) 14. Hsu, C., Lin, C.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002) 15. PVPMC Sandia Labs 2018. https://pvpmc.sandia.gov/. Accessed 26.04.18 16. Keerthi, S.S., Chapelle, O.: An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models, vol. 1, no. 3

Performance Analysis of Double Glass Water Based Photovoltaic/Thermal System Ajay Sharma, S. Vaishak, and Purnanand V. Bhale

1 Introduction Solar energy has great potential to meet various energy requirements of both domestic and industrial sectors due to its abundant availability and vast distribution. Devices used to extract solar energy are highly credible, low maintenance, and 20–30 years life span without any negative environmental impact. According to applications, traditionally these devices fall into two categories: Electrical (Photovoltaic Modules) and Heat (Solar thermal collectors) [1]. The electrical efficiency of commercially available modules varies in the range of 8-15%. More than 85% of the solar irradiance is transformed into heat energy, due to which the operating temperature of solar cell increases [2]. As a result, the electrical efficiency and power output of PV module are further reduced [3]. Research advances in this field led to the evolution of a hybrid PV/T system [4]. This system co-generates both heat and electricity with better yield compare to independent photovoltaic and solar thermal systems. Generally, in PV/T systems, air or water is used as the heat transfer medium for the thermal management of PV cells and to extract the heat energy. For different configurations and climate conditions, air-based PV/T systems can achieve a thermal efficiency of 28–42%, and an electrical efficiency of 6–12% [5, 6]. Alternatively, water-based PV/T systems give thermal efficiency of 45–80% and electrical efficiency of 10–15% [7, 8]. From an extensive review, Wu et al. [9] proposed that flat plate PV/T systems could be used for low and medium temperature applications and could attain 22–79% thermal efficiency and 6.7–15% electrical efficiency. Furthermore, Joshi et al. [10] concluded that the energy and exergy efficiency of PV/T system is higher than an independent PV system. In another study, Tiwari and Sodha [11] proposed a thermal model of integrated water-based PV/T solar system and the simulation predicts a daily thermal A. Sharma · S. Vaishak · P. V. Bhale (B) Renewable and Sustainable Energy Lab, S. V. National Institute of Technology, Surat 395007, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_71

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efficiency of around 58%. Likewise, Dupeyrat et al. [12] also developed a 2D thermal model to analyze the performance of a single glazed flat plate water based PV/T system. A prototype was developed to validate the numerical model and it achieved a maximum overall efficiency of 88% under the tested climatic conditions. Recently, water-based nanofluids were also tested for its application in PV/T system. One such study reported a 24.99%, 26%, and 25% improvement in thermal, electrical, and overall performance with the application of nanofluids [13]. Presently, many research are also being reported regarding the integration of Phase change material (PCM) with water-based PV/T system for achieving better performance [14]. From the literature survey, it was noted that for most of the reported research, conventional PV module with serpentine tube design was commonly used. A comparative study carried out by Zhang et al. [15] reported that double glass PV module has higher reliability, long durability, and better performance under different stress conditions when compared to conventional PV modules. However, studies carried out using double glass PV module are limited to date [16–18]. One such study found that in comparison to the glass to Tedlar based collector, double glass PV/T collector had a superior overall performance [16]. Furthermore, Ibrahim et al. [19] analyzed different tube configurations for PV/T collector design and it was observed that spiral flow design gives the maximum thermal and electrical performance. In this context, the objective of the present study is to experimentally evaluate the performance of double glass water based PV/T system. A prototype was fabricated in-house by integrating a double glass PV module with thermal absorber having spiral tube configuration and the performance was evaluated under the climatic conditions of Surat, India.

2 System Description and Methodology Figure 1 shows a photograph of the in-house developed water-based double glass PV/T system. The collector mainly consists of a double glass PV panel attached to a thermal absorber. The thermal absorber was made up of copper plate and tube design having a spiral configuration. It was attached to the PV module with a layer of thermal grease in between. A thick layer of glass wool insulation was provided at the rear of the collector to reduce the heat loss from the thermal absorber to the ambient. A submersible pump was used to circulate water between the insulated hot water storage tank and the PV/T collector. The detailed specifications of the system are given in Table 1. Besides, a conventional PV module was used as the reference module during experimentation to quantify the gain in electrical efficiency in PV/T system. Both the PV modules were of polycrystalline type having the same electrical characteristics.

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Fig. 1 Experimental setup of double glass PV/T system Table 1 Specifications of PV/T system

Components

Specifications

Polycrystalline silicon panel Type: double glass PV module: 100 W No. of cell: 36 Packing factor = 0.92 Area: 0.65 m2 I sc = 5.94 A V oc = 18.54 V Solar collector

Type of collector: flat plate Angle of collector: 23° Type of absorber: sheet and tube spiral arrangement Sheet thickness: 0.64 mm Absorber material: copper Area of sheet: 0.65 m2 Length of tube: 17 m Diameter of tube: 0.0063 m

Water tank

Type: rectangular Capacity: 150 L

Pump

Type: AC motor Power rating: 60 W

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2.1 Methodology The performance of the prototype was tested under different climate conditions of Surat, India. The PV/T system along with the reference panel was erected on the terrace of Renewable and Sustainable Lab of S V National Institute of Technology at a tilt angle of 23° and towards south facing. The water tank was filled up to 150 L and no water was withdrawn during the experimentation. During experimentation, ClassA RTD’s were used to measure different temperatures required for the performance evaluation of the system. Various ambient parameters such as wind speed, solar irradiance and ambient temperature during experimentation were recorded using a weather station which was installed close to the experimental setup. In addition, the system’s electrical characteristics were evaluated using a MECO make PV module analyzer. The PV/T system was tested from 9:00 am to 4:00 pm under the various climatic conditions. However, the performance under a clear sky condition was used for analysis purposes in the present study.

2.2 Performance Assessment Parameters The performance of the PV/T system is mainly assessed by thermal and electrical efficiencies. The thermal efficiency of a PV/T system is calculated as: ηth =

w MC p dT dt G AC

(1)

where G is the solar irradiance (W/m2 ), AC is the area of the collector (m2 ), M is the mass of water in the storage tank (kg), C p is the specific heat capacity of water w is the rise in temperature (°C/s) of water, respectively. (J/kg °C), dT dt Electrical efficiency of PV/T collector is given by: ηe =

P G AC

(2)

where P is the electrical power output (W). Another performance parameter is the overall efficiency of the system which can be represented as: ηo = ηth + ηe

(3)

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3 Results and Discussion Performance of the system corresponding to a clear sky condition (29/03/2019) was used for the analysis purpose. The experiment was performed at a mass flow rate of 0.026 kg/s with a tank capacity of 150 L. The initial water temperature was observed to be 30.1 °C. Figure 2 illustrates the variation of ambient temperature and solar irradiance during the experimentation. The solar irradiance was found to be varying between 520 and 1023 W/m2 while ambient temperature increased from 30.67 to 39.64 °C. Besides the average wind speed was observed to be 1.31 m/s. Figure 3 illustrates the variation of panel operating temperature for the PV/T system and the reference module. The maximum panel operating temperature for the PV/T system and the reference module was observed to be 52 °C and 63 °C, respectively. The PV/T system’s average working temperature was observed to be 10% lower than the reference module. However, in the late afternoon hours as the temperature of the circulating water was increased, the reduction in panel operating temperature achieved by the PV/T system when compared to the reference module was observed to be very less. The variation of water temperature with respect to the time shown in Fig. 4. The temperature of the water (150 L) gradually increased from 30.10 to 44.60 °C which shows the heat gain by the system. Generally, it is recommended that the water temperature has to reach at least 40 °C to support domestic heating applications. Figures 5 and 6 show the hourly variation of heat gain and electrical output from the PV/T system, respectively. The above two parameters were found to be following the same trend of solar irradiance. It can be deduced that the heat gain increased from 280 to 455 W with the rise in solar irradiance, following which it decreased to 245 W in the late afternoon hours. The electrical power output of the PV/T system was

Fig. 2 Variation of ambient temperature and solar irradiance

762 Fig. 3 Variation of panel operating temperature with time

Fig. 4 Variation of water temperature with time

Fig. 5 Variation of heat gain with time

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Fig. 6 Variation of power output with time

observed to be varying between 41 and 83 W, while that of the reference module varied between 41 and 73 W. Figure 7 illustrates the variation of the electrical efficiency of the PV/T system and the reference module. It can be observed that the PV/T system has higher electrical efficiency compared to the reference PV module. The PV/T system electrical efficiency was observed to be 14% for the peak solar irradiance while that of the reference module was 12.5%. The thermal management of the PV module in the present study increases its conversion efficiency by 12%. The high operating temperature of the PV module reduces the conversion efficiency and it was reported that the same decreases linearly with an increase in cell temperature [19]. Figure 8 illustrates the variation of thermal efficiency of the system with time. The average thermal efficiency of the system was observed to be 66% while it varied between 55 and 72%. The system’s high thermal efficiency attributes to the design Fig. 7 Variation of electrical efficiency with time

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Fig. 8 Variation of thermal efficiency with time

of the spiral heat exchanger and the double glass PV arrangement. With glass as the back-sheet material, the radiation falling on the non-packing area falls directly on the thermal absorber which increases the heat gain of the system. Furthermore, from Fig. 8, it can be deduced that the thermal efficiency is maximum in the morning and decrease sharply by late afternoon. This attributes to increasing solar irradiance and low water temperature during morning hours which reduces the heat loss to the surroundings. However, by late afternoon due to the collective result of a decrease in solar irradiance and an increase in water temperature, the heat loss to the ambient increases which reduces the thermal efficiency of the system. From Fig. 9, it can be deduced that the overall system efficiency varies in the range of 69–86%. This concludes that the energy yield per unit area of the PV/T system is higher than the PV and solar thermal systems independently. Fig. 9 Variation of overall efficiency with time

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4 Conclusion Solar cells are sensitive to operating temperature and its efficiency drop when the temperature of cells increases. Hybrid PV/T system can be a solution for improving the electrical output with a good amount of thermal benefits. The present study experimentally evaluates the performance of an in-house fabricated double glass water based-PV/T system under the climatic conditions of Surat, India. The results obtained show that the overall efficiency of the system was ranging from 69 to 86% with maximum thermal and electrical efficiency around 72% and 14%, respectively. It was also noted that at the end of the experiment, the water temperature attained can support practical domestic applications. In conclusion, the present PV/T system significantly improves the utilization of available solar irradiance and can be considered as a promising technology for the future.

References 1. Othman, M.Y., Ibrahim, A., Jin, G.L., Ruslan, M.H., Sopian, K.: Photovoltaic-thermal (PV/T) technology – the future energy technology. Renew. Energy 49, 171–174 (2013) 2. Chow, T.T., He, W., Ji, J.: Hybrid photovoltaic-thermosyphon water heating system for residential application. Sol. Energy (2006) 3. Skoplaki, E., Palyvos, J.A.: On the temperature dependence of photovoltaic module electrical performance: a review of efficiency/power correlations. Sol. Energy 83, 614–624 (2009) 4. Sathe, T.M., Dhoble, A.S.: A review on recent advancements in photovoltaic thermal techniques. Renew. Sustain. Energy Rev. (2017) 5. Solanki, S.C., Dubey, S., Tiwari, A.: Indoor simulation and testing of photovoltaic thermal (PV/T) air collectors. Appl. Energy 86, 2421–2428 (2009) 6. Agrawal, S., Tiwari, G.N., Pandey, H.D.: Indoor experimental analysis of glazed hybrid photovoltaic thermal tiles air collector connected in series. Energy Build. 53, 145–151 (2012) 7. Ji, J., Lu, J.-P., Chow, T.-T., He, W., Pei, G.: A sensitivity study of a hybrid photovoltaic/thermal water-heating system with natural circulation. Appl. Energy 84, 222–237 (2007) 8. Othman, M.Y., Hamid, S.A., Tabook, M.A.S., Sopian, K., Roslan, M.H., Ibarahim, Z.: Performance analysis of PV/T Combi with water and air heating system: an experimental study. Renew. Energy 86, 716–722 (2016) 9. Wu, J., Zhang, X., Shen, J., Wu, Y., Connelly, K., Yang, T., Tang, L., Xiao, M., Wei, Y., Jiang, K.: A review of thermal absorbers and their integration methods for the combined solar photovoltaic/thermal (PV/T) modules. Renew. Sustain. Energy Rev. 75, 839–854 (2017) 10. Joshi, A.S., Dincer, I., Reddy, B.V.: Performance analysis of photovoltaic systems: a review. Renew. Sustain. Energy Rev. 13, 1884–1897 (2009) 11. Tiwari, A., Sodha, M.S.: Performance evaluation of solar PV/T system: an experimental validation. Sol. Energy (2006) 12. Dupeyrat, P., Ménézo, C., Rommel, M., Henning, H.M.: Efficient single glazed flat plate photovoltaic-thermal hybrid collector for domestic hot water system. Sol. Energy (2011) 13. Al-Shamani, A.N., Alghoul, M.A., Elbreki, A.M., Ammar, A.A., Abed, A.M., Sopian, K.: Mathematical and experimental evaluation of thermal and electrical efficiency of PV/T collector using different water based nano-fluids. Energy (2018) 14. Al-Waeli, A.H.A., Sopian, K., Chaichan, M.T., Kazem, H.A., Ibrahim, A., Mat, S., Ruslan, M.H.: Evaluation of the nanofluid and nano-PCM based photovoltaic thermal. PVT) system. An experimental study, Energy Convers. Manag. (2017)

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15. Zhang, Y., Xu, J., Shu, Y., Quan, P., Wang, Y., Mao, J., Gao, Y., Fu, C., Feng, Z., Verlinden, P.J., Yang, P., Chu, J.: High-reliability and long-durability double-glass module with crystalline silicon solar cells with fire-safety class a certification. In: PVSEC 28th (2013) 16. Joshi, A.S., Tiwari, A., Tiwari, G.N., Dincer, I., Reddy, B.V.: Performance evaluation of a hybrid photovoltaic thermal (PV/T) (glass-to-glass) system. Int. J. Therm. Sci. 48, 154–164 (2009) 17. Dubey, S., Sandhu, G.S., Tiwari, G.N.: Analytical expression for electrical efficiency of PV/T hybrid air collector. Appl. Energy 86, 697–705 (2009) 18. Dubey, S., Tiwari, G.N.: Thermal modeling of a combined system of photovoltaic thermal (PV/T) solar water heater. Sol. Energy 82, 602–612 (2008) 19. Ibrahim, A., Othman, M.Y., Ruslan, M.H., Alghoul, M.A., Yahya, M., Zaharim, A., Sopian, K.: Performance of photovoltaic thermal collector (PVT) with different absorbers design. WSEAS Trans. Environ. Dev. (2009)

Modeling Polarization Losses in HTPEM Fuel Cells Vamsi Ambala, Anusree Unnikrishnan, N. Rajalakshmi, and Vinod M. Janardhanan

1 Introduction The relatively high operating temperature of high temperature polymer electrolyte membrane fuel cell (HTPEMFC) offers certain advantages over its low temperature counterpart [1]. They operate in the temperature range of 393–453 K, and the high temperature operation in general improves the electrode kinetics, CO tolerance, and eliminates challenges associated with water management that is encountered during operation below 100 °C [2–4]. Experimental studies assisted by modeling and simulation can rapidly advance the technological development and commercialization of HTPEMFC technology. There are several literatures that deals with the dc-polarization and impedance modeling of HTPEM fuel cells [5–9]. However, a detailed evaluation of electrode kinetics is rather limited or nonexistent under HTPEM fuel cell operating conditions. In general, the hydrogen oxidation and oxygen reduction kinetics are studied at room temperature using rotating disc electrodes [10, 11]. However, the exchange current density (i0 ) values estimated from real electrode systems are found to be much higher than that calculated from rotating disc electrode measurements [3, 11]. In this work, a systematic evaluation of the electrode kinetics under HTPEM fuel cell operating conditions is presented. The modeling literature indiscriminately uses the Butler–Volmer equation for calculating the current density without paying V. Ambala · A. Unnikrishnan · V. M. Janardhanan (B) Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Sangareddy 502285, India e-mail: [email protected] A. Unnikrishnan · N. Rajalakshmi Centre for Fuel Cell Technology (CFCT), International Advanced Research Centre for Powder Metallurgy and New Materials (ARCI), IIT-M Research Park, 2nd Floor, Phase-1, 6, Kanagam Road, Taramani, Chennai 600113, India

© Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_72

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any attention to its validity. Majority of the literature assumes an arbitrary order dependency of the reactants and products on the i0 formulation. In the work presented here, we use six different formulations for modeling the hydrogen oxidation reaction and three different models for oxygen reduction reaction [3]. The models are then validated using data from literature.

2 Modeling Framework 2.1 Electrochemical Model The hydrogen oxidation is assumed to follow [3, 4] H2 (g) + 2∗ ↔ 2H(∗) H(∗) ↔ H+ + e− H2 + ∗ ↔ H+ + H(∗) + e− The first reaction is known as Tafel reaction, second is known as Volmer reaction and the third is knows as Heyrovsky reaction. Here, (*) represents a Pt adsorption site. H(*) is an adsorbed hydrogen atom. The H+ formed is incorporated into the electrolyte phase. Assuming one of the above reactions to be rate limiting and others to be in equilibrium or of negligible rate, six different formulations for electrochemical oxidation of hydrogen can be derived. The final form of the current density and exchange current density formulations is given below [3]. Volmer–Tafel (Volmer rate limiting and Tafel in equilibrium)   i = i 0a exp(βa f ηa ) − exp(−βc f ηa )  i 0a =

i a∗

K H2 pH2

(1−βa )/2

1/2  1 + K CO pCO + K H2 pH2

Tafel–Volmer (Tafel rate limiting and Volmer in equilibrium)   1 − exp(−2 f ηa ) i = i 0a  2 1/2  1 + K CO pCO + K H2 pH2 exp(− f ηa ) i 0a = i a∗ pH2

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Heyrovsky–Volmer (Heyrovsky rate limiting and Volmer in equilibrium)   exp(βa f ηa ) − exp(−(1 + βc ) f ηa )  i = i 0a   1/2 1 + K CO pCO + K T pH2 exp(− f ηa ) i 0a = i a∗ pH1−βa /2 2

Volmer–Heyrovsky (Volmer rate limiting and Heyrovsky in equilibrium)   exp((1 + βa ) f ηa ) − exp(−βc f ηa )  i = i 0a   1/2 1 + K H2 pH2 exp( f ηa ) + K CO pCO i 0a = i a∗ pHβc /2 2

Tafel–Heyrovsky (Tafel rate limiting and Heyrovsky in equilibrium)   1 − exp(2 f ηa ) i = i 0a  2  1/2 1 + K H2 pH2 exp( f ηa ) + K CO pCO i 0a = i a∗ pH2 Heyrovsky–Tafel (Heyrovsky rate limiting and Tafel in equilibrium)   i = i 0a exp(βa f ηa ) − exp(−βc f ηa ) 

i 0a =

i a∗p

1−β H2 a

β /2 K H2 pH2 a 1/2  1 + K H2 pH2 + K CO pCO

The oxygen reduction is assumed to follow a dissociative mechanism [12, 13] O2 + 2∗ ↔ 2O(∗) O(∗) + H+ + e− ↔ OH(∗) OH(∗) + H+ + e− ↔ H2 O(∗) H2 O(∗) ↔ H2 O + (∗) Three different forms of model equations result by assuming one of the first three reactions to be rate limiting. Assumption of O2 adsorption as rate limiting leads to

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a non-Butler–Volmer-type kinetic expression, which could not reproduce the data measured experimentally. Therefore, we only present the model that leads to the successful prediction of the experimental data for ORR. i = i 0c {exp[(1 + βa ) f ηc ] − exp[−βc f ηc ] 

i 0c = i c∗

β /2  (2−βc )/4 K H2 O pH2 O c K O2 pO2   1/2  1 + K O2 pO2 + K H2 O pH2 O 1 + exp(−G ∗ /RT )

3 Transport The charge transport and species transport models and the boundary conditions used in this work are explained in previous reports and are not repeated here. Interested readers may refer to [3, 4, 14].

4 Results and Discussion The fitting parameters used in the model equations are i a∗ , i c∗ and β. i 0a and i 0c depends on the concentrations of hydrogen and oxygen with an order that is decided by β. The equilibrium constants appearing the expressions for i 0a and i 0c may be calculated by equating the rates for the adsorption reaction and the corresponding desorption reaction. In order to do a model discrimination, simulations are performed with different models for HOR, however, using the same parameter values. The results are shown in Fig. 1. It can be seen that all models result in almost same performance, except Heyrovsky–Tafel, which results in slightly lower performance compared to other models. This essentially means that one can reproduce the same experimental data using any of the HOR models by slightly changing the parameters. Since Volmertype equations are the most commonly found ones in the literature, we use the same for the rest of the simulations presented in this work. The model predictions for cell operating on pure hydrogen are compared with experimental data at different temperatures in Fig. 2. Good agreement between model predictions and experimentally measured data is observed. The temperature dependency is introduced in the model by expressing i 0a and i 0c in the Arrhenius form ∗ = i  exp(−E  /RT )). The fit parameter values are given in Table 1. (i a/c Although the agreement is good, it is not a guarantee of the uniqueness of the model parameters. To be certain about the uniqueness of the fitted parameters, the model must be capable of reproducing the experimentally measured polarization and activation losses using the same set of parameters. Therefore, the experiments

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Volmer-Tafel Volmer-Heyrovsky Tafel-Heyrovsky Hetrovsky-Tafel Heyrovsky-Volmer

0.9 0.8

Voltage/ V

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

0.1

0.2

0.3

0.4

0.6

0.5

Current density/ A/cm

0.7

2

Fig. 1 Comparison between different HOR models

Fig. 2 Comparison between model predictions and experimental observation for cell operating at three different temperatures Table 1 Exchange current density parameters used to reproduce experimental data given in Fig. 2

Parameter

HOR

ORR

i

8.25 × 1010

1.06 × 107

E

68.11

81.66

βa

0.7

0.3

772 Table 2 Exchange current density parameters used to reproduce experimental data given in Fig. 3

V. Ambala et al. Parameter

HOR

ORR

i

20

0.025

βa

0.7

0.4

Fig. 3 Comparison between experimental measurements of cell performance and activation losses at 160 °C with the model predictions

reported by Kaserer et al., are simulated here [15]. The experiments are done at 160 °C and the parameters used to reproduce the experimentally measured polarization and activation losses are given in Table 2. The comparison between the model predicted and experimentally measured cell performance is shown in Fig. 3 and the corresponding model predicted activation losses and the experimentally measured activation losses are also shown in Fig. 3. Excellent agreement is observed between the measured data and the model predictions. In Fig. 3, the activation losses for the anode are i R corrected, i.e., the data plotted is ηa − i R. The cathode activation losses are reported as ηc − E rev .

5 Conclusions This paper presents the modeling of cell performance using electrochemical models derived from single-step electron transfer reactions. The model is capable of predicting the polarization behavior of the cell and the activation losses with a single set of parameters.

References 1. Hall, W.J.: The operation of HT-PEM fuel cells coupled to lithium ion batteries in a fleet of light passenger vehicles. Fuel Cells 14(6), 945–953 (2014)

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2. Araya, S.S., Zhou, F., Liso, V., Sahlin, S.L., Vang, J.R., Thomas, S., Gao, X., Jeppesen, C., Kær, S.K.: A comprehensive review of PBI-based high temperature PEM fuel cells. Int. J. Hydrogen Energy 41(46), 21310–21344 (2016) 3. Unnikrishnan, A., Rajalakshmi, N., Janardhanan, V.M.: Kinetics of electrochemical charge transfer in HT-PEM fuel cells. Electrochim. Acta 293, 128–140 (2019) 4. Unnikrishnan, A., Rajalakshmi, N., Janardhanan, V.M.: Mechanistic modeling of electrochemical charge transfer in HT-PEM fuel cells. Electrochim. Acta 261, 436–444 (2018) 5. Zuliani, N., Taccani, R.: Microcogeneration system based on HTPEM fuel cell fueled with natural gas: performance analysis. Appl Energy 97, 802–808 (2012) 6. Bergmann, A., Gerteisen, D., Kurz, T.: Modelling of CO poisoning and its dynamics in HTPEM fuel cells. Fuel Cells 10(2), 278–287 (2010) 7. Vang, J.R., Andreasen, S.J., Kær, S.K.: A transient fuel cell model to simulate HTPEM fuel cell impedance spectra. J. Fuel Cell Sci. Technol. 9(2), 021005 (2012) 8. Jiao, K., Alaefour, I.E., Li, X.: Three-dimensional non-isothermal modeling of carbon monoxide poisoning in high temperature proton exchange membrane fuel cells with phosphoric acid doped polybenzimidazole membranes. Fuel 90(2), 568–582 (2011) 9. Andreasen, S.J., Kær, S.K.: Modelling and evaluation of heating strategies for high temperature polymer electrolyte membrane fuel cell stacks. Int. J. Hydrogen Energy 33(17), 4655–4664 (2008) 10. Igarashi, H., Fujino, T., Watanabe, M.: Hydrogen electro-oxidation on platinum catalysts in the presence of trace carbon monoxide. J. Electroanal. Chem. 391(1–2), 119–123 (1995) 11. Sheng, W., Gasteiger, H.A., Shao-Horn, Y.: Hydrogen oxidation and evolution reaction kinetics on platinum: acid vs alkaline electrolytes. J. Electrochem. Soc. 157(11), B1529–B1536 (2010) 12. Luntz, A.C., Williams, M.D., Bethune, D.S.: The sticking of O2 on a Pt (111) surface. J Chem. Phys. 89(7), 4381–4395 (1988) 13. Zhdanov, V.P.: Simulations of processes related to H2 –O2 PEM fuel cells. J. Electroanal. Chem. 607, 17–24 (2007) 14. Andersson, M., Paradis, H., Yuan, J., Sundén, B.: Three dimensional modeling of a solid oxide fuel cell coupling charge transfer phenomena with transport processes and heat generation. Electrochim. Acta 109, 881–893 (2013) 15. Kaserer, S., Rakousky, C., Melke, J., Roth, C.: Design of a reference electrode for high temperature PEM fuel cells. J. Appl. Electrochem. 43, 1069–1078 (2013)

Effect of Diesel Injection Timings on the Nature of Cyclic Combustion Variations in a RCCI Engine Ajay Singh, Rakesh Kumar Maurya, and Mohit Raj Saxena

1 Introduction The reciprocating internal combustion (IC) engines are intrinsic to automotive vehicles since their introduction long back. Spark ignition (SI) and compression ignition (CI) are the two main commercially used internal combustion engines. A huge research and development work (to improve the performance, durability, and cost) has made automobiles accessible to the common people which led to a tremendous increase in the number of automotive vehicles worldwide. This increased count has caused serious problems related to environmental pollution, depletion of energy resources beneath the earth, and led to the present stringent government emissionsrelated policies. Both SI and CI internal combustion engines have their relative benefits and drawbacks. The SI engines have clean operation when employed with threeway catalytic converter, but they have poor part-load efficiency, while CI engines have higher efficiency but there exists a trade-off between NOx and soot emissions [1]. Several advanced low-temperature combustion (LTC) strategies namely HCCI, PCCI, RCCI have been introduced as alternatives to the conventional IC engines providing a thermal efficiency comparable to CI engines along with lower NOx and particulate matter emissions [2]. However, all of these strategies are not equally promising for future automotive applications due to problems related to control of heat release rate and combustion phasing over a wide range of engine operating conditions [2]. In reactivity controlled compression ignition (RCCI) engines, a low reactivity fuel (gasoline-like fuels) is injected into port, and a high reactivity fuel (diesellike fuels) is injected directly inside the combustion chamber. In RCCI combustion strategy heat release rate and combustion phasing are controlled by spatial stratification between the two fuels and relative ratio of the two fuels [3]. At higher A. Singh · R. K. Maurya (B) · M. R. Saxena Advanced Engine and Fuel Research Laboratory, Department of Mechanical Engineering, Indian Institute of Technology, Ropar, Rupnagar 140001, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_73

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engine loads, there is an increase in CO and HC emissions along with a high value of maximum pressure rise rate (PRRmax ) in RCCI combustion [4], which limits its operating load range to be narrower than convention diesel engines. Extending the operating load range of RCCI engine has been an interesting topic of research [4, 5]. Operating load range at higher engine speeds has been increased by using high EGR rate and quantity of gasoline along with moderate advancement in diesel injection timing [4]. Advanced diesel injection timing has been utilized to decrease the PRRmax while using both gasoline and methanol as port-injected fuels but the coefficient of variation of indicated mean effective pressure (COVIMEP ) and of total heat release (COVTHR), statistical measures of cyclic combustion instabilities increased beyond the desired limit with the advancement in start of injection (SOI) timing [6]. Increased cyclic combustion variations cause poor drivability (variations in engine speed and torque), increase in engine-out emissions, increased fuel consumption, lower thermodynamic efficiency, lower knock resistance, and compromised spark timing/injection timing [7, 8]. In the case of SI engines, it has been found that a COVIMEP greater than 10% causes drivability issues [1] while this limit is 3.5% for advanced premixed combustion modes [9]. The combustion process in an engine is known to be a complex nonlinear process and identification of the nature of this process (random, regular, or chaotic) has been a topic of interest for research because it can be a help in designing engine control strategies. Traditional methods such as phase space reconstruction, Poincaré section, recurrence plots, recurrence quantitative analysis, correlation dimension, Lyapunov exponent along with return maps and wavelet analysis have been used to investigate the existence of deterministic chaotic behavior in combustion for diesel engines [10–14] and for gasoline and natural gas-operated SI engines [15–21]. The traditional methods are very complex and cumbersome computationally because they require phase space reconstruction. Recently, a simpler method, 0-1 has been used by researchers to determine nature of combustion instabilities using combustion noise from a combustor [15] and in-cylinder pressure time series obtained from a lean-burn natural gas engine using spark advance angle as a control parameter [16, 22]. No such studies related to chaotic dynamics have been done for RCCI engines where the existence of a deterministic nature is highly probable [23]. Also, previous studies [16, 22] are done with continuous time series and it would be interesting to test the applicability of 0-1 test method with a discrete-time series. In the present study, the 0-1 test method is applied to a discrete-time series of PRRmax obtained from an RCCI engine, and effect of diesel SOI timing is analyzed for estimating the nonlinear dynamical nature of combustion.

2 Experimental Setup A single-cylinder automotive diesel engine has been modified to operate in RCCI combustion mode. Gasoline and methanol are used as low reactivity fuels and diesel as a high reactivity fuel. Low reactivity fuel is injected into the intake manifold

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by using a solenoid port fuel injector at an injection pressure of 3 bar, while high reactivity fuel is injected directly into the cylinder at a pressure of 500 bar by using common rail injection system. The engine was tested at engine speed of 1500 rpm and fixed engine load of 1.5 bar BMEP. The diesel start of injection (SOI) sweeps from 10° bTDC to 60° bTDC when gasoline is used as a low reactivity fuel while it sweeps from 10° bTDC to 40° bTDC when methanol is used as a low reactivity fuel. For more details about the experimental setup and procedure, the readers are suggested to refer to a recent paper by the same authors [6].

3 0-1 Test Method Gottwald and Melbourne [24–27] proposed 0-1 test for determining the presence of chaotic nature in a time series. The output is either 0 or 1 indicating a regular or chaotic behavior of the underlying dynamics, respectively. The 0-1 test method has been applied to various systems including cutting process, population model, bouncing ball system, doffing system [28–31] along with combustion system analysis of internal combustion engines [15, 16, 22]. The 0-1 test is simpler and effective to estimate chaotic nature of a time series including several advantages: (1) it is a binary system, i.e., output is either 0 or 1, (2) there is no need of phase space reconstruction, (3) the choice of observable alters the convergence properties but not the ultimate output of the test, and thus, the test can be applied to any deterministic dynamic system. According to this method, for a given time series x( j) for j = 1, 2 . . . , N , two new translation variables p and q are defined as: pc (t) =

t 

x( j)cos( jc)

(1)

x( j)sin( jc)

(2)

j=1

qc (t) =

t  j=1

where t = 1, 2, 3, . . . and c is a randomly chosen value between 0 and π. The track of pc (t) − qc (t) resembles a Brownian motion for a chaotic dynamic system while it is bounded for a regular dynamical system. To investigate the diffusive or non-diffusive behavior of pc (t) and qc (t), mean square displacement Mc (t) of the translation variables is computed for several values of c and is given by: Mc (t) = lim

N →∞

N 1  [ pc ( j + t) − pc ( j)]2 + [qc ( j + t) − qc ( j)]2 N j=1

(3)

It should be noted that the above equation is applicable for t  N which can be assured if t approaches some cut-off value t ≤ tcutoff . It has been observed that

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tcutoff = N /10 yields good results [24]. For a regular system, Mc (t) is a bounded function of time while for a chaotic system, it scales linearly with time. Finally to investigate more about the chaotic behavior of a system, the asymptotic growth rate kc , of mean square displacement is computed whose values are 1 and 0 for a chaotic and regular system, respectively. Computation of kc may be done by the regression method or correlation method and the latter is implemented in the present study. cov(t, ) kc = corr(t, ) = √ var(t) · var()

(4)

where cov and var stand for covariance and variance, respectively, and  = Dc (1), Dc (2), . . . , Dc (N /10) where Dc (t) is the modified mean square displacement, giving better convergence properties than Mc (t) and is obtained by using equations (5) and (6): Dc (t) = Mc (t) − vosc (c, t) vosc (c, t) = mean(x)2

1 − cos(nt) 1 − cos(t)

(5) (6)

All the  steps are repeated for 100 randomly chosen values of ‘c’ from the  above π , [22, 28–31] and median of obtained 100 values of kc is computed interval 4π 5 5 which provides knowledge about the chaotic nature of the observed time series. The 0-1 test is similar to a single frequency Fourier transform, and the frequency may coincide with the resonance frequency. If it happens for some chosen value of c then irrespective of dynamics, the results are obtained as pc (t) ∼ t and Mc (t) ∼ t 2 . Choosing appropriate value of ‘c’ and averaging the results over values of c is very important [32]. The 0-1 test is inherent to resonance at c = 0 and that is why the range of c is restricted above zero. Also, taking median of ‘k’ in place of mean over a constant number of values of c is recommended because the median is less sensitive to the resonance [24]. In place of random values, c values may also be chosen at a specific step increment in the interval (0, π ) as done in [15, 16]. In our investigation both of these techniques of choosing c have been used, and the similar results were obtained with both techniques. The 0-1 test method is sensitive to the units of observables and one may get different results based on the considered units. Thus, normalized time series of max values of pressure rise rate, PRRmax of 1000 consecutive combustion cycles is used as done by the authors [15, 16] for in-cylinder pressure time series. In Eqs. (1) and (2) x  ( j) has been used in place of x( j) which j)mean , x( j)mean is the mean of x( j) and x( j)std is the is given by x  ( j) = x( j)−x( x( j)std standard deviation of x( j). In the next parts of the paper the subscript c is not used for any of the parameters of 0-1 test for the sake of convenience.

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4 Results and Discussions First, the 0-1 test method is applied to the Logistic system, X n+1 = a X n (1 − X n ) to validate the written code for 0-1 test method, and the results obtained are in accordance with the previously obtained results in [15, 24]. The plots of translation variables, mean square displacement and its growth rate (Fig. 1) show that the nature of the system changes from the regular to chaotic as the control parameter (a) increases from 3.5 to 3.7. Next, the time series of PRRmax of 1000 consecutive cycles are analyzed using 0-1 test method to investigate the chaotic nature of in-cylinder combustion for RCCI engine. The results obtained from 0-1 test are shown in Fig. 2 for gasoline-diesel operated RCCI engine (GD) and Fig. 3 for methanol-diesel operated RCCI engine (MD) from top to bottom for different start of injection timing (SOI) before top dead center (bTDC) for the high reactivity fuel (diesel). It can be noticed from the figures that the p − q plots have a random track, M − t plots show the linear relation between M and t, and value of ‘k’ is close to unity for the all the random values of c of the used range. The rightmost parts of Figs. 2 and 3 signify the varying strength of chaotic behavior for both GD and MD RCCI combustion strategies along with the range of different SOI timings considered for direct injection of diesel. These results of 0-1 test indicate the nature of combustion system is chaotic for all the considered cases. The k median values obtained for all the SOI timings of high reactivity fuel for both GD and MD RCCI are shown in Fig. 4a. Although the value of k median always remains approximately equal to unity but there is a decreasing trend in its values for SOI timings up to 30° bTDC and an increasing trend afterward for both low reactivity fuels. According to 0-1 test, the chaotic behavior is found to be weakest when injection timing of high reactivity fuel is 30° bTDC. At very retarded and very

Fig. 1 0-1 test results from left to right p–q plane, k–c plot, M–t plot, for Logistic system for a parameter value of 3.5 (top) and 3.7 (bottom)

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Fig. 2 0-1 test results from left to right p–q plane, k–c plot, M–t plot, for PRRmax time series for GD RCCI for different injection timing varies from 10 to 55 CAD bTDC (top to bottom)

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Fig. 3 0-1 test results from left to right p–q plane, k–c plot, M–t plot, for PRRmax time series for MD RCCI for different injection timings varies from 10 to 40 CAD bTDC (top to bottom)

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Fig. 4 a k median ; b PRRmax [6] for different diesel SOI timings

advanced SOI timings with respect to TDC, the dynamics of the combustion system from cycle-to-cycle has strong chaotic characteristics and trajectories may diverge from the neighboring trajectories at a relatively higher rate. Now, the possible reasons for these varying dynamical behavior are discussed. It has been shown in [6, 33, 34] that when SOI timing of diesel is advanced then maximum value of rate of heat release, RoHRmax, and mean in-cylinder temperature decrease. The proportion of premixed combustion increases with an advance in injection timing due to the availability of more time for mixing leading to a more homogenous mixture and a reduction in local equivalence ratio, which lowers the mean in-cylinder temperature. Thus, the lower values of PRRmax is obtained. A similar trend is observed in PRRmax variations for different SOI timing for both gasoline and methanol fuels, Fig. 4b. Due to relatively higher octane number and heat of vaporization of methanol, combustion timing is retarded more than that with gasoline. Higher heat of vaporization results into reduction of rate of increase of mean cylinder temperature before ignition and thus a lower mean combustion temperature. Due to the combined effect of these factors comparatively lower values of PRRmax are observed in MD RCCI except when SOI time is very close to the TDC (because of relatively higher mean gas temperature in MD RCCI for this injection timing) [6, 35]. It may be concluded from Fig. 4 that strength of chaotic nature of cyclic combustion dynamics is dependent on PRRmax which is dependent on injection timing of diesel fuel and SOI time for diesel should be moderated if a controller is to be made based on the deterministic chaotic combustion.

5 Conclusions In this paper 0-1 test has been implemented on the normalized time series of PRRmax for 1000 consecutive cycles of a RCCI combustion engine operated with varying diesel SOI timings using two low reactivity fuels (gasoline and methanol). The test has successfully delivered the information regarding the dynamic characteristics of the combustion process varying from cycle-to-cycle. At all the considered diesel SOI timings, the nature of combustion is found to be chaotic. It is found that the strength of chaotic nature of combustion is directly related to diesel SOI timing and PRRmax . An interesting point to be noted is that irrespective of their different properties, both the low reactivity fuels have minimum value of PRRmax and k median

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at the same SOI timing of high reactivity fuel, i.e., at 30° bTDC. Strong chaotic behavior is found at highly retarded and advanced diesel injection timings for both fuels. Thus, in comparison to moderate SOI timing the engine combustion exhibits serious unrepeatable variations at these SOI timings. The results of this paper help in understanding the chaotic dynamics of combustion and can be utilized to design a controller using prior cycle information. Acknowledgements The authors gratefully acknowledge the research funding provided by DST-SERB, Government of India through Project No. ECR/2015/000177 titled “Soot Particle Number Emission Characterization and Investigation of Load Constraints in Reactivity Controlled Compression Ignition (RCCI) Engines.”

References 1. Heywood, J.B.: Internal Engine Combustion Fundamentals. McGraw-Hill, New York (1988) 2. Maurya R.K.: Characteristics and Control of Low Temperature Combustion Engines: Employing Gasoline, Ethanol and Methanol. Springer (2018). ISBN 978-3-319-68508-3 3. Paykani, A., Kakaee, A.H., Rahnama, P., Reitz, R.D.: Progress and recent trends in reactivitycontrolled compression ignition engines. Int. J. Engine Res. 17(5), 481–524 (2016) 4. Wang, Y., Zhu, Z., Yao, M., Li, T., Zhang, W., Zheng, Z.: An investigation into the RCCI engine operation under low load and its achievable operational range at different engine speeds. Energy Convers. Manag. 124, 399–413 (2016) 5. Molina, S., García, A., Pastor, J.M., Belarte, E., Balloul, I.: Operating range extension of RCCI combustion concept from low to full load in a heavy-duty engine. Appl. Energy 143, 211–227 (2015) 6. Saxena, M.R., Maurya, R.K.: Effect of Diesel Injection Timing on Peak Pressure Rise Rate and Combustion Stability in RCCI Engine (No. 2018-01-1731). SAE Technical Paper (2018) 7. Ozdor, N., Dulger, M., Sher, E.: An Experimental Study of the Cyclic Variability in SPARK Ignition Engines (No. 960611). SAE Technical Paper (1996) 8. Atkins, R.D.: An Introduction to Engine Testing and Development, vol. 344. SAE Technical Paper (2009) 9. Maurya, R.K.: Reciprocating Engine Combustion Diagnostics: In-Cylinder Pressure Measurement and Analysis. Springer (2019). ISBN 978-3-030-11953-9 10. Bogu´s, P., Merkisz, J.: Misfire detection of locomotive diesel engine by non-linear analysis. Mech. Syst. Signal Process. 19(4), 881–899 (2005) 11. Sen, A.K., Longwic, R., Litak, G., Gorski, K.: Analysis of cycle-to-cycle pressure oscillations in a diesel engine. Mech. Syst. Signal Process. 22(2), 362–373 (2008) 12. Yang, L.P., Ding, S.L., Litak, G., Song, E.Z., Ma, X.Z.: Identification and quantification analysis of nonlinear dynamics properties of combustion instability in a diesel engine. Chaos 25(1), 013105 (2015) 13. Ding, S.L., Yang, L.P., Song, E.Z., Ma, XZ.: Investigations on In-Cylinder Pressure Cycleto-Cycle Variations in a Diesel Engine by Recurrence Analysis (No. 2015-01-0875). SAE Technical Paper (2015) 14. Maurya, R.K., Akhil, N.: Experimental Investigation on Effect of Compression Ratio, Injection Pressure and Engine Load on Cyclic Variations in Diesel Engine Using Wavelets (No. 201801-5007). SAE Technical Paper (2018) 15. Ding, S.L., Song, E.Z., Yang, L.P., Litak, G., Wang, Y.Y., Yao, C., Ma, X.Z.: Analysis of chaos in the combustion process of premixed natural gas engine. Appl. Therm. Eng. 121, 768–778 (2017)

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Investigating the Impact of Energy Use on Carbon Emissions: Evidence from a Non-parametric Panel Data Approach Barsha Nibedita

and Mohd Irfan

1 Introduction Since twenty-five percentage of the world’s population resides in South Asia, the region suffers severe environmental issues, in particular, climate change, air and water pollution, shortage of drinking water, land degradation, deforestation, and industrial waste [18]. Throughout the region, expansion in economic activities, higher energy consumption, population growth, and urbanization, have to lead to the degradation of environmental resources over the past few decades [10]. This has posed a serious challenge of maintaining growth with minimum adverse impacts on environmental resources. Therefore, the sustainability of environmental resources is extremely relevant for this region as it is very dynamic in terms of macroeconomic and demographic changes. However, at the same time, the region is also home to 40% of the world’s poor. It is estimated that the region will be inhabited by around 2.2 billion people before 2025 and with the increased population, environmental resources demand will rise as well as intensifies their uses to provide access to energy, land, food, and water for the burgeoning population. Increase in income due to unprecedented growth may further worsen the situation in this region. Increased demand for goods and services will create a challenge of supplying energy for production and domestic use with low carbon intensity. This has created a serious concern for India, Bangladesh, Pakistan, Nepal, and Sri Lanka, as these countries use conventional sources of energy to meet the growing energy demands, which emits carbon in the atmosphere. One may argue that policies for reducing carbon emissions may create instability in energy supply and as a result challenges the overall macroeconomic stability of these countries. Rapid urbanization and population growth are also considered as a determining factor for carbon emissions in South Asia [17]. The possible reasons are associated B. Nibedita (B) · M. Irfan Department of Management Studies, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_74

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with a reduction in forest cover area and widespread changes in land use due to the growing demand for fossil fuels, land, and timber. However, evidence also suggests that an increase in population density raises the concern about pollution abatement in the more densely populated areas and could be one of the factors responsible for the reduction in carbon emissions [5]. Further, relatively less stringent environmental protection laws of South Asian countries have provided an opportunity for migration of dirty industries from western countries to this region. Recognizing that balanced integration of economic, energy, and environmental issues is essential for sustainable development in South Asia, the researchers have turned their attention to evaluate the role of energy use in promoting carbon emissions. An extensive review of the recent literature suggests that an increase in energy use promotes the level of carbon emissions and the results are uniform in most of the South Asian countries [1, 4, 17, 20]. However, this relationship may vary with the type of pollutant used and econometric specification (parametric and non-parametric) adopted in the analysis [7, 13]. From an empirical point of view, the existing studies based on parametric approaches pre-define the functional form before estimating the regression model. Specifically, studies have used either quadratic or cubic type polynomial to examine the role of energy use in promoting carbon emissions [1, 4, 20]. This suggests that studies have ignored the fact that pollution-energy consumption nexus may have some other forms. In other words, it may be possible that the development and adoption of energy-efficient technologies could influence the rate of change in the level of carbon emissions with an increase in energy use. Therefore, under the evolving energy-efficient technologies, such approaches restrict the chances of discovering the true effect of energy use on carbon emissions. Hence, one can say that there may be some other functional forms for the effect of energy use on carbon emissions, unlike quadratic and cubic. Failure to recognize this fact in an empirical setting, the estimated results may provide inadequate implications for economic, energy, and environmental policies along with sustainable management of environmental resources. Therefore, in recent years, empirical researches focus on semi-parametric and non-parametric approaches to investigate the determining factors for emissions [6–8, 12, 17]. From a regional perspective, it may be argued that cross-border emissions pose a serious challenge to the region’s ecosystem and access to environmental resources. In particular, since carbon emissions do not respect national boundaries, an increase in the emissions in one country results in severe environmental, energy, and economic crises in neighbouring countries. For instance, strong convection currents can drive some of GHGs from South Asia plains to the Hindu Kush Himalayan region and result in delayed monsoon with changes in the weather pattern, rainfall anomalies, decrease in rainfall in plains, and increase in rainfall in mountains. Such imbalances to the South Asian ecosystem may generate floods in Indian rivers, namely the Ganges and Brahmaputra, which can pose a threat to food and freshwater security in Bangladesh. Similarly, a rise in average surface temperature due to carbon emissions in India and Pakistan leads to surface drying and intensity of droughts, which may further be extended to Bangladesh. In addition, to reduce carbon emissions from energy generation, construction of hydropower generation units on the rivers of Nepal could

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result in limited access to water resources for downstream countries, namely India, Bangladesh, and Pakistan. Despite the importance of upstream-downstream linkages of the ecosystem in South Asia, understanding the role of energy use in promoting the level of carbon emissions has been overlooked for the region. Thus, failure to recognize the regional dimension may results in inadequate measures to the sustainable management of environmental resources and hampers the future growth and development of the region. Overall, the regional dimension of access to environmental resources has put emphasis on integrating policies for carbon emissions abatement and managing trade-offs through coordination in economic, energy, and environmental policies across boundaries in South Asian countries. Therefore, analyzing the role of energy consumption in determining the level of emissions is of particular interest for both research and a policy perspective in South Asia. Given the above motivations, this study seeks to make a contribution to the debate on the role of energy consumption in increasing emissions in South Asia. This study utilizes a cross-country data covering the period 1978–2011 for India, Pakistan, Bangladesh, Nepal, and Sri Lanka. The dataset is extracted from the World Development Indicators database and a non-parametric approach is employed as the estimation method. Findings of this study provide a framework for understanding the effect of energy use on carbon emissions and give more insights into how environmental degradation will be mitigated through coordination of policies and managing the trade-offs across boundaries in the region. The remainder of the paper is organized as follows. The next section provides a brief review of the relevant studies. Section 2 discusses the data and variables used in this study. Section 3 discusses the non-parametric panel data approach to estimate the impact of energy use on carbon emissions. Section 4 presents the results of econometric estimation along with their robustness checks. Finally, the conclusions are presented in the last section.

2 Literature Review—In Brief Several empirical studies have been conducted in the past for individual countries of South Asia to investigate the role of energy use in determining the level of carbon emissions. For instance, Alam et al. [3] examined the energy-emissions nexus by applying the causality test and generalized impulse response function for India. Using annual time series covering the period 1971–2006, the results indicate that causality is present in the long-run and short-run both among the variables. Kanjilal and Ghosh [20] studied the relationship between carbon emissions and energy use for India and the study found a positive relationship for the period 1971 to 2008. Similarly, Ahmed and Long [1] studied the effect of energy use on carbon emissions for Pakistan during the period 1971 to 2008. Using autoregressive distributed lag (ARDL) bounds methodology, they concluded that energy use is one of the determinants of carbon emissions. Similarly, Ghosh et al. [15] have estimated a cointegrating relationship between carbon emissions and energy use for Bangladesh. Their study concluded that

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for the study period 1971–2011, energy use and carbon emissions share a long-run relationship. Few empirical studies have also used cross-country data of South Asian countries to assess the role of energy use in determining the level of carbon emissions. For example, using a non-parametric approach, Irfan and Shaw [17] assessed the impact of energy use on carbon emissions for South Asian countries during the period 1978– 2011. The non-parametric approach includes country-specific as well as time-specific fixed effects in the estimation procedure. The results suggested that more energy use results in more carbon emissions in South Asian countries. Ahmed et al. [2] in a recent study have explored the emission-energy nexus for South Asian countries by utilizing annual time series for the period 1971 to 2013. The result suggested that environmental quality is negatively related to energy consumption in South Asian countries. Although energy consumption has been identified as a leading factor for carbon emissions for South Asian countries, it may be argued that estimated results could be biased because of country-specific characteristics and selection of time period for the analysis. More specifically, it cannot be ruled out that the econometric issues such as small sample size and omitted variable bias could have influenced the estimated results [19]. A number of other factors that were overlooked in the existing studies are associated with the level of adoption of energy-efficient technologies and stringent environmental norms in the respective countries [22]. Since countries of the region have different income distribution and levels of awareness for environmental protection, countries may experience different trajectories in their level of carbon emissions. To overcome such challenges, few empirical studies have utilized a cross-country panel data and non-parametric approaches to assess the impact of energy use in determining the level of carbon dioxide emissions [6–8, 12]. The main advantages of using panel data in this regard come from the possibility of controlling the fixed effects, more specifically, incorporating the country and time effects in the econometric specification along with an increase in sample size. Similarly, the nonparametric approach does not require restrictive assumptions for estimation and thus allows a researcher to avoid any specification issue in the econometric model [6]. The suggestion for implementing a non-parametric panel data approach in carbon emissions studies was first proposed in Azomahou et al. [6]. Their study employs a non-parametric econometric model to investigate the factors influencing the level of carbon emissions for a panel of countries including developed and developing countries. The estimation procedure for their non-parametric model relies on local linear kernel regression. Their estimated results suggested that carbon emissions increase with an increase in income and energy consumption. This new approach has recently attracted the attention of researchers and few studies have employed this methodology in their estimation procedure [12, 17]. In both the studies, the role of energy use in determining the level of carbon emissions was examined by using a non-parametric panel data econometric model and the estimation was carried out by using an iterative method, in particular, the backfitting algorithm. The present study thus relies on a panel data of South Asian countries and non-parametric approach to

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assess the role of energy use in determining the level of carbon emissions with a set of control variables such as income, trade, and population density.

3 Data, Variables, and Econometric Model The dataset covers five South Asian countries and it was compiled from the World Development Indicators database. The annual time series for the study period 1978– 2011 include variables such as energy use, income, exports-imports, and population density for India, Nepal, Bangladesh, Pakistan, and Sri Lanka. The set of variables are carbon emissions (in metric tons per capita), energy use (in kg of oil), income (in GDP per capita), trade (measured in terms of trade openness) and population density (measured as a ratio of the total population to land area). The summary statistics of all variables are presented in Table 1 and expressed without taking the natural logarithm transformation. Five variables are there namely, carbon emissions (co2), income (gdpp), energy use (eu), population density (pd), and trade openness (op). The total number of observations (Obs) in our sample is 170. The range of co2 variable is 0.0200–1.7000 having a mean value of 0.463 and the standard deviation (Std. Dev.) is 0.377. Similarly, the range of gdpp variable is 185.13–1724.8, having a mean value and a standard deviation of 535.5 and 296.8, respectively. The mean value of variable eu is 332.1, the standard deviation is 118.1, and range is 97.120–613.72. Likewise, in the case of variables pd and op, mean value and standard deviation are 362.1, 39.84 and 293.2, 19.50, respectively. To assess the role of energy use in determining the level of carbon dioxide emissions, the empirical specification in this study relies on the environmental Kuznets Curve (EKC) estimation framework [14]. More specifically, carbon emissions are specified as a function of income and energy use with some control variables. In a non-parametric setting, the functional form of the impact of each explanatory variable on carbon emissions is unspecified, or in other words, there is no pre-defined quadratic or cubic function form. Thus, the econometric model in a non-parametric panel data setting can be expressed as follows:       lco2it = α0 + vi + γt + f lgdppit + f (leuit ) + f lopit + f lpdit + eit Table 1 Descriptive statistics of all variables Panel

Variable

Obs

Mean

Std. Dev.

Min

Max

co2

170

0.463

0.377

0.0200

1.7000

Gddp

170

535.5

296.8

185.13

1724.8

Eu

170

332.1

118.1

97.120

613.72

Pd

170

362.1

293.2

96.160

1174.3

Op

170

39.84

19.50

12.010

88.640

(1)

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where prefix l in the variable’s nomenclature stands for natural logarithm transformation, vi, and γ t are fixed effects specific to country and time, respectively. The unspecified function (.) is required to be identified from the data itself for each explanatory variable. The estimation procedure involves iterative method where the impact of one explanatory variable on carbon emissions is estimated by assuming its independence with other explanatory variables [16]. In particular, the estimation procedure involves a backfitting algorithm that employs a scatter plot smoothing technique until the convergence is achieved [23]. The non-parametric econometric model was estimated by using the gamm function, specifically designed for mixed-effects modelling in R software.

4 Results and Discussion The estimated results of our non-parametric econometric model are presented in Table 2. The estimated results of two models—Model 1 (without fixed effects) and Model 2 (with fixed effects) are reported in columns 2 and 3, respectively, in Table 2. The above table shows that Model 1 is without country and time-specific fixed effects and Model 2 is with country and time-specific fixed effects. Edf is estimated degrees of freedom and Ref.df is original degrees of freedom before deductions for smoothing fits (F is F statistic and *** significant at 1% level). The results suggest that although both models have a very high value of adjusted R-square, Model 2 provides a better fit in our sample. Therefore, the estimated results of Model 2 are depicted in the form of graphical representation in Fig. 1 and further utilized for the interpretation purpose. The s(leu) in Fig. 1 shows deviance from the mean value of lco2 with respect to a change in leu and similar interpretations can be made for other explanatory variables in our econometric model. The predicted values of the dependent variable as a function of each independent variable are expressed in the form of solid lines and the estimated standard errors are shown as a shaded area. Table 2 Estimated results of non-parametric panel model fit Variable

Model 1

Model 2

Estimate

Std. Err.

t-Value

Estimate

Std. Err.

t-Value

Constant

−1.181

0.0114

−103.3***

−0.946

0.1329

−7.12***

Smooth terms

Edf

Ref.df

F

Edf

Ref.df

F

s(lgdpp)

8.362

8.853

156.5***

6.365

6.365

22.62***

s(leu)

7.986

8.685

9.190***

1.000

1.000

16.12***

s(lop)

6.120

7.431

21.99***

3.863

3.863

9.110***

s(lpd)

8.670

8.927

12.30***

0.999

0.999

1.850

R-square (adj.)

0.978

0.991

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Fig. 1 Plots of relationship of response variable with lgdpp, leu, lop, and lpd

The plots of the relationship show several interesting results. It is evident that energy consumption is a significant factor in determining the level of carbon dioxide emissions in countries. The plot between s(leu) versus leu values (in Fig. 1) shows a monotonic increasing impact of energy consumption on carbon emissions. This finding suggests that with more energy consumption there are more carbon emissions in the countries of the South Asian region. This is possible because most of the countries in this region—except Bhutan, mainly use conventional energy sources to meet their growing energy demand. It is suggested that an improvement in renewable energy sources, energy consumption structure, and energy-saving techniques can help to reduce the carbon emissions in South Asian countries and subsequently motivates the sustainable energy system. In this regard, the government in South Asian countries should provide renewable and energy-efficient technologies to mitigate carbon emissions. To get a more sustainable energy system, improving energy efficiency is inevitable which plays a substantial role in balancing the demand and supply of energy. Energy efficiency will mitigate the effect of energy consumption as well as carbon emission, only if the demand for energy use remains stable. Thus, demand-side management plays a greater role while carrying out a low carbon strategy and also results in energy savings.

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With regard to some control variables, it is apparent that the lco2 has a nonmonotonic relationship with lgdpp. The existence of the EKC type effect of income on carbon emissions is evident; however, it is only up to a threshold level of income. Beyond this level, more income is leading to more carbon emissions in the countries. This finding submits that the relationship between income and emissions is approximately like an N-shaped curve rather than a commonly discovered inverted U-shaped curve. One plausible explanation of such a relationship is that as income increases, there is an expansion of economic activities with more production and exploitation of resources with more waste generation and pollution. Therefore, emissions increase with an increase in income—termed as scale effect. However, a rise in income also calls for better environmental quality as the income elasticity is positive between emissions and income or environmental quality is a normal good [11, 14]. Thus, emissions as negative externalities must be internalized in the costs of production, which involves the substitution of obsolete, energy inefficient, and dirty technology with more advanced and energy-efficient technology that complies with the stringent pollution abatement policies [9]. Such kinds of internalization lead to a decrease in emissions with a higher level of income. However, after reaching the lowest level of emissions, more increase in income causes the scale effect to become so large that the emissions cannot be counterbalanced with current structural and technological changes in the country [9]. In other words, in this stage, as the internalization process is complete, the scale effect becomes active in the internalization process of negative externalities as well [21]. Moreover, De Bruyn [11] suggested that when technological opportunities for reducing carbon emissions ceases or becomes expensive, then there is again rise in emissions along with a higher level of income. The plot of s(lop) versus lop confirms the effect of trade on carbon emissions and it is found to be positive. However, this impact is visible when the level of trade openness is fairly high. This finding is justifying the applicability of the pollution haven hypothesis [22]. South Asian countries are somewhat relying on trade for their growth and development and therefore, openness has increased the economic activities in the region with more waste generation and pollution. Another reason may be associated with the relatively less stringent environmental protection laws in South Asian countries, as such dirty industries from developed countries have migrated to this region. The impact of population density on carbon emissions are shown in the plot of s(lpd) versus lpd. Although the plot suggests a decrease in carbon emissions with respect to an increase in population density, the smooth term of lop is found to be statistically insignificant (see Table 2). Therefore, drawing any conclusion for the impact of population density on emissions seems inappropriate.

5 Concluding Remarks The present study investigates the role of energy use in determining the level of carbon emissions for five South Asian countries—India, Pakistan, Bangladesh, Nepal, and Sri Lanka. The empirical analysis estimates a non-parametric panel data model using

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cross-country data covering the period 1978–2011. The study reveals that the effect of energy use in determining the level of carbon emissions is significant and it is found to be linear as well as positive in nature. The results also suggest that the effect of income is significant and it is found to be an N-shaped type for the countries. In the beginning, trade liberalization has not affected carbon emissions but subsequently, it has shown a considerable impact on emissions. In contrast, the effect of population density on carbon emissions has been found statistically insignificant. The present study puts forward some empirical insights on the energy use and its effect on carbon emissions for the South Asian region, which has broad policy implications for the region as a whole.

References 1. Ahmed, K., Long, W.: Environmental Kuznets curve and Pakistan: an empirical analysis. Procedia Econ. Finance 1, 4–13 (2012). https://doi.org/10.1016/S2212-5671(12)00003-2 2. Ahmed, K., Rehman, M.U., Ozturk, I.: What drives carbon dioxide emissions in the long-run? Evidence from selected South Asian countries. Renew. Sustain. Energy Rev. 70, 1142–1153 (2017). https://doi.org/10.1016/j.rser.2016.12.018 3. Alam, M.J., Begum, I.A., Buysse, J., Rahman, S., Huylenbroeck, G.V.: Dynamic modeling of causal relationship between energy consumption, CO2 emissions and economic growth in India. Renew. Sustain. Energy Rev. 15(6), 3243–3251 (2011). https://doi.org/10.1016/j.rser. 2011.04.029 4. Al-Mulali, U., Weng-Wai, C., Sheau-Ting, L., Mohammed, A.H.: Investigating the environmental Kuznets curve (EKC) hypothesis by utilizing the ecological footprint as an indicator of environmental degradation. Ecol. Ind. 48, 315–323 (2015). https://doi.org/10.1016/j.ecolind. 2014.08.029 5. Apergis, N., Ozturk, I.: Testing environmental Kuznets curve hypothesis in Asian countries. Ecol. Ind. 52, 16–22 (2015). https://doi.org/10.1016/j.ecolind.2014.11.026 6. Azomahou, T., Laisney, F., Van, P.N.: Economic development and CO2 emissions: a nonparametric panel approach. J. Public Econ. 90(6–7), 1347–1363 (2006). https://doi.org/10. 1016/j.jpubeco.2005.09.005 7. Baiocchi, G., Di Falco, S.: Investigating the shape of the EKC: a nonparametric approach. FEEM 9, 1–25 (2001). https://doi.org/10.2139/ssrn.286695 8. Bertinelli, L., Strobl, E.: The environmental Kuznets curve semi-parametrically revisited. Econ. Lett. 88(3), 350–357 (2005). https://doi.org/10.1016/j.econlet.2005.03.004 9. Borghesi, S.: The Environmental Kuznets Curve: A Survey of the Literature. FEEM Working Paper No. 85, pp. 1–29 (1999). https://doi.org/10.2139/ssrn.200556 10. Bowonder, B.: Environmental problems in developing countries. Prog. Phys. Geogr. 11(2), 246–259 (1987). https://doi.org/10.1177/030913338701100204 11. De Bruyn, S.M.: Economic Growth and the Environment: An Empirical Analysis. Springer, Dordrecht (2000). https://doi.org/10.1007/978-94-011-4068-3_5 12. Chen, L., Chen, S.: The estimation of environmental Kuznets curve in China: non-parametric panel approach. Comput. Econ. 46(3), 405–420 (2015). https://doi.org/10.1007/s10614-0159486-7 13. Dinda, S., Coondoo, D., Pal, M.: Air quality and economic growth: an empirical study. Ecol. Econ. 34(3), 409–423 (2000). https://doi.org/10.1016/S0921-8009(00)00179-8 14. Dinda, S.: Environmental Kuznets curve hypothesis: a survey. Ecol. Econ. 49(4), 431–455 (2004). https://doi.org/10.1016/j.ecolecon.2004.02.011

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15. Ghosh, B.C., Alam, K.J., Osmani, MdAG: Economic growth, CO2 emissions and energy consumption: the case of Bangladesh. Int. J. Bus. Econ. Res. 3(6), 220–227 (2014). https://doi. org/10.11648/j.ijber.20140306.13 16. Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman and Hall, London (1990) 17. Irfan, M., Shaw, K.: Modeling the effects of energy consumption and urbanization on environmental pollution in South Asian countries: a non-parametric panel approach. Qual. Quant. 51(1), 65–78 (2017). https://doi.org/10.1007/s11135-015-0294-x 18. Jha, U.C.: Environmental issues and SAARC. Econ. Polit. Wkly. 39, 1666–1671 (2004) 19. Kaika, D., Zervas, E.: The environmental Kuznets curve (EKC) theory—Part A: Concept, causes and the CO2 emissions case. Energy Policy 62, 1392–1402 (2013). https://doi.org/10. 1016/j.enpol.2013.07.131 20. Kanjilal, K., Ghosh, S.: Environmental Kuznet’s curve for India: evidence from tests for cointegration with unknown structural breaks. Energy Policy 56, 509–515 (2013). https://doi.org/ 10.1016/j.enpol.2013.01.015 21. Lieb, C.M.: The environmental Kuznets curve: a survey of the empirical evidence and of possible causes. Discussion Paper Series, No. 391, University of Heidelberg, Department of Economics, Heidelberg (2003). http://hdl.handle.net/10419/127208. Accessed 25.07.16 22. Panayotou, T.: Demystifying the environmental Kuznets curve: turning a black box into a policy tool. Environ. Dev. Econ. 2(4), 465–484 (1997). https://doi.org/10.1017/S1355770X97000259 23. Qian, S.S.: Environmental and Ecological Statistics with R, 2nd edn. CRC Press, Florida (2010)

Studies on the Use of Thorium in PWR Devesh Raj and Umasankari Kannan

1 Introduction The use of thorium in nuclear fuel cycle has been studied [1–4] and found to have an advantage over the Uranium-Plutonium cycle with regards to parameters such as proliferation resistance, limiting the production of Transuranic and Minor Actinides. The loading of thorium in LWRs was also part of these studies. A preliminary study was taken up to investigate the effect of thorium loading in existing western PWR fuel assemblies. First, the burnup dependent characteristic of the nominal 17 × 17 PWR FA, with the fuel rods enriched in 4.9% weight percent of U235 was obtained as the reference. Then the FA having alternative fuel, thorium mixed with LEU or reactorgrade Plutonium to have comparable fissile content as the reference, was studied. This paper has four sections. Section 1 is this introduction. Section 2 describes the 17 × 17 fuel assembly geometry and the fuel compositions used in this study. Section 3 describes the result and discussion in four subsections. Section 3.1 gives reactivity of the assemblies and its variation with the burnup. Section 3.2 discusses the neutron flux spectrum in the fuel assemblies. Section 3.3 gives a variation of inventory of chief actinides in the three variants of the assemblies. Section 3.4 gives the burnup dependent pin power distribution in the three variants of assembly. Section 4 has the conclusion of this work.

D. Raj (B) · U. Kannan Reactor Physics Design Division, Bhabha Atomic Research Centre, Mumbai 400085, India e-mail: [email protected] Homi Bhabha National Institute, Mumbai, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_75

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2 Fuel Assembly Geometry and Fuel Compositions 2.1 Fuel Assembly Design The Westinghouse 17 × 17 FA has 289 lattice locations with a square pitch of 1.26 cm. Out of 289 lattice locations 264 locations are occupied by the fuel pins. The thimbles for the control rods are provided at 24 lattice locations. One lattice location is usually left for the in-core instrumentation, which can be self-powered neutron detector, temperature sensor or a combination of these. The dimensions of 17 × 17 FA used in this work are given in Table 1 and depicted schematically in Fig. 1 [5, 6]. In general, the fuel pins in a fuel assembly has heterogeneous composition resulting in enrichment profile which can be radial, axial or both. In this work fuel pins of uniform composition have been considered. The two modern western PWRs, AP1000 [5] and EPR [6], despite the marked difference in design, operation and features, are envisaged to use standard Westinghouse 17 × 17 fuel assemblies albeit with small differences in fuel pellet diameter, fuel clad gap and the clad thickness. Table 1 17 × 17 fuel assembly design

Fuel pin pitch

12.600 mm

Fuel rod clad OD

9.512 mm

Fuel clad gap

0.187 mm

Fuel clad ID

8.941 mm

Fuel meat OD

8.190 mm

Fuel assembly pitch

215.046 mm

Fig. 1 17 × 17 fuel assembly design

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Table 2 Initial composition of fuel in 17 × 17 assemblies (1/barn-cm) FA-I

FA-II

FA-III

Fuel

4.9% enriched UO2

Th + LEU (19.75% U235)

Th + Pu

Th232

–

1.5801E−02

1.8966E−02

U235

1.0700E−03

1.2226E−03

–

U238

2.0505E−02

4.9050E−03

–

Pu238

–

–

1.2086E−04

Pu239

–

–

1.5044E−03

Pu240

–

–

6.8913E−04

Pu241

–

–

3.5806E−04

Pu242

–

–

2.8228E−04

Am241

–

–

4.4756E−05

Table 3 Plutonium composition [6] Pu238

Pu239

Pu240

Pu241

Pu242

Am241

4.0%

50.0%

23.0%

12.0%

9.5%

1.5%

2.2 Fuel Composition in Reference and Thorium FAs A uniform UO2 fuel with 4.9 wt% enriched in U235 has been used for the reference fuel assembly. Such an approach has been adopted to bring out the relative differences in burnup characteristics between the reference fuel assembly and the fuel assembly with the modified fuel, namely, ThO2-UO2 MOX and ThO2-PuO2 MOX. Several fuel assembly burnup calculations with the Th-LEU and Th–Pu was done to arrive at the equivalent composition of Th-LEU and Th–Pu that would give a discharge burnup of about 60 MWD/kgHM in three batch fuel management scheme. The fuel heavy element composition of the three variants thus formed is given in Table 2. The Plutonium vector composition was taken from Ref. [6] is given in Table 3. The plutonium composition is given in Ref. [6] has been used which counts Am241 with Plutonium. It may be noted that Ref. [6] erroneously gives Pu240 content as 53%. The correct value is 23% which has been used in current work.

3 Result and Discussion 3.1 Fuel Assembly Burnup The burnup dependent fuel assembly calculation was done with square lattice spectrum and burnup code SUPERB [7, 8]. The ENDF/B-VII.1 based WIMS format

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Fig. 2 The burnup dependent reactivity of fuel assemblies

nuclear data library released by IAEA [9] has been used for the calculation. The fuel composition for the three FA types has been chosen to yield a discharge burnup of 60 MWD/kgHM. A three batch fuel management has been considered to estimate the discharge burnup. The burnup dependent infinite multiplication factor K-infinity of the fuel assemblies are given in Fig. 2. The K-infinity of the three assembly types at zero burnup, 30 MWD/kgHM and at 60 MWD/kgHM is given in Table 4. The reactivity of the reference fuel assembly (FA-I) is highest at the beginning compared to both the thorium bearing assemblies (FA-II & FA-III). The reactivity of the thorium bearing FAs are lower at the beginning and it falls off slowly with the burnup. At the zero burnup the reactivity of reference FA-I is about 61.8 mk higher than the FA-II and about 123 mk higher than the FA-III. This indicates the requirement of initial reactivity suppression in core constituted with FA-I will be higher. The reactivity of FA-II is about 61 mk less than the FA-I. Therefore, in case of PWR core constituted with FA-II, less reactivity shall be required to be suppressed by means of the burnable absorber and the dissolved boron. The reactivity of thorium Table 4 K-infinity of three fuel types at a different burnup

Burnup

FA-I

FA-II

FA-III

0.0 MWD/kgHM 30.0 MWD/kgHM

1.37597

1.26804

1.17585

1.09913

1.08478

1.07208

60.0 MWD/kgHM

0.90908

0.95115

0.99399

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bearing FAs take over that of the reference FA at burnup of about 35 MWD/kgHM. The thorium bearing FAs, compared to FA-I, are less reactive at BOC but more reactive at end of cycle (EOC).

3.2 Neutron Flux Spectrum in Fuel Assemblies The normalized neutron flux spectrum (dφ/dE) at the beginning and at the burnup of 60 MWD/kgHM is plotted in Figs. 3 and 4. The reference assembly (FA-I) has the most soft spectrum from beginning of cycle (BOC) to end of cycle (EOC) at 60 MWD/kgHM. The neutron flux spectrum in thorium bearing FAs (FA-II & FA-III) remains harder during the burnup. The softest spectrum in the reference FA-I is the reason for it being most reactive at the beginning despite having equivalent fissile loading as in FA-II and FA-III. The use of thorium with LEU in FA-II and with Pu in FA-III results in a relatively harder spectrum. The FA-III constituted with the Th-PuMOX is the least reactive owing to harder spectrum than the other two configurations. The relatively harder spectrums that prevail in thorium bearing FAs are the reason for the flatter reactivity-burnup response.

Fig. 3 Neutron spectrum at the beginning of burnup

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Fig. 4 Neutron spectrum at the beginning of burnup

3.3 Burnup-Dependent Composition of the Fuel Assemblies In case of reference FA-I the initial content of U235 falls down to 7.3 g/kgHM at the end of burnup from initial value of 49 g/kgHM. The total plutonium (with Am-241) at the end of burnup is 12.75 g/kgHM. The Pu239 increases from 0.0 g/kg at the beginning to 6.1 g/kg at the end of burnup. The burnup dependent isotope composition is given in Fig. 5 and the Pu vector at the end of burnup for the reference FA-I is given in Table 5. The fissile component in the discharged plutonium depends on the burnup. At relatively higher burnup the fertile component of the plutonium increases at the expense of fissile component. That is the reason for the difference in plutonium composition in Table 5 and in Table 3. The fissile content in reference FA-I at the end of burnup at 60 MWD/kgHM is 7.34 g/kgHM of U235 and 8.15 g/kgHM of fissile Pu. Therefore, for the reference FA-I about 15.5 g/kgHM fissile is discharged after a burnup of 60 MWD/kgHM. In case of the FA-II, constituted with thorium and 19.75% LEU the main fissile isotope is U235. With burnup, the other fissile isotopes, primarily U233, Pu239 get produced. The number density of three main fissile isotopes U235, U233 and Pu239 are plotted in Fig. 6. The Initial loading of 56.11 g/kgHM U235 falls down to 10.72 g/kgHM at the end of burnup. The content of Pu isotopes rises from 0.0 to 5.08 g/kgHM. In contrast to reference FA-I the total Pu generated in case of FA-II is only 39% of the earlier. Therefore, for producing the same amount of energy use of LEU with the thorium reduces the Pu production by more than 60%. The cost of such reduction is the slightly increased

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Fig. 5 U235, Pu239 and Pu241 number density in reference FA-I Table 5 End of the burnup plutonium vector in reference FA-I Nuclide

94-Pu-238

94-Pu-239

94-Pu-240

94-Pu-241

g/kgHM

3.87E−01

6.17E+00

3.10E+00

1.93E+00

1.10E+00

7.04E−02

Percent (%)

3.03E+00

4.84E+01

2.43E+01

1.51E+01

8.60E+00

5.52E−01

Fig. 6 Pa233, U233, U235 and Pu239 number density in FA-II

94-Pu-242

95-Am-241

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Fig. 7 U233, Pu239, Pu241 and Am241 number density in FA-III

Table 6 Plutonium composition (g/kgHM) at the beginning and at the end of burnup in FA-III Burnup (MWD/kg) 94-Pu-238 94-Pu-239 94-Pu-240 94-Pu-241 94-Pu-242 95-Am-241 0.0

5.61

70.23

32.30

16.85

13.34

2.10

60.0

5.73

20.10

25.41

14.84

13.50

2.30

fissile loading at the beginning, 56.11 g/kg instead of 49 g/kg. The fissile Pu at the end of burnup is 3.3 g/kgHM. Thus, in case of FA-II the net fissile inventory falls down from 56.11 to 14.07 g/kgHM. In case of FA-III the main fissile isotopes are Pu239, Pu241 and Am-241. The burnup dependent number density of these nuclides together with the bred U233 is are given in Fig. 7. The change in Pu composition in case of FA-III is given in Table 6. In FA-III the initial Plutonium loading is 140.47 g/kgHM with 63.5% fissile. The Plutonium content of the discharged fuel is 81.90 g/kgHM with 45.48% fissile. The content of bred U233 in the discharged fuel in FA-III is 15.27 g/kgHM.

3.4 Power Distribution in Fuel Assemblies Since the fuel assembly has four-fold reflective symmetry, the power distribution is given for the first quadrant pins (9 × 9) only. The fuel pins in thorium bearing fuel assemblies show slightly higher pin power than the reference assembly. The local peaking factors at the beginning for the FA-I, FA-II and FA-III are 1.058, 1.064, 1.094, respectively. At the end of burnup they are 1.023, 1.029 and 1.050, respectively. The

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Fig. 8 Pin power distribution at 0.0, 30 and at 60 MWD/kgHM for FA-I

Fig. 9 Pin power distribution at 0.0, 30 and at 60 MWD/kgHM for FA-II

Power distribution at the beginning, at 30 MWD/kgHM and at 60 MWD/kgHM for FA-I, FA-II, FA-III is given in Figs. 8, 9, and 10 respectively. The power distribution suggests in case of FA-III there is a rise in local pin power (4–5%) as compared to power distribution in case of FA-I.

4 Conclusion The use of thorium in existing PWR is possible without any change in the current design of the fuel assemblies. The use of thorium in once through PWR fuel cycle without reprocessing is advisable, as it will reduce the production of Pu and MA (TRU). This will result in reduction in high-level waste and reduced TRU burden

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Fig. 10 Pin power distribution at 0.0, 30 and at 60 MWD/kgHM for FA-II

on repositories. Use of thorium will also bring in proliferation resistance due to inevitable production of U232. The use of thorium in the fuel cycle without reprocessing is appropriate because the reprocessing of ThO2 fuel is relatively difficult than that of UO2 fuel. The consequence of the use of thorium on kinetic parameters and consequence thereof needs to be studied in future work.

References 1. Safety and Regulatory Issues of the Thorium Fuel Cycle, NUREG/CR-7176, ORNL/TM2013/543 2. Thorium Fuel Utilization: OPTIONS and Trends, IAEA-TECDOC-1319 3. Thorium Based Fuel Options for the Generation of Electricity: Developments in the 1990s, IAEA-TECDOC-115 4. Introduction of Thorium in the Nuclear Fuel Cycle, NEA-7224, OECD 2015 5. Westinghouse AP1000 Design Control Document Rev. 19—Tier 2 Chapter 4—Reactor— Section 4.3 Nuclear Design, ML11171A445 (2011) 6. Sengler, G., et al.: EPR core design. Nucl. Eng. Des. 187, 79–119 (1999) 7. Jagannathan, V., et al.: A fast and reliable calculation model for BWR fuel assembly burnup analyses. Ann. Nucl. Energy 7(12), 641–654 (1980) 8. Jagannathan, V., Jain, R.P.: A Guide to the Use of SUPERB Code, BARC-1198 (1983) 9. IAEA, WLUP. https://www-nds.iaea.org/wimsd/downloads2.htm

Coaxial Thermal Probe for High-Frequency Periodic Response in an IC Engine Test Rig Anil Kumar Rout, Santosh Kumar Hotta, Niranjan Sahoo, Pankaj Kalita, and Vinayak Kulkarni

1 Introduction Transient temperature measurement is an essential objective in many engineering systems and subsystems for the design of efficient products. Few capable sensors are having the potential of capturing transient temperatures. One among such sensors is Coaxial Surface Junction Thermocouple (CSJT). Due to small response time (~ few microsecond), CSJT is able to capture transient phenomena in harsh environments like shock tubes, shock tunnels, etc. The temperature history in the internal combustion chamber, the exhaust gas temperature, and the material temperature on the piston are some important measurable parameters required for performance improvement of IC engines. The measurement of internal combustion chamber temperature is one of the important aspects of measurement since decades which was accomplished with the help of sound waves. Inside the test chamber, a transient sound pulse is generated and the interval between transmitted and detected signal is analyzed for the transient time [1]. Similarly, the cylinder average gas temperature can be measured simply by extending a thin metallic wire to the gas cylinder where the wire temperature is the same as the gas temperature [2]. The study can be extended to heat transfer measurements and surface heat flux measurements through fast response thermocouples of type K, E, and J where the transient phenomena can be recorded [3, 4]. The fast response of the thermocouples can help in better capturing the transient heat transfer phenomena in the engine walls which will help in modeling and design of the engine cylinder [5]. According to thermodynamic analysis, the state of both source and sink helps in a better understanding of the engine phenomena. Therefore, the exhaust temperature measurement and its variation is an important aspect in the study of IC engine features [6–8]. There exist a certain relationship between the exhaust temperature and engine cylinder temperature in long-term and short-term A. K. Rout (B) · S. K. Hotta · N. Sahoo · P. Kalita · V. Kulkarni Indian Institute of Technology Guwahati, Guwahati, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_76

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unsteady heat transfer process which can add to the understanding of many combustion phenomena happening inside the cylinder, the cooling aspects in the cylinder, the engine thermodynamics during engine warm-up, etc. [9, 10]. The variation of surface temperature and heat flux in the exhaust manifold follows the same trend as in the engine cylinder head, however, the trend differs if the measurement position is away from the exhaust valve [8]. The literature suggests the efficient use of coaxial thermal probes in the measurement of transient phenomena at a place 100 mm away from the exhaust manifold. Therefore, in the present study, a calibration set up is designed by using the exhaust gas of a SI engine as a source of periodic heat transfer to a K-type CSJT thermal probe fabricated in-house. A K-type thermocouple can measure temperature up to 1000 °C conveniently and is most widely used for temperature measurement. Surface topography of the thermal probe is studied under optically zoomed environment generated by FESEM technique and the composition of the thermo-elements has been evaluated through Electron Discharge X-ray (EDX) methodology. The cycle times for the completion of each cycle in two different RPMs have been calculated experimentally and compared with the analytical value. Such studies are desirable to understand the expected operation of an engine or its smooth working. Mounting of the sensor in the exhaust does not alter the combustion chamber design, hence it can be thought of as a routine sensor for engine inspection.

2 Fabrication of CSJT In order to study the response characteristics, a K-type CSJT has been fabricated in laboratory-scale which is a combination of two thermo-elements made of different alloys; Chromel and Alumel. Bare alumel element (0.91 mm diameter, 15 mm length) is placed coaxially and concentrically in the annulus made in chromel element (3.25 mm diameter, 10 mm length) with a small insulation (~0.02 mm) thickness in between them. The insulation (Epoxy) avoids an electrical connection between the two thermo-elements along the entire sensor length leaving the sensing surface un-insulated. The connection between the two thermo-elements is created at the sensing surface through abrasion technique (using sandpaper of grit size 100) which forms a junction in the form of cold weld in between them. Chromel and constantan wires of 0.25 mm diameter are spot welded and taken out as lead wire for further instrumentation purposes. More information regarding fabrication can be obtained from the literature [11–13]. The schematic of the fabricated probe is mentioned in Fig. 1.

3 Microstructural Analysis and Chemical Characterization The junction between two thermo-elements on the surface of CSJT is an important parameter for small response time of the thermocouple. Therefore, to observe the

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Fig. 1 Fabrication of CSJT: a schematic of the sensor; b fabricated sensor with lead wire; c packaged sensor

surface morphology, microstructural analysis of the CSJT has been carried out by placing the sensor under a zooming environment created by Field Emission Scanning Electron Microscope (FESEM). The junction thickness created between two thermoelements through abrasion technique is measured to be varying between 19.85 and 26.67 µm and it confirms the connection between the two elements (Fig. 2). An Energy Dispersive X-ray (EDX) technique has been used for the current study to qualitatively identify the composition of the thermo-elements used in the current study. The EDX analysis has confirmed the presence of chromel and alumel composition over the complete surface of the thermocouples. The major component in both the element is nickel and other elements are in small percentages. The composition varies in different samples. For the present case, chromel is having a composition of Chromel

A

26.5 μm

Alumel Chromel

26.67μm Junction

A

Alumel

25.14 μm Junction

19.85 μm 23.59 μm

(a) Fig. 2 a Polished sensing surface of the probe; b junction thickness

(b)

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Fig. 3 Configuration of thermo-elements a chromel; b alumel

87.1% Ni, 10.1% Cr, and 2.8% Si and alumel contains 76.3% Nickel, 10.3% carbon, 7.7% oxygen, 3.7% Si, and Mn and Zn in tracer amount. The compositions are nearly close to the EDX results presented by other researchers [11, 14] (Fig. 3).

4 Response of CSJT Towards a Cyclic Heating Load The response of the thermal probe can be evaluated by exposing the sensor to an environment where the operating parameters can be controlled and measured. In this context, IC engine helps in providing a real-time application. A standard four-stroke IC engine is commonly executed in one operational cycle time for complete four strokes (suction stroke, compression stroke, power stroke and exhaust stroke) which involves two revolutions of crankshaft. For a fixed RPM, the cycle time of the engine (for completion of one cycle) remains constant. Along with cycle time a few other parameters such as valve opening time, injection time of the IC engine can be fixed. The combustion process takes place inside the engine cylinder by injecting fuel after sucking and compressing the air and igniting with the help of spark plug. It may be noted here that the exhaust valve opens once in a cycle to expel the byproducts formed due to combustion process inside the engine cylinder. Therefore, the sensor can be exposed to the exhaust gases at a periodic interval by mounting it at any place along the exhaust pipe. The details of the engine setup and mountings are mentioned below.

4.1 Description of the Test Engine Experiments were carried out by mounting the sensor in the exhaust of a petrol-fueled single-cylinder research engine set up. The detailed technical specification of the engine is given in Table 1. This is a naturally aspired, water-cooled, four-stroke engine with hemispherical bowl in piston-type combustion chamber. The engine is coupled

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Table 1 Engine specification Engine type

Research engine test setup one cylinder, four strokes, multi-fuel VCR

Power

4.5 kW @ 1800 rpm, speed range 1200–1800 rpm

CR range

6:1–10:1

Bore × stroke

87.5 mm × 110 mm

Capacity

661 cc

Connecting rod length

234 mm

Type of cooling

Water-cooled

Speed sensor and indicator

Resolution 1°, range (5500 rpm) with TDC pulse

with water-cooled eddy current dynamometer for loading the engine crankshaft. The pressure and crank angle data of the engine are measured by a piezo pressure sensor with a built-in amplifier and a crank angle encoder attached on the cylinder head and crankshaft of the engine, respectively. The engine is well-instrumented with Pt-100 RTD for measuring the temperature of fluids at different points of the engine. All the measuring outputs from the engine sensors are connected to a data acquisition system (Make: NI USB-6210, 16-bit, 250 kS/s) which is further connected to a PC based software to record the pressure variations, the engine RPM, the throttle opening, etc. Apart from the mentioned specifications, the engine test bench is also accompanied by many general-purpose attachments. Such as fuel tank, orifice meters, manometer, rotameters, load sensors, calorimeter, and its attachments. The engine running condition was continuously monitored through PC based engine performance analysis software “Engine Soft”. The thermal probe is mounted in the exhaust of the engine (at the entry to calorimeter) operated in SI mode with a compression ratio 10:1 and petrol as fuel. The location of the probe is mentioned in Figs. 4 and 5. The output from a thermocouple is small which needs to be amplified before capturing the data. It was then connected to a data acquisition system (Make: National Instruments, Model: NI-9223/cDAQ-9178) for capturing data. Depending on the revolution, i.e., revolution per minute (RPM) of the crankshaft, the time taken for a complete cycle can be calculated analytically from Eq. 1. Figure 6 indicates the raw voltage signals captured by data acquisition system at a frequency of 1 lakh samples per second. Signals show a continuous rise with parabolic trend following basic laws of heat transfer. Cycletime , t (ms) =

2 × 60 × 1000 RPM

(1)

Close view of Fig. 6 is given for better understanding in Fig. 7. From this figure, the analytically calculated cycle time is 80 ms against the experimentally calculated cycle time 80.25 ms for 1500 RPM. Similarly, for 1700 RPM, the experimental cycle time is 70.36 ms against the analytical cycle time of 70.58 ms. The difference in the time may be due to experimental errors and most specifically the fluctuation of RPM

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Fig. 4 Schematic of engine set up with CSJT

Fig. 5 Actual engine set up with CSJT

value during recording (generated due to vibrations induced during engine running). Five sets of experiments are performed to check the repeatability of the cycle time and it is repeated with an error band of ±0.25%.

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Fig. 6 Recorded raw voltage signals

5 Conclusion A K-type coaxial thermal probe (CSJT probe) has been fabricated in-house. The response characteristics have been studied by mounting it along with the exhaust of a variable compression research engine operating in petrol mode with a compression ratio 10:1. The cyclic passage of exhaust gas over the probe (due to opening of the exhaust valve in a periodic manner) was detected by it through transient response in voltage output. The time taken to complete a single cycle was calculated from the experimental signal by measuring the time between two consecutive peaks and was compared with the cycle time calculated analytically for 1500 and 1700 RPM. Both the results show an appreciable match between them. Hence, the fabricated thermal probe is capable of capturing transient phenomena and can be used for capturing different unsteady phenomena. Further, the thermal sensor is found to be useful not only in understanding the engine operation but also to inspect working of the engine. Present studies recommend the mounting of such a sensor for fundamental engine studies and also to gauge its smooth operation.

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Fig. 7 Comparison of cycle time a 1500 RPM; b 1700 RPM

Acknowledgements The authors would like to acknowledge the financial support received from DRDO, New Delhi (India) for this experimental work.

References 1. Livengood, J.C., Rona, T.P., Baruch, J.J.: Ultrasonic temperature measurement in internal combustion engine chamber. J. Acoust. Soc. Am. 26(5), 824–830 (1954)

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2. Nagao, et al.: Measurement of cylinder gas temperature of internal combustion engines. Bull. JSME 13(64), 1240–1246 (1970) 3. Assanis, D.N., Badillo, E.: On heat transfer measurements in diesel engines using fast-response coaxial thermocouples. J. Eng. Gas Turb. Power Trans. ASME 111, 458–465 (1989) 4. Marr, et al.: A fast response thermocouple for internal combustion engine surface temperature measurements. Exp. Therm. Fluid Sci. 34, 183–189 (2010) 5. Rakopoulos, C.D., Mavropoulos, G.C.: Experimental instantaneous heat fluxes in the cylinder head and exhaust manifold of an air-cooled diesel engine. Energy Convers. Manag. 41, 1265– 1281 (2000) 6. Kar et al.: Instantaneous exhaust temperature measurements using thermocouple compensation techniques. SAE Technical Paper Series, 2004-01-1418 (2004) 7. Kee et al.: Fast response exhaust gas temperature measurement in IC engines. SAE Technical Paper Series, 2006-01-1319 (2006) 8. Mavropoulos, G.C.: Unsteady heat conduction phenomena in internal combustion engine chamber and exhaust manifold surfaces. Heat Transfer—Engineering Applications, pp. 283– 308 (2011) 9. Wang, X., Stone, C.R.: A study of combustion, instantaneous heat transfer, and emissions in a spark ignition during warm-up. J Autom. Eng. IMechE Part D 222, 607–618 (2008) 10. Mavropoulos, G.C.: Experimental study of the interactions between long and short-term unsteady heat transfer responses on the in-cylinder and exhaust manifold diesel engine surfaces. Appl. Energy 88, 867–881 (2011) 11. Mohammed, H., Salleh, H., Yusoff, M.Z.: Design and fabrication of coaxial surface junction thermocouples for transient heat transfer measurements. Int. Commun. Heat Mass Transfer 35(7), 853–859 (2008) 12. Agarwal, S., Sahoo, N., Singh, R.K.: Experimental techniques for thermal product determination of coaxial surface junction thermocouples during short duration transient measurements. Int. J. Heat Mass Transfer 103, 327335 (2016) 13. Agarwal et al.: Comparative performance assessments of surface junction probes for stagnation heat flux estimation in a hypersonic shock tunnel. Int. J. Heat Mass Transfer 114, 748–757 (2017) 14. Touloukian, Y.S.: Specific heat metallic elements and alloys. In: Touloukian, Y.S. (ed.) Thermophysical Properties of Matter. TPRC Data Series, vol. 4. IFI/Plenum Press, New York (1970)

Effect of Injection Pressure on the Performance Characteristics of Double Cylinder Four-Stroke CI Engine Using Neem Bio-diesel Sushant S. Satputaley, Iheteshamhusain Jafri, Gauravkumar Bangare, and Rahul P. Kavishwar

1 Introduction As in the current scenario, diesel engines are used on large-scale in transportation, Agriculture vehicles, Power generations, Marine application, etc. This resulted in an abrupt rise in the demand for petroleum products in the world. As the crude oil reserves are not going to last forever. Therefore, it is needed to develop alternatives to diesel fuels which would contribute to the diminution of dependence on fossil fuels, many countries including India are taking substantial efforts to investigate alternative to diesel [1]. Now a day’s bio-diesel is emerging as an alternative fuel as a viable alternative to petroleum diesel. Among various possible sources bio-fuels derived from seeds bearing trees rich in oil such as Neem (Azadirachta indica), Jatropha (Jatropha curcas) and Karanja (Pongamia pinnata) are the favourable species as they can be grown almost on all types of terrain all over India. So many methods are used for the production of bio-diesel conventionally such as transesterification, pyrolysis, micro emulsification, dilution, etc. [2].

1.1 Biodiesel The technical definition of bio-diesel is: “The mono-alkyl esters of long fatty acids traced from renewable lipid feed-stock such as vegetable oils or animal fats, for use in compression ignition (diesel) engines” (National Bio-diesel Board, 1996). It is a reasonable golden-yellow fluid with a consistency like that of petro-diesel [2]. Unlike petro-diesel, bio-diesel is biodegradable and non- toxic. Bio-diesel can be created from enormous assortment of vegetable oils, for example, cottonseed, S. S. Satputaley (B) · I. Jafri · G. Bangare · R. P. Kavishwar St. Vincent Pallotti College of Engineering & Technology, Nagpur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_77

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Fig. 1 Transesterification reaction

coconut, peanut, soya bean, sunflower palm safflower, also from non-edible oils such as soap nut, mahua, jatropha, Karanja, Neem, etc. and animal fats as tallow [2]. Due to high viscosity, high density, high flash point, and lower calorific value of vegetable oil or animal fats, it can’t be directly used in the diesel engine. So, it needs to be changed into bio-diesel to make it steady with fuel properties of diesel to eliminate engine-related problems such as fuel clogging, incomplete combustion, very low fuel atomization, deposits of carbon, engine fouling and spoiling of lubricating oil. Due to the complete miscibility of bio-diesel in petro-diesel in many countries, it is used as blends with the petro-diesel and these blends are often denoted as, for example, B20 which is actually 20% of bio-diesel and 80% of petro-diesel and must not be misinterpreted with bio-diesel only [2].

1.2 Bio-diesel Preparation The most common method for bio-diesel production is transesterification. It is also called alcoholysis which as the name suggests is the displacement of an alcohol from an ester by another alcohol, i.e., basically conversion of one ester into other. In the process, the equilibrium reaction takes place by mixing the reactants. It can be enhanced by raising the temperature up to certain limits, adding catalyst to it, and further by using excess of alcohol to achieve high yields of esters Fig. 1 describing the basic transesterification reaction [3]. Here from Fig. 1, the mixed fatty acid chains and methyl part is CH3 . If methanol is used in the above reaction, then it is called methanolysis. Generally, the process is base or acid-catalyzed but with base catalyst (sodium or potassium hydroxide) the process goes comparatively faster than acid-catalyzed reaction [4].

1.3 Neem Oil Bio-diesel Preparation Using Transesterification Process The following process was adopted for the preparation of bio-diesel

Effect of Injection Pressure on the Performance Characteristics …

817 Condenser

Thermometer

Pre-Heated Oil Hot Plate Magnetic Stirrer

Temperature Regulator Steering Regulator

Fig. 2 Experimental setup for bio-diesel preparation

1. The known quantity of crude Neem oil was taken in a round bottom flask and the oil was heated on a heating plate up to 65 °C temperature. 2. Then the quantity of methanol added for transesterification process was 25% of the oil taken for bio-diesel preparation. The potassium hydroxide was added as a catalyst. 3. The mixture was heated and stirred for 60–90 min at a constant temperature of 50 °C on a hot plate magnetic stirrer shows in Fig. 2. 4. After the constant heating and stirring of 60–90 min the mixture was poured in separating funnel for the separation of glycerol. 5. After 3–4 h of settling down a layer of glycerol was obtained in the bottom which was separated and removed. 6. The obtained oil was water washed to neutralize the value of pH. 7. The oil obtained after water wash is methyl ester (bio-diesel) of Neem oil and the yield obtained was 85%.

2 Evaluation of Engine Performance The picture of the engine on which the experiments are carried out is shown Fig. 3, i.e., of the constant speed compression ignition engine. The engine consists of twocylinder vertical diesel engine mounted over a robust frame. Different measurements provided helps to evaluate the performance of the engine at different loads [5].

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Fig. 3 Actual view diesel engine setup

2.1 Specifications of the Engine are as follows: Make Type: Mahender Ltd. Engine Type: Double Cylinder 4-Stroke, Water Cooled Engine Compression ratio: 18:1 Rated power: 10BHP @1500 rpm Stroke: 110 mm, Bore: 102 mm Loading device: Rope Brake dynamometer, Drum dia. = 0.25 m, Rope dia. = 0.012 m Load indicator: Range 0–50 kg Speed Measurement: Digital with contact type speed Tachometer Temperature sensor: Thermocouple, Type K (Nickel-Chromium/NickelAlumnae).

3 Injection Pressure The performance of diesel engines is vigorously affected by their injection system design. Even, the most prominent advances in diesel engines came from superior fuel injection system designs. While the primary purpose of the system is to carry through fuel to the cylinders of a diesel engine and which makes the difference in engine performance, emissions, and noise characteristics. Different from its spark-ignited

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engine opposite, the diesel fuel injection system has high fuel injection pressures. This depicts that the system component designs and materials should withstand higher stresses to perform for longer durations to match the engine’s durability targets. More noteworthy assembling exactness and tight resistances are likewise required for the framework to work efficiently. Also costly materials and manufacturing costs, diesel injection systems are defined by more complicated control requirements [6].

3.1 Parameters of Injectors in IC Engines The currently used fuel injectors should be characterized by the accompanying parameters: • Injector nozzle opening time and terminal time (estimated in millisecond; deciding the base injection time) • Working pressure (the range of pressure deciding the linear characteristics of an injector output) • Fuel spray front penetration—the fuel spray front penetration rate is same for all injectors in a given engine. In the injectors in which a change of spray front length happened, the fuel-air mixture is developed in the other point which occurs in fuel consumption, improper engine operation and faster engine components’ wear. The fuel spray front penetration has also a significant impact on the hazardous substances generation in the course of combustion process • Fuel spray cone angle and spray area engine manufacturers make use of injectors with various fuel injection characteristics which results from different structures of engine intake systems, different types of engine heads (8, 16, 20 V) and various fuel-air mixture formation parameters. Sufficient injection characteristics guarantee appropriate combustion parameters and consequently proper engine operation • Fuel droplet size—proper fuel atomization significantly affects the fuel combustion parameters. The most efficient use of fuel, i.e., its most effective combustion, requires preparation of sufficient fuel-air mixture. Manufacturers apply higher injection pressure in order to achieve the finest possible fuel droplets, which while evaporating at the time, measured in milliseconds form a combustible fuel-air mixture. Improper atomization leads to increased fuel consumption and increased hydrocarbons, nitrogen oxides and carbon monoxide emission, which may often result in the exhaust system defects. Injection pressure is changed by tightening and UN-tightening of spring with the help of an instrument known as nozzle tester.

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4 Result and Discussion The aim of the discussion was conducted to examine the performance characteristics such as Brake power (BP), Brake Thermal Efficiency (BTE) and Brake Specific Fuel Consumption (BSFC) of a vertical, four-stroke, double cylinder, constant speed, direct injection, water-cooled, diesel engine running on Neem bio-diesel blended with diesel with varying proportions. For the examination of performance parameters, results of diesel fuel were considered as baseline data. The objective of results and discussion is to obtain a suitable blend out of all the blends at optimum injection pressure by considering the performance parameters. The Following parameters were discussed at full load conditions [7]. Brake power (BP) plays an important role in indicating the performance of diesel engines. It is defined as the power which is generated at crankshaft of the engine. Fig. 4 shows the variation between load and BP. From the above figure, it is observed that the brake power increases linearly with an increase in the load.

4.1 Brake Thermal Efficiencies Brake Thermal Efficiency is used to evaluate how well an engine converts the thermal energy from a fuel to mechanical energy. It is the ratio of the power the engine delivers to the crankshaft to the thermal power available in the fuel. This greatly depends on the manner in which the energy is converted since the efficiency is normalized with fuel heating value [8]. Fig. 5 represents the variation of brake thermal efficiencies with BP using pure diesel and blends at different injection pressure. The brake thermal efficiency has increased with an increase in BP (i.e., load). The rate of increase was more at lower loads whereas it has decreased with increasing the load further. The rise in brake thermal efficiency is because of the presence of oxygen in the bio-diesel molecules which enhance the combustion efficiency as compared to pure diesel. From the above figures it can be seen that At 220 bar, The BTHE obtained for B40 blend was maximum at higher loads. It was found that while plotting graph, Fig. 4 Variation of brake power versus load

7 6 BP (kW)

5 4 3 2 1 0

0

10

20 Load (W) (kg)

30

40

Effect of Injection Pressure on the Performance Characteristics … 40

220 bar

30 20

NBTH (%)

NBTH (%)

40

DIESEL B20 B40 B60

10

2

4 BP (kW)

DIESEL

20

B20 B40

10

B60

6

0

8

2

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BP (kW)

40 NBTH (%)

240 bar

30

0

0 0

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30 DIESEL B20 B40 B60

20 10 0 0

2

4

6

8

BP (kW)

Fig. 5 Variation of BTHE versus BP for diesel and various bio-diesel blends at 220, 240 and 260 bar

the BTHE of pure diesel which is 30.68% is lower than the BTHE of B40 being 33.23% which is 8.3% higher than pure diesel. At 240 bar, it was found that The BTHE obtained for B40 blend was maximum at higher loads. The BTHE of pure diesel which is 31.22% is lower than the BTHE of B40 being 33.95% which is 8.74% higher than pure diesel. At 260 bar, it was found that the BTHE obtained for B40 blend was maximum at higher loads. The BTHE of pure diesel which is 31.76% is lower than the BTHE of B40 being 34.43% which is 8.40% higher than pure diesel. Comparison showing brake thermal efficiency of pure diesel and blends at 220, 240 and 260 bar is shown in Fig. 6. 36 NBTH (%)

Fig. 6 Comparison showing brake thermal efficiency of pure diesel and different blends at a different injection pressure

DIESEL

B20

B40

B60

34 32 30 28 220

240 PRESSURE (bar)

260

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From the above graph, by comparing the brake thermal efficiency at different injection pressures 220, 240 and 260 bar, using different fuel it can be stated that the maximum efficiency 34.43% is obtained at 260 bar using B40 at full load.

4.2 Brake Specific Fuel Consumption The Brake Specific Fuel Consumption plays a major role to evaluate engine performance and determine the fuel efficiency of an engine. It defined as the fuel flow rate per unit power output. It is desirable to obtain a lower value of BSFC meaning that the engine used less fuel to bring about the same amount of work [9]. Fig. 7 shows the difference of brake specific fuel consumption (BSFC) with BP using pure diesel and blends at different injection pressure. In the above figures, it is observed that up to 1 kW there is an increase in BSFC with respect to load due to rich air-fuel ratio. After 1 kW load there is a marginal increase in the quantity of fuel supplied to the engine with respect to the increase in load hence the BSFC reduces. As per the definition of BSFC, the initial increase BSFC due to negligible load was obtained initially and then the BSFC decreases as the load increases for all the fuel used as shown in Fig. 7. As the BP increases, the BSFC decreases. With an increase in the bio-diesel percentage in the blends, the BSFC values increase because the fuel consumption increases due to the lower calorific value of bio-diesel. From the above figures it can be seen that at 220 bar, The BSFC obtained for B40 blend was minimum at higher loads. It was found that

220 bar

0.8

DIESEL B20

0.6

BSFC (kg/kWhr)

BSFC (kg/kWhr)

0.8

B40

0.4

B60

0.2 0

0.6 0.4 0.2 0

0

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BP (kW)

BP (kW)

0.6 BSFC (kg/kWhr)

DIESEL B20 B40 B60

240 bar

260 bar

DIESEL B20 B40 B60

0.4 0.2 0 0

2

4

6

8

BP (kW)

Fig. 7 Variation of BSFC versus BP for diesel and various bio-diesel blends at 220, 240 and 260 bar

Effect of Injection Pressure on the Performance Characteristics … 0.28 BSFC (kg/kWhr)

Fig. 8 Comparison showing brake specific fuel consumption of pure diesel and different blends at a different injection pressure

DIESEL

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B20

B40

B60

0.27 0.26 0.25 0.24 0.23 0.22

220 BAR

240 BAR Pressure (bar)

260 BAR

while plotting graph, the BSFC of pure diesel which is 0.2578 kg/kWh is higher than the BSFC of B40 being 0.2462 kg/kWh which is 4.5% lower than pure diesel. At 240 bar, it was found that The BSFC obtained for B40 blend was minimum at higher loads. The BSFC of pure diesel which is 0.2534 kg/kWh are higher than the BSFC of B40 being 0.2410 kg/kWh which is 4.89% lower than pure diesel. At 260 bar, it was found that the BSFC obtained for B40 blend was minimum at higher loads. The BSFC of pure diesel which is 0.2490 kg/kWh is higher than the BSFC of B40 being 0.2377 kg/kWh which is 4.53% lower than pure diesel [10]. Comparison showing brake specific fuel consumption of pure diesel and blends at 220, 240 and 260 bar is shown in Fig. 8. From the above graph, by comparing the brake specific fuel consumption’s at different injection pressures 220, 240 and 260 bar using different fuel it can be stated that the minimum BSFC 0.237749 kg/kWh is obtained at 260 bar using B40 at full loads. From the above discussion, it is found that when blend B40 is used at injection pressure 260 bar gives the maximum brake thermal efficiency of 34.43% and minimum brake specific fuel consumption 0.23 kg/kWh.

5 Conclusion From the experimental investigation and comparison of the result obtained at 220, 240 and 260 bar injection pressure it is can be stated that 1. Brake thermal efficiency (BTHE) has shown linear increasing trend at lower loads, whereas the BTHE increases by diminishing rate at higher loads. BTHE of B40 is 8.4% higher than BTHE of pure diesel using injection pressure 260 bar at full load. 2. Brake specific fuel consumption (BSFC) has shown decreasing trends with the load. At lower loads decrease in BSFC is more due to higher rate of increase of brake power. BSFC of B40 is 4.53% lower than pure diesel using injection pressure 260 bar at full load.

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References 1. Karmakar, Anindita, Karmakar, Subrata, Mukherjee, Souti: Biodiesel production from neem towards feedstock diversification: Indian perspective. Renew. Sustain. Energy Rev. 16, 1050– 1060 (2012) 2. Shikha, K., Rita, C.Y.: Biodiesel production from non-edible oils: a review. J. Chem. Pharm. Res. 4, 4219–4230 (2012) 3. Heroor, S.H., Rahul Bharadwaj, S.D.: Production of bio-fuel from crude neem oil and its performance. Int. J. Environ. Eng. Manag. 4, 425–432 (2013) 4. Dhar, A., Kevin, R., Agarwal, A.K.: Production of biodiesel from high-FFA neem oil and its performance, emission and combustion characterization in a single cylinder DICI engine. Fuel Process. Technol. 97, 118–129 (2012) 5. Agarwal, A.K.: Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines. Prog. Energy Combust. 33(2), 33–271 (2007) 6. Harsha, B.M., Chethana, G.D., Yogesh Kumar, K.J.: Experimental investigation on CI engine using neem seed oil as biodiesel at different injection pressures. Int. J. Emerg. Technol. Adv. Eng. 5(11) (2015). ISSN 2250-2459 7. Senthilkumar, R., Ramadoss, K., Prabhu, M.: Emission and performance characteristics of single cylinder diesel engine fuelled with neem biodiesel. In: IEEE—International Conference on Advance in Engineering, Science and Management (ICAESM-2012), March 30–31, 2012, pp. 353–359 8. Ashraful, A.M., Masjuki, H.H., Kalam, M.A., Rizwanul Fattah, I.M., Imtenan, S., Shahir, S.A., Mobarak, H.M.: Production and comparison of fuel properties, engine performance and emission characteristics of biodiesel from various non-edible vegetable oils: a review. Energy Convers. Manag. 80, 202–228 (2014) 9. Tyagi, N., Sharma, A.: Experimental investigation of neem methyl esters as biodiesel on C.I. engine. Int. J. Eng. Res. Appl. 2(4), 1673–1679 (2012). ISSN: 22489622 10. Knothe, G., Van Gerpen, J., Krahl, J.: The Biodiesel Handbook. AOCS Press, Champaign, IL (2005)

Experimental Study of a Helical Coil Receiver Using Fresnel Lens Sumit Sharma and Sandip K. Saha

1 Introduction Solar energy has been looked upon as the solution of the energy needs all over the world. But due to its inherent unsteady nature, efficient harnessing poses a problem. One of the promising options for efficient utilization of solar power is Concentrated Solar Power (CSP). It is a high temperature solar energy conversion system with temperatures exceeding 400 °C [1]. The solar radiation is concentrated on to a receiver where the aforementioned high temperatures are achieved, which is then utilized for energy conversion. Helical coil design for the cavity receiver garnered interest as the design has the advantage of reduced thermal stresses [2] and higher heat transfer coefficients between the tube and the heat transfer fluid [3]. Prakash et al. [4] conducted an experimental study to estimate losses from a helical coil solar thermal cavity receiver under non-uniform wall temperature, varying inclination, and wind speed. They proposed a Nusselt number correlation for convective losses from the receiver under no-wind conditions based on their findings. Qiu et al. [5] in their study on helical coil receiver included the effect of direction of flow, presence of glass cover, varying geometrical parameters like inner tube diameter and number of turns. Venkatachalam et al. [6] used a parabolic dish as a concentrator for tapered helical coil cavity receiver in their experiments undertaken to study the effect of aspect ratio. Aspect ratio was varied from 0.8 to 1.2, and they reported that the performance of the receiver with aspect ratio 0.8 was better than the other designs. Parabolic dish concentrators and compound parabolic concentrators are widely used for concentrating power systems [7]. Lorenzo and Luque [8] compared the optical gain achieved by Fresnel lens and a parabolic mirror and found that former performed better, and this motivates us to incorporate Fresnel in the current study. S. Sharma · S. K. Saha (B) Indian Institute of Technology, Bombay, Maharashtra 400076, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_78

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Fresnel lens have been majorly used in concentrating for photovoltaic applications [9]. Few studies are available in literature where Fresnel lens is used for medium temperature range (~200 °C) applications [10, 11] but the potential to be used for concentrating solar power systems is yet untapped. Thus, in this study, Fresnel lens for concentrating solar irradiation is used to analyze the performance of the helical coil solar receiver with compressed air as the heat transfer fluid. Direct Normal Irradiation (DNI) was recorded using a pyranometer at the site. The experiments are carried for three flow rates: 100, 125, and 150 L/min and transient variation of efficiency is calculated and compared for the flow rates.

2 Description of the Cavity Receiver The cylindrical helical coil tubular cavity receiver was fabricated by Primetech Engineers, Thane, India. It consists of mild steel tubes of outer diameter 13.7 mm and inner diameter of 10.3 mm. There are a total number of 12 turns with a spiral at the bottom as the 13th turn. The nominal diameter (Dhx ) of the helix is 115 mm, and the height of the helix (H hx ) is 215 mm. The aspect ratio (r) is defined as the ratio of height to diameter is thus 1.88. Glass wool insulation of thickness 25 mm is provided around the tubes. The complete structure is placed inside a cylindrical cavity made of mild steel having inner diameter 180 mm and height 240 mm. To avoid any concentrated rays escaping from the bottom of the helix, a reflecting cone of anodized sheet metal is placed at the bottom. A photograph of the receiver is shown in Fig. 1.

3 Experimental Setup The on-flux testing was carried out at Indian Institute of Science, Bangalore, India (12.9716° N, 77.5946° E). The test rig which mounts the receiver is developed by Bharat Heavy Electricals Limited (BHEL), Bangalore, India. The schematic is shown in Fig. 2. The diameter of the lens in 1.1 m and the focal length is 1.3 m. The optical properties of the lens material were provided by the manufacturer Shenzhen Haiwang Sensor Co Ltd, China. Transmissivity, τ lens = 0.93, absorptivity, α lens = 0.031, and reflectivity, φ lens = 0.039. Compressed air at 1.5 bar is used as heat transfer fluid. A pressure gauge (working range: 0.2–14 bar and uncertainty: ±4% of total pressure), and a rotameter (working range: 20–200 L/min and uncertainty: ±6.66% for 150 L/min) calibrated for air, are used for pressure and flow measurement respectively. For measuring temperatures of the tube surfaces, 10 K type thermocouples (working range: −10 to 600 °C and uncertainty: ±0.5 °C) are installed. The temperature at the outer surface of the cover is also measured using 2 K type thermocouples. For measuring the inlet and outlet air temperatures, T type thermocouples (working range: 0–350 °C and uncertainty: ±0.5 °C) are employed. For acquiring

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Fig. 1 Helical coil receiver with cone at the bottom

Fig. 2 Schematic of the experimental setup

temperature data, Keithley data acquisition system, model 2701 is used. The DNI at the site of the experiment is measured using a pyranometer (uncertainty: ±5% at 1000 W/m2 ) manufactured by Dynalab, Pune, India. The uncertainty in calculation of heat absorbed is ±7.7% and that in efficiency calculation is ±9%. For heat transfer coefficient calculation, the uncertainty is ±3.61%.

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3.1 Experimental Procedure The duration of experiment on any day is of two and a half hours from 1130 to 1400 h as the peak DNI is achieved around solar noon. For uniformity, a non-dimensionalized time is defined in Eq. 1. δ=

t − t0 ttot

(1)

where, t is the current time, t 0 is the initial time, and t tot is the duration of the experiment that is 150 min. The temperature data is recorded in every 15 min. The test rig has a geared single axis tracking mechanism to track the movement of sun manually. The flow rate is kept constant throughout the duration of the experiment. The experiments with three different flow rates: 100, 125, and 150 L/min are carried out on different days and corresponding to each flow rate, two flow directions are studied. 1. Downward flow: The inlet of the heat transfer fluid is provided from the top tube (tube 1) of the upward-facing cavity receiver and the heated fluid is obtained from the bottom tube (tube 13). 2. Upward flow: The inlet and outlet are reversed, and cold fluid enters from tube 13 at the bottom, and heated fluid is obtained from the extension of the top tube (tube 1). The flow directions can be visualized from Fig. 3.

3.2 Efficiency The efficiency of the receiver is defined as, ηrec =

Q abs Q in

(2)

where Qabs and Qin are the heat absorbed by the receiver and the total heat input to the receiver, and are given by Eqs. 3 and 4, respectively. ˙ p (Tfo − Tfi ) Q abs = mc

(3)

Q in = Ib · Alens · ηo

(4)

m ˙ is the mass flow rate of the fluid (kg/s). cp is the specific heat capacity (kJ/kg K) and T fi and T fo are the fluid inlet and outlet temperatures. Alens (0.95 m2 ), is the surface area of the lens. The optical efficiency of the lens, ηo (0.835) is defined as

Experimental Study of a Helical Coil Receiver Using Fresnel Lens

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Fig. 3 Downward flow and upward flow of the heat transfer fluid through the helical tubes

ηo = τ · γ

(5)

where, τ is the transmissivity of the lens (0.93) provided by the manufacturer and γ is the intercept factor, taken as 0.9 to account for any spillage. The losses from the receiver are then calculated by Eq. 6. Q loss = Q in − Q abs

(6)

The beam irradiation, I b (W/m2 ) in Eq. 4 is measured by a pyranometer by taking the difference of the global and diffuse irradiation. The diffuse irradiation is measured by casting a shadow on the pyranometer using a shadow ring. Ib = I g − Id

(7)

Beam irradiation is quite unsteady and intermittent cloud cover affects the values abruptly. The average value of DNI for the experiment with 150 L/min and downward flow is 950 W/m2 . The lowest recorded DNI is for 125 L/min with upward flow with an average value of 680 W/m2 . The fluctuation of irradiation is monitored and the time varying data is plotted in Fig. 4.

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S. Sharma and S. K. Saha 1100

DNI (W/m2)

900 700 500 100 lpm; up flow 125 lpm; up flow 150 lpm; up flow

300 100 11:30

12:00

100 lpm; down flow 125 lpm; down flow 150 lpm; down flow

12:30

13:00

13:30

14:00

Time (hh:mm)

Fig. 4 Variation of beam irradiation over the duration of experiments on different days

3.3 Heat Transfer Coefficient on Tube Surface The convective heat transfer coefficient provided on the tube surface is determined using a correlation for Nusselt number based on the tube diameter of the helical tube given in Eq. 8 [12]. Nu = 0.48Ra0.25 , 104 ≤ Ra ≤ 107 h conv =

Nu · k d

(8) (9)

where, k is the thermal conductivity of air and d is the tube diameter. Radiative heat transfer coefficient is determined from Eq. 10. h rad =

εσ (Ts4 − Ta4 ) (Ts − Ta )

(10)

where ε is the emissivity of the tube, σ is the Stefan–Boltzmann constant. T s is the average tube surface temperature and T a is the ambient temperature. The total heat transfer coefficient provided on the surface is a summation of convective and radiative heat transfer coefficient. h total = h conv + h rad

(11)

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4 Results and Discussion 4.1 Tube Surface Temperatures To compare the tube surface temperatures at different flow rates of the heat transfer fluid and on different days with widely varying DNI, a non-dimensional temperature is defined based on T s , T a , and fluid inlet (T fi ) and outlet (T fo ) temperatures as in Eq. 12. θ=

Ts − Ta Tfo − Tfi

(12)

The ratio gives a measure of the losses from the receiver to the heat absorbed by the receiver. Variation of non-dimensional temperature (θ ) with non-dimensional time (δ) for various flow rates in the two flow arrangements is plotted in Fig. 5. In the downward flow the fluid moves from tube 1 at the top to tube 13 at the bottom and gains heat, therefore higher tube surface temperatures, in the range of 130–175 °C, are obtained in the lower part of the cavity while temperatures in the upper part are in the range of 40–100 °C. In the upward flow, when the fluid flows from tube 13 to tube 1, the temperature in the upper part reaches to about 150– 160 °C and in the lower part is in the range of 50–120 °C. The average tube surface temperature is higher in the downward flow arrangement. In case of upward flow, the tube surface temperature is higher near the mouth of the cavity; thus convective losses are enhanced and because of this reason, the performance is lowered as compared to the downward flow arrangement. This can also be seen from Fig. 5 that the ratio θ is greater than 1 for upward flow which supports the observation that not only the 2.8 100 lpm; up flow 125 lpm; up flow 150 lpm; up flow

2.4

100 lpm; down flow 125 lpm; down flow 150 lpm; down flow

θ

2 1.6 1.2 0.8 0.4 0

0.1

0.2

0.3

0.4

0.5 δ

0.6

0.7

Fig. 5 Variation of θ with δ for different flow rates and flow directions

0.8

0.9

1

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losses are higher as compared to downward flow but are also greater than the heat gained by the fluid which translates to lower efficiencies.

4.2 Air Outlet Temperatures The maximum air outlet temperature obtained in the experiments is 164 °C for the flow rate 100 L/min in the downward flow. The air inlet temperature for all the experiments is approximately 30 °C. A comparison of different flow rates for upward and downward flow directions is made on the basis of the temperature difference between fluid inlet and outlet temperatures since the DNI varied significantly on different days. The results are plotted in Fig. 6. As can be observed from Fig. 6 that the temperature gain for the fluid increases as the mass flow rate is reduced which is due to the longer residence time of the fluid inside the tube. Also, the temperature gain is higher in the downward flow arrangement than in the upward flow as higher average tube surface temperature is achieved in the former, as already discussed in Sect. 4.1. One of the major reason of low-temperature gain in upward flow is that the design of the receiver is not suitable for this flow arrangement. It can be seen from Fig. 3 that in upward flow, the heated fluid exiting from tube 1 traverses through an extension tube which lies in the shadow and no solar irradiation is received on it. Thus, the fluid loses heat to the structure. 140 120

Tfo-Tfi (°C)

100 80 60 40

100 lpm; up flow 125 lpm; up flow 150 lpm; up flow

20 0 11:30

12:00

12:30 13:00 Time (hh:mm)

100 lpm; down flow 125 lpm; down flow 150 lpm; down flow 13:30

14:00

Fig. 6 Variation of air inlet and outlet temperature difference with time for different flow rates and flow directions

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0.6

Efficiency (η)

0.5 0.4 0.3 0.2 100 lpm; up flow 125 lpm; up flow 150 lpm' up flow

0.1

100 lpm; down flow 125 lpm; down flow 150 lpm; down flow

0 0

0.1

0.2

0.3

0.4

0.5 δ

0.6

0.7

0.8

0.9

1

Fig. 7 Variation of efficiency for different flow rates and flow directions

4.3 Efficiency Efficiency of the receiver is defined as in Eq. 2. The variation of efficiency during the course of the experiment for different flow rates and flow directions is plotted in Fig. 7. As the mass flow rate reduces, efficiency falls due to lower residence time, as can be seen from Fig. 7. At 150 L/min, the average efficiency achieved is 52.7% which drops to 47% at 100 L/min. As discussed in Sect. 4.1, the efficiencies are lower for the upward flow arrangement due to higher temperatures near the mouth of the cavity, which results in higher convective losses.

4.4 Heat Transfer Coefficient Heat transfer coefficient calculated from Eq. 11 gives the calculated values from the experimental data. As the flow rate is increased, the tube surface temperatures are reduced, thereby lowering the heat transfer coefficients. In upward flow configuration, higher tube surface temperatures are obtained, thus higher heat transfer coefficients. The value of heat transfer coefficient is lower for 125 L/min in upward flow due to lower available DNI on the day of the experiment; thus, lower tube temperatures were achieved. The variation of heat transfer coefficient with time for different flow rates and flow direction is plotted in Fig. 8. The average values of heat transfer coefficient for 100, 125, and 150 L/min in down flow conditions are found to be 16.09, 14.86 and 13.91 W/m2 K. The corresponding values in up flow conditions are 17.71, 16.41, and 16.6 W/m2 K, respectively.

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19 18 17 h (W/m2)

16 15 14 13 12

100 lpm; up flow 125 lpm; up flow 150 lpm; up flow

11

100 lpm; down flow 125 lpm; down flow 150 lpm; down flow

10 0

0.2

0.4

δ

0.6

0.8

1

Fig. 8 Variation of heat transfer coefficient for different flow rates and flow directions

5 Conclusions In this work, on-sun testing of an upward-facing helical coil open cavity receiver is carried out using a Fresnel lens with compressed air as the heat transfer fluid. The tests were conducted for three flow rates: 100, 125 and 150 L/min and two flow directions; upward flow and downward flow. It is found that the maximum efficiency achieved by the receiver is 53% at 150 L/min in the downward flow condition. Efficiency depends strongly on flow rate, and it falls to 47% when the flow rate is reduced to 100 L/min. The study of flow direction revealed that for an upward-facing open cavity helical receiver, the preferred direction of flow is from top to bottom rather than from bottom to top. Changing the direction of flow majorly affects efficiency. For 150 L/min, efficiency dropped from 53% in downward flow to 38% in the upward flow. It was also seen that upward flow efficiency can be increased by changing the design of the receiver. The air outlet may be provided right next to tube 1 and remove the tube extension through which heat loss occurs.

References 1. Pavlovi´c, T.M., Radonji´c, I.S., Milosavljevi´c, D.D., Panti´c, L.S.: A review of concentrating solar power plants in the world and their potential use in Serbia. Renew. Sustain. Energy Rev. 16, 3891–3902 (2012) 2. Heller, P., Pfander, M., Denk, T., Tellez, F., Valverde, A., Fernandez, J.: Test and evaluation of a solar powered gas turbine system. Sol. Energy 80(10), 1225–1230 (2006) 3. Naphon, P., Wongwises, S.: A review of flow and heat transfer characteristics in curved tubes. Renew. Sustain. Energy Rev. 10, 463–490 (2006)

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4. Prakash, M., Kedare, S.B., Nayak, J.K.: Investigations on heat losses from a solar cavity receiver. Sol. Energy 83, 157–170 (2009) 5. Qiu, K., Yan, L., Ni, M., Wang, C., Xiao, G., Luo, Z., Cen, K.: Simulation and experimental study of an air tube-cavity solar receiver. Energy Convers. Manag. 103, 847–858 (2015) 6. Venkatachalam, T., Cheralathan, N.: Effect of aspect ratio on thermal performance of cavity receiver for solar parabolic dish concentrator: an experimental study. Renew. Energy 139, 573–581 (2019) 7. Tian, M., Su, Y., Zheng, H., Pei, G., Li, G., Riffat, S.: A review on the recent research progress in the compound parabolic concentrator (CPC) for solar energy applications. Renew. Sustain. Energy Rev. 82(1), 1272–1296 (2018) 8. Lorenzo, E., Luque, A.: Comparison of Fresnel lenses and parabolic mirrors as solar energy concentrators. Appl. Opt. 21(10) (1982) 9. Xie, W.T., Dai, Y.J., Wang, R.Z., Sumathy, K.: Concentrated solar energy applications using Fresnel lenses: a review. Renew. Sustain. Energy Rev. 15(6), 2588–2606 (2011) 10. Valmiki, M.M., Li, P., Heyer, J., Morgan, M., Albinali, A., Alhamidi, K., Wagoner, J.: A novel application of a Fresnel lens for a solar stove and solar heating. Renew. Energy 36(5), 1614–1620 (2011) 11. Wang, H., Huang, J., Song, M., Yan, J.: Effects of receiver parameters on the optical performance of a fixed-focus Fresnel lens solar concentrator/cavity receiver system in solar cooker. Appl. Energy 237, 70–82 (2019) 12. Xin, R.C., Ebadian, M.A.: Natural convection heat transfer from helicoidal pipes. J. Thermophys. Heat Transfer 10, 297–302 (2008)

Substrate-Assisted Electrosynthesis of Patterned Lamellar Type Indium Selenide (InSe) Layer for Photovoltaic Application A. B. Bhalerao, S. B. Jambure, R. N. Bulakhe, S. S. Kahandal, S. D. Jagtap, A. G. Banpurkar, A. W. M. H. Ansari, Insik In, and C. D. Lokhande

1 Introduction In infrared detectors, InSe is used as promising material. It is an equally promising material in optical mass memories, switching devices, and optical fibers. InSe is a layered type narrow bandgap semiconductor. It is strongly anisotropic, which carries bonded Se-In-In-Se units held by Vander Waals forces, having inertness for chemical reactions. InSe electrode due to its partial kinetic stabilization property shows quick photoresponse [1]. Accordingly, it can be used as an absorber layer in the thin film heterojunction solar cell [1]. Hence, for the formation of efficient and photochemically stable solar cells, atomic-level control in the growth of this material is desired. Nanosized clusters of this semiconductor have high optical absorption. In view of this electrodeposition technique is more suitable due to feasibility in the growth rate A. B. Bhalerao (B) · S. S. Kahandal · A. W. M. H. Ansari K. K. Wagh Institute of Engineering Education & Research, Amrutdham, Nasik 422003, India e-mail: [email protected] S. B. Jambure · A. G. Banpurkar Department of Physics, Savitribai Phule Pune University, Pune 411007, India R. N. Bulakhe Department of Polymer Science and Engineering, Korea National University of Transportation, Chungju 380-702, South Korea S. D. Jagtap Department of Instrumentation Science, Savitribai Phule Pune University, Pune 411007, India I. In Department of IT Convergence (Brain Korea PLUS 21), Korea National University of Transportation, Chungju 380-702, South Korea e-mail: [email protected] C. D. Lokhande D. Y. Patil University, Kasaba Bawada, Kolhapur 416006, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_79

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and structural morphology controlling. Substrate plays an important role in determining the structural and photovoltaic properties of electrodeposits. Very few papers reported the role of substrate and effect of patterning on electrodeposits growths. Common patterning techniques like masking and photolithography have limitations of poor resolution, environmental as well as safety issues. Another sophisticated technique like scanning probe microscopy is not feasible for large-scale patterning. However, the desired pattern in large scale with higher resolution can be employed by controlled electrodeposition [2]. Rasmussen et al. [3] reported the role of substrate in electrodeposition of copper layers. Gadwal et al. [2] reported electrochemical deposition of substrate-assisted patterned cobalt thin films. The literature reveals that very few reports are available on the photoelectrochemical study of InSe thin films. Das et al. [4] reported J sc of about 1.2 mA cm−2 and V oc 400 mV with efficiency 0.26% for vacuum-deposited n-InSe thin film. Hankare et al. [5] deposited InSe thin films by chemical deposition and determined efficiency as 0.61%. A literature survey [1–5] reveals that along with photosensitivity indium enhances the strength of chalcogenide thin films. However, no efforts were taken on the patterning of InSe layer. Also, no report is available on the photoelectrochemical study of patterned lamellar type nanocrystalline InSe based semiconductorelectrolyte interfacial region. Hence, the present work is directed towards synthesis and photoelectrochemical (PEC) study of patterned nanocrystalline lamellar type InSe thin films on patterned stainless steel (SS) substrates.

2 Electrochemical Synthesis of InSe Thin Films Deposition parameters like potential and pH were optimized using Pourbaix diagram and linear sweep voltammogram (LSV) for indium and selenium precursors containing electrolyte.

2.1 Formation Mechanism of InSe Thin Films The detailed synthesis protocol with respect to Pourbaix diagram for the In-Se-H2 O system is shown in Fig. 1; which shows that, depending on the potential (+1.0 to −1.5 V) and pH (0 to +4) range, InSe decomposition produces different selenium species that are HSeO3 , Se, and H2 Se, H2 SeO3 . There are three In-containing species that is InSe, In2 Se3 , In, and In3+ [6]. The necessary stability equation In3+ + 3e− → In0 occurs at −0.348 V potential. This indicates that indium reduction is possible in the domain (5) with negative potential. When H2 SeO3 is excess in the solution, it is reduced as Se0 and the deposits formed in the domain (3) contains both In2 Se3 and Se; where In3+ is in excess, the deposit contains only In2 Se3 [7], which specify that concentration of selenium must be less than indium concentration in the bath

Substrate-Assisted Electrosynthesis of Patterned Lamellar …

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Fig. 1 Pourbaix diagram shows variation of deposition potential against pH for the In-Se-H2 O system

of electrolytes. Since domain (5) is suitable for codeposition of both precursors; the potential and pH range has been selected near to this domain for optimization of potential and pH parameters. Indium sulfate dissolved in water gives indium cations In3+ , while the SeO2 dissolved in water form H2 SeO3 solution which further dissociates to elemental Se. In the selected potential region metal cations are firstly reduced and then react with elemental Se to form selenide nanomaterials [6]. The deposition process (shown in Fig. 2a) is associated with the following reactions [7]: In3+ + 3e− → In E 0 = −0.348 V, G 1 = −17.24 kJ mol−1

(1)

H2 SeO3 + 4H+ + 4e− → Se + 3H2 O E 0 = +0.74 V, G 2 = −933 kJ mol−1 (2)

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Fig. 2 a Polarization curve for reduction of InSe from electrolyte containing InCl3 , SeO2 at RT. b Variation of InSe film thickness with deposition time (inset: water contact angle of InSe thin film). c The XRD pattern of as-deposited InSe thin film. d EDAX pattern of InSe film

2In + 3Se → In2 Se3 E 0 = 0.195 V, G 3 = −386.49 kJ mol−1

(3)

In2 Se3 + In2+ + 3e− → 3InSe E 0 = −0.26 V, G 4 = −426.9 kJ mol−1 (4) During this process, the total Gibbs free energy is −1763.73 kJ mol−1 and as per standard deposition conditions, InSe deposition is possible at −0.268 V. In3+ first get electrons from the electrode and reduced to In, then react with elemental Se forming InSe. Optimal deposition potential was obtained from polarization curve by linear sweep voltammogram (LSV) study as shown in Fig. 2a. At the optimized deposition, potential films were grown at −550 mV with respect to the saturated calomel electrode (SCE). On stainless steel substrate, InSe thin films were electrodeposited. The pH of the electrolyte solution was varied by dilute HCl in the range selected from Pourbaix diagram (Fig. 1). The effect on grain size due to pH value suggests the pH plays a vital role in formation of InSe thin films. Since efforts were made to synthesize nanomaterial; acidic pH range was selected for deposition of InSe.

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2.2 Characterization Techniques The potentiostatically deposited InSe thin films are characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive analysis (EDAX), UV-Visible Spectroscopy, and surface wettability techniques. The structural characterization of films performed by XRD patterns obtained with Philips X-ray diffractometer in the 2θ range 20–80°. Surface morphological study of InSe thin film was performed using SEM. The compositional energy dispersive analysis was carried out using JEOL-JSM 6360 instrument in the energy range 0–20 keV. Using a UV-1800 Shimadzu spectrophotometer optical absorption spectrum was studied successfully. The solid-liquid junction for photoelectrochemical (PEC) cell is formed with InSe electrode as photoelectrode and 1 M polysulphide (Na2 S-NaOH-S) solution as an electrolyte. In the cell, graphite rod implemented as a counter electrode. The PEC cell is used to study conductivity type and photoresponse exhibited by InSe thin films. The I–V characteristics of PEC cells are studied using potentiostat (263A) with InSe electrode as an active electrode.

3 Results and Discussion Thin film deposition time varies from a range of 15–90 min. For pH = 2 ± 0.1 uniform film growth is observed. Figure 2b shows film thickness variation with an increase in deposition time. The deposition rate in first 30 min is very slow; which is also called a process initialization period. It includes the formation of thin-film with adhesive forces. Energy for adhesion comes from chemical or physical linkages. After stabilization of reaction; entire energy of the system is utilized for the growth of thin film. Hence, growth rate for a further period up to 75 min is fast and linear with thickness about 0.73 μm [8]. The growth rate declines further if the time increased from 75 to 90 min. During this span when the film becomes thicker, tensile stress develops to cause delamination. The film with maximum uniform thickness 0.73 μm is used for further characterization. XRD study shows a mixed monoclinic and rhombohedral crystalline structure showing a good match with JCPDS card no. 44-1007 and 29-0676 for as-deposited InSe film as shown in Fig. 2c. The peak observed at 32.5° is prominent and indicating the crystalline nature of the material. Growth at the optimized pH and deposition time shows four sharp peaks for as-deposited InSe film, corresponding to interplanar distances of 2.8, 2.4, 2.2, 2.1 Å at 2θ = 32.5°, 35.8°, 38.5°, 43.5°, respectively. The lattice parameters are a = 4.11 Å, b = 4.61 Å, and c = 11.02 Å for monoclinic structure, while lattice parameters for rhombohedral structure are a = 4.0 Å = b and c = 25.32 Å. The ‘d’ values, crystallite size and dislocation density of electrodeposited InSe thin films on stainless steel substrate have been given in Table 1. The observed ‘d’ values have a good match with theoretical ‘d’ values. The dislocation density (δ) has been

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Table 1 Structural analysis of InSe thin film 2θ (°)

θ (°)

Interplanar distance ‘d’ observed (Å)

Crystallite size ‘D’ (nm)

Dislocation density ‘δ’ δ = 1/D2 (nm)−2

32.57

16.2

2.855

0.5572

3.2

35.85

17.9

2.498

1.823

0.29

38.50

19.7

2.280

1.230

0.66

43.59

21.6

2.10

0.746

1.79

Table 2 EDAX analysis of InSe thin film showing elemental composition

Element

Energy (keV)

Mass %

At %

In Se

3.285

61.55

52.40

1.379

38.45

47.60

calculated by using crystallite size is around 1.5 nm−2 . Good crystallization levels of the films are obtained because of their small ‘δ’ values, which is also an indication of defects in the deposited films [2]. The EDAX analysis with elemental composition for InSe film (0.73 μm thickness) shows stoichiometric composition as per the data given in Table 2. The SEM study of patterned InSe film in Fig. 3a and b reveals nanometerscale spherical grains with porous lamellar morphology with 150 nm average grain size. The high surface area microporous structure enhances ion intercalation/deintercalation to improve electrochemical activity than bulk counterpart [9]. The peculiar lamellar structure is responsible for high optical and electrical anisotropic properties [10, 11]. The growth of InSe is highly influenced by the surface pattern formed due to scratches obtained from mechanical polishing of the SS substrate. The diagonally polished substrate shows diagonal growth of InSe, so as to form patterned arrays of InSe nanocrystals. Thus, scratches on substrate give mechanical control on growth direction. The SEM image reveals that nucleation and growth of as-deposited InSe thin film follow the Stranski–Krastanov mode of growth, which exhibits layer-plus-island pattern [12]. The trenches formed on the substrate surface during the mechanical polishing act as guiding patterns for the patterned growth of InSe. The selection of nucleation sites like the apex of scratch or valley between the two scratches for the growth depends on the interplay between surface and strain energy [2]. From UV-visible absorption spectrum in Fig. 3(c-inset), absorption hump was found at 600 nm, which is the characteristic of InSe to use it as an absorber layer. Absorption hump at 600 nm indicates that InSe layer gives a good absorption response to this spectral wavelength; as it is part of the visible spectrum of electromagnetic radiation. To use InSe for photovoltaic applications, it is essential to absorb light in visible spectrum. Optical property study of InSe confirms this response of layer. Hence it can be used as an absorber layer of photovoltaic solar cells [13]. Figure 3c shows a variation of (αhν)2 with hν for InSe film. The linear nature of the plot indicates the existence of the direct transition with 1.9 eV bandgap for a film

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Fig. 3 SEM images of Patterned InSe film at a ×3000 and b ×60,000 magnifications. c Plot of (αhν)2 versus hν of InSe thin film [inset: the variation of absorption (αt) with wavelength (λ)]. (d) Current–voltage (I–V ) characteristics of InSe/polysulfide PEC cell

of thickness 0.73 μm, which is in good agreement with the values reported earlier [14]. The observed bandgap shows a blue shift of 0.1 eV from the standard value of 1.8 eV due to size quantization [14], which occurs due to the localization of electrons and holes in a confined volume of nanocrystals. These consequences change in the energy band structure, with separation of individual energy levels and an increase in effective optical ‘band gap’ of the semiconductor as compared with bulk value [15]. Figure 3d shows I–V characteristics of PEC cell formed with non-patterned and patterned InSe thin film as anode and graphite as a cathode in polysulfide electrolyte. The inset shows light chopping data of patterned InSe electrodes. From the figure, it is clear that highest values obtained for J sc and V oc are 0.88 mA cm−1 and 0.23 V, respectively for patterned InSe electrodes. It is seen that the cell gives dark voltage (V d ) and dark current (I d ) due to the difference in between two half-cell potentials in the PEC cell. Due to the illumination of PEC cell, the I–V curve shifts in the fourth quadrant shows that the cell is a generator of electricity. The magnitude of the voltage increases with negative polarity towards the InSe thin film after illumination of the junction. This indicates that film shows cathodic behavior with n-type conductivity.

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Fig. 4 a Light chopping current density of patterned InSe thin film. b Contact angle study of InSe thin film with aqueous interface

The nature of I–V curve observed from Fig. 4a shows the formation of rectifying junction which shows a response to photoactivity [16]. I–V curve indicates good photocurrent with quick photoresponse. The contact angle for InSe thin film is found to be 67.5° as shown in Fig. 4b. Contact angle less than 90° shows that thin film has hydrophilic nature with better wettability property to form interfacial contact. The contact angle depends upon local homogeneity, its chemical composition as well as surface morphology of the thin film [17]. This nanocrystalline hydrophilic nature of InSe thin film electrode is helpful for better interfacial contacts in PEC cell, so as to show superior photoresponse with well-defined photoactivity [18].

4 Conclusions The electrodeposition technique has been implemented for the growth of patterned InSe film electrode consisting of regular spherical grains grown on SS substrate. The structural study shows polycrystalline nature of the film with mixed rhombohedral and monoclinic crystal structure as well as crystallite size comparable to 1 nm. The morphological study with SEM shows spherical granules of diameter about 150 nm, which have been deposited to form layered type patterned semiconductor. The film shows n-type semiconducting nature, with good photosensitive behavior, which is useful for the fabrication of n-type absorber layer of solar cells. Better stability is observed during photoresponse study. This study concludes that patterned InSe thin film electrodes synthesized by electrodeposition show steady and stable photoelectrochemical performance. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A1A03023788). A. B. B. gratefully acknowledges Savitribai Phule Pune University, Pune, research fund under Assistance by SPPU for Project-based Innovative Research (ASPIRE:

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Impact Oriented Research Program) Scheme. S. B. J. is grateful for the financial support from UGC India for Dr. D. S. Kothari Postdoctoral fellowship (BSR/PH/14-15/0043).

References 1. Clement, C.L., Arvamuthan, S., Santhanam, K.S.V.: Stabilization of the polycarbazole protected n-InSe photoanode for photoelectrochemical solar cells. J. Electroanal. Chem. 248, 233–237 (1988) 2. Gadwal, M.S.M., Sartale, S.D., Mathe, V.L., Pathan, H.M.: Substrate assisted electrochemical deposition of patterned cobalt thin films. Electrochem. Commun. 11, 1711–1713 (2009) 3. Rasmussen, A.A., Jensen, J.A.D., Horsewell, A., Somers, M.A.J.: Microstructures in electrodeposited copper layers; the role of the substrate. Electrochim. Acta 47, 67–74 (2001) 4. Das, V.D., Sathyanarayanan, J., Damodare, L.: Effect of annealing and surface treatment on the efficiency of photoelectrochemical (PEC) solar cells with vacuum-deposited n-InSe thin film electrode. Surf. Coat. Technol. 94, 669–671 (1997) 5. Hankare, P.P., Rathod, K.C., Asabe, M.R., Jadhav, A.V., Helavi, V.B., Chavan, S.S., Garadkar, K.M., Mulla, I.S.: Photoelectrochemical applications of In2 Se3 thin films by chemical deposition. J. Mater. Sci.: Mater. Electron. 22(4), 359–364 (2011) 6. Chung, Y., Lee, W.C.: Electrochemical behaviors of indium. J. Electrochem. Sci. Technol. 3(1), 1–13 (2012) 7. Massaccesi, S., Sanchez, S., Vedel, J.: Electrodeposition of indium selenide In2 Se3 . J. Electroanal. Chem. 412, 95–101 (1996) 8. Bhalerao, A.B., Wagh, B.G., Bulakhe, R.N., Jagadale, A.D., Lokhande, C.D.: Effect of different modes of electrodeposition on photoelectrochemical cell performance of nanocrystalline zinc selenide thin films. Adv. Sci. Lett. 22, 759–765 (2016) 9. Jambure, S.B., Lokhande, C.D.: Photoelectrochemical solar cells with chemically grown CdO rice grains on flexible stainless steel substrates. Mater. Lett. 106, 133–136 (2013) 10. Segura, A., Guesdon, J.P., Besson, J.M., Chevy, A.J.: Photoconductivity and photovoltaic effect in indium selenide. J. Appl. Phys. 54, 876–888 (1983) 11. Otsmane, L.B., Emery, J.Y., Eddrief, M.: X-ray reflection high electron energy diffraction and X-ray photoelectron spectroscopy studies of InSe and γ-In2 Se3 thin films grown by molecular beam deposition. Thin Solid Films 237, 291–296 (1994) 12. Kashchiev, D.: Nucleation: Basic Theory with Applications. Butterworth Heinenann, Burlington, p. 478 (2000) 13. Bhalerao, A.B., Wagh, B.G., Bulakhe, R.N., Deshmukh, P.R., Shim, J.J., Lokhande, C.D.: (Photo)electrochemical analysis of electrosynthesized fibrous cadmium indium selenide (CdIn2 Se4 ) thin fim. J. Photochem. Photobiol. A: Chem. 336, 69–76 (2017) 14. Matheswaran, P., Kumar, R.S., Sathyamoorthy, R.: Effect of annealing on the structural and optical properties of InSe bilayer thin films. Vacuum 85, 820–826 (2005) 15. Bhalerao, A.B., Lokhande, C.D., Wagh, B.G.: Photoelectrochemical cell based on electrodeposited nanofibrous ZnS thin film. IEEE Trans. Nanotechnol. 12(6), 996–1001 (2013) 16. Tyona, M.D., Jambure, S.B.„ Lokhande C.D., Banpurkar, A.G., Osuji, R.U., Ezema, F.I.: Dyesensitized solar cells based on Al-doped ZnO photoelectrodes sensitized with rhodamine. Mater. Lett. 220, 281–284 (2018) 17. Jambure, S.B., Patil, S.J., Deshpande, A.R., Lokhande, C.D.: A comparative study of physicochemical properties of CBD and SILAR grown ZnO thin films. Mater. Res. Bull. 49, 420–425 (2014) 18. Bhalerao, A.B., Wagh, B.G., Shinde, N.M., Jambure, S.B., Lokhande, C.D.: Crystalline zinc indium selenide thin film electrosynthesis and its photoelectrochemical studies. Energy Procedia 54, 549–556 (2014)

Optimization of Injector Location on the Cylinder Head in a Direct Injection Spark Ignition Engine Srinibas Tripathy, Sridhar Sahoo, and Dhananjay Kumar Srivastava

1 Introduction Spark ignition (SI) engine has low part-load efficiency due to the higher pumping loss [1]. Various techniques are applied to improve the SI engine performance such as cylinder de-activation technique, variable valve timing technique, and gasoline direct injection technique, etc. [2]. Several automotive industries have adopted GDI technology for improving SI engine efficiency [3]. GDI engine shows better performance over PFI and carburetor system in terms of power, fuel economy, and emissions [4]. The in-cylinder gasoline direct injection provides charge cooling to the incoming air which increases the volumetric efficiency during high load conditions. On the other hand, the throttle-less operation with the stratified mixture inside the combustion chamber helps in reducing fuel consumption at part-load condition [5]. However, the development of a direct injection engine is a challenging task as the injector has to be installed directly on the cylinder head. Sevik et al. [6] investigated the effect of side and central injection in a natural gas direct injection spark ignition engine and varied the fuel injection timing. It was observed that the side injection strategy showed better performance in terms of combustion and efficiency improvements over the central injection strategy. Saw and Mallikarjuna [7] performed a numerical simulation to study the effect of spark plug and fuel injector location on mixture formation in a wall guided GDI engine. It was noticed that side injection with centrally located spark ignition was the best way to improve the combustion and performance of the engine. The cylinder head is the most crowded area as the intake and exhaust valves, spark plug, and water jacket are present; it limits the space for injector installation. The S. Tripathy (B) · S. Sahoo School of Energy Science and Engineering, IIT Kharagpur, Kharagpur 721302, India e-mail: [email protected] D. K. Srivastava Mechanical Engineering Department, IIT Kharagpur, Kharagpur 721302, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_80

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optimum distance between the spark plug and injector plays a crucial role in performance, combustion, and emissions evaluation. Hence, in this study, a GDI injector location was optimized for the upgradation of an existing single-cylinder gasoline PFI research engine to GDI engine. A computational domain was developed for the existing research engine, and the in-cylinder flow phenomenon was investigated. An experimental analysis was performed and compared with the simulation results for validation of the CFD model. In addition to that, a GDI injector spray characterization was performed numerically, and spray penetration length was validated with the existing literature. The cylinder head was diagnosed, and possible injector locations were identified. The spray data was used for the direct injection study at different injector locations and in-cylinder air-fuel homogeneity at the time of ignition is presented.

2 Numerical Methodology 2.1 Geometry Modeling Geometry modeling is the preliminary process of CFD modeling, where the threedimensional computational fluid domain is designed. The computational domain is the volume, where the air-fuel interaction and in-cylinder combustion occur. Therefore, accurate design of the intake and exhaust ports, piston, valves, cylinder head, and the liner is necessary for the computational fluid domain development. The details about the engine used for the development of a direct injection spark ignition engine are shown in Table 1. The process followed for the computational fluid domain extraction from a singlecylinder PFI research engine is shown in Fig. 1. As the intake port is a complex curved structure, so direct computer-aided design (CAD) model development is very difficult to achieve. Hence, in this study, a scanning method was used for the computational fluid domain extraction of the intake port. The wire electro-discharge machine (EDM) was used to cut each section of the cylinder head, and intake port was extracted. An Table 1 Technical details of the single-cylinder PFI SI research engine

Engine details

Remarks

Engine model

Single-cylinder

Cooling system

Water-cooled

Cylinder head type

Flat

Piston bowl shape

Hemispherical

Spark plug location

Center

Rated power

5 kW @ 1500 rpm

Bore

87.5 mm

Stroke

110 mm

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Fig. 1 Fluid domain extraction from the single-cylinder PFI research engine

industrial computed tomography scanner was used for scanning the intake port and intake valve, where the scanning accuracy was 0.2 mm. The scanned data was in stereolithography format, which needs to be converted into the CAD model. Therefore, the reverse engineering method was applied, and a three-dimensional CAD model was developed. Similarly, hemispherical piston, exhaust port, and exhaust valve were designed in CAD software and assembled to generate the computational fluid domain.

2.2 Geometry Discretization A fully automatic dynamic volume mesh was generated during the simulation in ANSYS Forte. A geometry-based mesh refinement technique was used in which the entire domain was discretized by user-defined global mesh size. Based on the physics and fluid flow in the critical regions, smooth refinement was employed based on the global mesh size. A solution adoptive mesh refinement was used to reduce the computational time and enhance the simulation accuracy [8].

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2.3 Numerical Modeling The general governing fluid flow equations of mass, momentum, and energy were used to evaluate the pressure, velocity, and temperature variable across the fluid domain. The Reynolds-Averaged Navier–Stokes based Re-Normalization K–ε model was used for turbulence modeling [9]. The primary and secondary gasoline spray breakup was modeled by Kelvin–Helmholtz (KH) and Rayleigh–Taylor (RT) model [10]. However, KH-RT model depends on the grid size of the computational domain for spray penetration length evaluation. This is due to the liquid–gas relative velocity was modeled as CFD cell gas velocity [11]. Hence, an unsteady gas jet model [12] was coupled with KH-RT model for spray modeling where the spray breakup was predicted without fine discretizing the CFD domain. The evaporation rate of the individual fuel component was tracked by a discrete multi-component vaporization model [13].

3 Validation of the CFD Model Prior to the validation of the CFD model, a grid independent test was performed. The global mesh size was optimized as 2 mm for numerical simulation based on the tradeoff between computational time and simulation accuracy. The experimental analysis was performed on a single-cylinder PFI research engine at compression ratio 9 and 1500 rpm under motoring and premixed combustion mode. Also, a full engine fullcycle computational simulation was performed at the wide open throttle for both the cases. Experimental data were taken and successfully used in the simulation. The experimental in-cylinder pressure traces for motoring and premixed combustion mode shows a good agreement with the numerical results, as shown in Fig. 2a and b,

Fig. 2 In-cylinder pressure traces comparison between experimental and simulation at a motoring and b premixed combustion modes

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Fig. 3 GDI injector locations on the cylinder head after investigation of the internal structure

respectively. Also, error between the experimental and simulation in-cylinder peak pressure for motoring and premixed combustion modes was found to be 1.01% and 1.19%, respectively.

4 GDI Injector Locations The validated CFD model was used for the development of a DI SI engine. The cylinder head has intake and exhaust valve, spark plug, pressure transducer and water jackets that limit the space available for GDI injector installation. To find the possible injector locations, the cylinder head was cut, and the internal structure was designed using CAD software. After a thorough investigation of the internal structure of the cylinder head and GDI injector dimension, three possible locations were identified, as shown in Fig. 3.

5 Spray Characterization A constant volume chamber of 150 mm diameter and 200 mm length was designed for spray modeling of a six-hole GDI injector. The spray penetration length and spray morphology of fuel vapor mass fraction were investigated. The surrogate fuel used in this study was iso-octane. The six-hole injector was placed on the top of the computational domain. Iso-octane was injected into the computation domain,

852 Table 2 Simulation conditions for spray characterization of GDI injector

S. Tripathy et al. Parameter

Value

Number of injector hole

Six

Injection pressure

100 bar

Ambient pressure

1 bar

Fuel inlet temperature

290 K

Injection duration

1.5 ms

Nozzle diameter

0.2 mm

Ambient temperature

291 K

Injected quantity

11.55 mg

Fig. 4 Spray penetration length comparison between experimental and simulation results

and simulation was performed under vaporization condition. Fuel injection pressure, injection duration, and ambient conditions, as shown in Table 2 were produced from the experiment performed by Li et al. [14]. The experimental fuel injection rate profile was used for the spray simulation. More details about the simulation test conditions can be found in the author’s previous work [15]. It was found that the experimental spray penetration length shows a good agreement with the simulation result, as shown in Fig. 4. Also, the spray morphology of gasoline injection at 1.5 ms after the start of injection is presented, as shown in Fig. 5.

6 Optimization of Injector Locations The spray data mentioned in Table 2 was used for the direct injection study at three different GDI injector locations. The purpose of the injection is to investigate the airfuel homogeneity inside the combustion chamber at the time of ignition for different

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Fig. 5 Fuel vapor mass fraction for the six-hole GDI injector at 1.5 ms after the start of injection

injector locations. Before injecting the fuel into the combustion chamber, a motoring simulation was performed in which only air is allowed at the inlet boundary. The fuel injection was deactivated, and the in-cylinder flow phenomenon during the intake stroke was investigated. The in-cylinder turbulent kinetic energy is presented for different crank angles during the intake stroke is shown in Fig. 6. It was found that the maximum turbulence occurs from 30° to 50° aTDC with a kinetic energy of 200 m2 /s2 . Therefore, injecting the fuel during a high turbulence period will enhance the incylinder mixing and rapid evaporation. Hence, the motoring simulation provides greater insight for flow visualization and helps in optimizing the fuel injection timing for three injector locations. After that, motoring simulations were performed in the direct injection spark ignition engine for three injector locations where fuel injection was activated. The fuel was injected directly into the combustion chamber, and the air was allowed to enter from the inlet boundary. The spark timing and combustion chemistry were deactivated as the location was optimized based on the air-fuel homogeneity at the time of ignition. The in-cylinder equivalence ratio, which represents the quality of air-fuel mixing for three locations is presented in Fig. 7. It was observed that injecting the fuel at location 2 and location 3, the equivalence ratio varies from 0.68 to 1.50 throughout the combustion chamber. Variation in equivalence ratio leads to ultra-lean and ultrarich conditions in the spark plug and piston bowl areas. Whereas injecting the fuel at location 1, the equivalence ratio varies from 0.93 to 1.06 resulting in homogenous conditions throughout the combustion chamber. Hence, location 1 results in better air-fuel homogeneity throughout the combustion chamber as compared to the other two locations.

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Fig. 6 Turbulent kinetic energy of in-cylinder air for crank angle a 30° aTDC; b 40° aTDC; c 50° aTDC; d 60° aTDC; e 70° aTDC; f 80° aTDC; g 90° aTDC; h 100° aTDC; i 110° aTDC during the intake stroke

Fig. 7 In-cylinder equivalence ratio for three injector locations

7 Conclusions A computation model was developed for a single-cylinder PFI research engine and used for the upgradation of PFI to GDI engine. The following conclusions are summarized. • Experimental and numerical simulations were performed under motoring and premixed combustion modes for PFI engine. The in-cylinder pressure was compared between the computational and experimental data and validated the CFD model. • A spray characterization was performed to investigate the spray penetration length and spray morphology of a GDI injector and validated with the existing literature.

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• The possible GDI injector location was identified after diagnosing the cylinder head of an existing PFI research engine. • The GDI injector spray data were used at different possible locations and injected directly into the combustion chamber. • The optimum location was chosen among all possible locations based on the in-cylinder air-fuel homogeneity at the time of ignition. The computational model helps in determining the optimum location so that the PFI engine will be upgraded to the GDI engine, which is the future scope of this project.

References 1. Zhao, F., Lai, M., Harrington, D.: Automotive spark-ignited direct-injection gasoline engines. Prog. Energy Combust. Sci. 25(5), 437–562 (1991) 2. Salim, W., Mahdi, A., Ismail, M., Abas, M., Botas, R., Rajoo, S.: Benefits of spark-ignition engine fuel-saving technologies under transient part load operations. J. Mech. Eng. Sci. 11(4), 3027–3037 (2017) 3. Tripathy, S., Sahoo, S., Srivastava, D.: Gasoline direct injection-challenges. In: Combustion for Power Generation and Transportation, 1st edn. Springer, Singapore (2017) 4. Park, C., Kim, S., Kim, H., Moriyoshi, Y.: Stratified lean combustion characteristics of a spray-guided combustion system in a gasoline direct injection engine. Energy 41(1), 401–407 (2012) 5. Koike, M., Saito, A., Tomoda, T., Yamamoto, Y.: Research and development of a new direct injection gasoline engine. SAE Trans. 109, 543–552 (2000) 6. Sevik, J., Pamminger, M., Wallner, T., Scarcelli, R., Boyer, B., Wooldridge, S., Hall, C. Miers, S.: Influence of injector location on part-load performance characteristics of natural gas directinjection in a spark ignition engine. SAE Int. J. Eng. 9, 2262–2271 (2016) 7. Saw, O., Mallikarjuna, J.: Effect of spark plug and fuel injector location on mixture stratification in a GDI engine—a CFD analysis. In: IOP Conference Series: Materials Science and Engineering, p. 012025. IOP Publishing (2016) 8. Verma, I., Bish, E., Kuntz, M., Meeks, E. et al.: CFD modeling of spark ignited gasoline engines—Part 1: Modeling the engine under motored and premixed-charge combustion mode. SAE Technical Paper 2016-01-0591 (2016) 9. Han, Z., Reitz, R.: Turbulence modeling of internal combustion engines using RNG k-ε models. Combust. Sci. Technol. 106(4–6), 267–295 (1995) 10. Beale, J., Reitz, R.: Modeling spray atomization with the Kelvin-Helmholtz/Rayleigh-Taylor hybrid model. At. Sprays 9(6), 623–650 (1999) 11. Reitz, R.: Modeling atomization processes in high-pressure vaporizing sprays. At. Spray Technol. 3(4), 309–337 (1987) 12. Abani, N., Reitz, R.: Unsteady turbulent round jets and vortex motion. Phys. Fluids 19(12), 125102 (2007) 13. Ra, Y., Reitz, R.D.: A vaporization model for discrete multi-component fuel sprays. Int. J. Multiph. Flow 35, 101–117 (2009) 14. Li, Z., He, B., Zhao, H.: Application of a hybrid breakup model for the spray simulation of a multi-hole injector used for a DISI gasoline engine. Appl. Therm. Eng. 65(1–2), 282–292 (2014) 15. Tripathy, S., Sahoo, S., Srivastava, D.: Numerical investigation on the effect of advanced breakup model on spray simulation of a multi-hole injector. In: ASME 2018 Internal Combustion Engine Division Fall Technical Conference, p. V002T06A013. American Society of Mechanical Engineers, USA (2018)

Automated Cleaning of PV Panels Using the Comparative Algorithm and Arduino Huzefa Lightwala, Dipesh Kumar, and Nidhi Mehta

1 Introduction We live in a time where the demand for energy is increasing by the hour and the energy resources are depleting by the minute. Thus, there is a need to switch to the energy coming from renewable sources such as the sun, wind, tidal waves, etc. From these, solar PV is one of the most promising renewable energy sources due to the fact that its extractable energy potential exceeds the others by 1000 s of Mega-Watts [1]. Studies show that the total energy obtained from the sun in less than an hour is enough to meet the global energy requirement for a year [2]. Photovoltaic (PV) panels that convert this solar energy into electricity have garnered attention from researchers all over the world for over 30 years now. They are capable of doing, what most of our conventional energy resources fail to do, provide an unlimited and inexhaustible amount of green energy. Despite these facts solar PV caters to only a small amount of our daily energy needs. In 2017, the contribution of solar energy to the total energy was less than 1% [3]. The main reason behind this is the high set up costs of PV panels and this is exacerbated by their low efficiency. PV panels are can convert only a fifth of the energy incident upon them.1 Due to their low efficiency they cannot give back immediately, benefits for the large investments made in setting them up. There are a number of factors that affect the efficiency of a PV panel and

1 https://blog.pickmysolar.com/what-is-solar-panel-efficiency

H. Lightwala (B) · D. Kumar · N. Mehta K.J. Somaiya College of Engineering, Mumbai, Maharashtra 400077, India e-mail: [email protected] D. Kumar e-mail: [email protected] N. Mehta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_81

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thus cause a drop in the net energy that can be obtained from the panel. If any of these is not optimized, it can lead to major energy losses. The factors that affect the efficiency of PV panels can be classified into the following three categories: Drop in efficiency due to internal factors (Efficiency reduction: 65–85%): These factors are the inherent properties of the panel when it comes from the manufacturer. It determines the conversion efficiency of the panel when it is new. For example, Selection of semiconductors and other Cell Materials, Degradation of the materials, cracking of cells, Corrosion, quality of the solar cell, manufacturing company, a transmittance of glass surface, etc. Drop in efficiency due to external/environmental factors (Efficiency reduction: 5–45%): When the panels are left in the open, a number of factors such as dust, temperature of the PV panels and ambient temperature, shading, latitude, tracking, tilt angle, degradation of glass cover, wind direction and speed, humidity, debris and bird droppings, leaves, MPPT, etc. further reduce the efficiency of the panels. Drop in efficiency due to peripherals (Efficiency reduction: 5–20%): A number of peripheral components are connected to the PV panel to transfer generated power from the panel to load, battery, or grid. The net power at the end of transfer line may be reduced due to these components. These include characteristics of batteries, inverters and charge controllers, efficiency of inverters and wires, improper connections, incorrect grounding and under-rated using of components, contact resistances, etc. While many of the above factors cannot be resolved completely without further advancements in technology, high initial costs, or undesirable side effects, there are a few factors that can be eliminated or at least reduced to a great extent, thus saving the useful solar energy that is lost otherwise. One such problem is the deposition of dust particles on PV panels. Dust is a term generally applying to minute solid particles with diameters less than 500 mm [4]. Since dust particles are opaque, the effect of dust deposition on a PV panel is to block the radiation reaching the PV panel. Hence, unless the panels are cleaned regularly, the energy obtained from them can and will reduce over time. Since the twentieth century, a great number of studies have been carried out worldwide to find out the effect of dust on PV modules. Nimmo and Seid [5] claimed an efficiency drop of about 40% for a period of 6 months in their studies. Rao et al. [6] developed an indoor set up to find out the effect of dust on PV panel performance. They calculated a power loss of 45–55% of the maximum power output for a dust deposition density of 7.155 g/m2 . Salim et al. [7] obtained an efficiency loss of 32% in 8 months in his long-term studies in Riyadh. Sayigh et al. [8] determined that the glass transmittance of the PV panel reduced by 64–17% for tilt angles 0° –60° over 38 days of exposure. Thus, it is clear that the efficiency of a panel is very sensitive to dust deposition. With this being said, the panels cannot be kept clean at all times, because the cleaning process comes with a cost. Studies of Hussein and Miqdam [9] reveal that it takes anywhere from 0.4 to 1.2 USD/m2 per cleaning cycle to clean the panels depending on the method of cleaning. This number builds up to a much bigger value for large PV panel systems, especially for solar fields with thousands of panels. Hence, it is not only imperative to develop an

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efficient cleaning system that cleans every last particle of dust on the panel surface but also to develop a smart, automated cleaning system that works on a cleaning algorithm that can determine the exact moment that the panels need to be cleaned to optimize the power obtained from the PV panels as well as the cost of cleaning. This paper focuses more on the latter part of this problem statement and attempts to develop such a cleaning algorithm system which is basically a closed-loop control system that uses feedback of current output from a reference panel, and this entire system is automated using the Arduino UNO microprocessor.

2 Methodology 2.1 Cleaning Algorithms Various cleaning algorithms are implemented in various solar fields to carry out automatic cleaning of the panels. The algorithm is the set of rules that determine the moment when the panel is to be cleaned, using a deciding factor such as cleaning interval, amount of dust deposited, reduction in output current or power, etc. Some of these algorithms are discussed below, followed by one of our own. Semi-automatic cleaning system: These are the most commonly implemented systems. The panels are cleaned automatically by robotic devices or by spray nozzles or both, but it requires a signal to start the cleaning process from a human operator. Hence this system is not truly automatic, as the human needs to monitor the effect of dust on PV panel. Moreover, the human judgement factor comes in play and the cleaning process may be initiated earlier or later than required at times. Time-based fully automatic system: (Fig. 1).

Fig. 1 Time-based fully automatic system

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These systems are becoming more and more common these days. In these systems the panels are cleaned at specific and constant intervals of time. The time interval needs to be determined first using environmental conditions and through experimentation. The time-based system cleaning graph has been illustrated above. The time-frequency is determined to clean the panel when its efficiency drops by 80%. However, due to erratic environment, at some point the panels are cleaned when they do not need to be cleaned and at some points they are not cleaned when they need to be cleaned as shown above. Disadvantages of Time-based system: • The cleaning frequency is very difficult to determine accurately, due to the unpredictable environmental behavior. • It needs to be different for different seasons and climates. • Tedious experimentation is required to determine the cleaning frequency. • Even after it is determined, it will never be accurate as dust deposition rate is never constant. In normal conditions, it keeps varying with humidity, wind speed, weather, etc. and in extreme conditions, a storm may leave panels dirty for several days, till it is cleaning time again. Power output based cleaning system: In this system, the instant when the panel is cleaned depends on the power output of the panel. When the power output of the panel reaches a certain threshold value, the cleaning process is initiated by the microcontroller. Tsamaase [10] developed such a system. It was a dry cleaning system that implemented the Arduino UNO. The system is unpopular due to the following disadvantages: • Power output of a PV panel depends on a vast number of factors such as temperature, time of the year, malfunctioning of PV panels, humidity, etc. besides dust. Hence the cleaning system may get activated even when the panel is clean, if power reduces due to other factors. Similarly, it may not be activated, even if the panel is dirty, due to power boost by other factors. • Physical damage to a panel may deteriorate its output. However, the microcontroller will perceive the cause of this as dust and keep cleaning the panel indefinitely. • Sudden drops in power output due to cloudy weather, shadow, etc. may cause the panel to be cleaned when not required. Comparative cleaning system: This is the system that we have employed in the project. The system makes use of two exactly identical PV panels. In this system, one panel is always kept clean. This panel is called the “reference” panel. The other panel is allowed to get dirty. This panel is called the ‘dirty’ panel. A microprocessor (such as Arduino) continuously takes the power input of both panels. Once the power output of the dirty panel becomes equal to “f” times power output of clean panel, the cleaning process is initiated. Since the two panels are exactly alike, except the fact that dust has accumulated on one, the only factor causing the difference in the outputs of two panels is dust accumulation. Hence, the cleaning system is purely dependent on dust. It is to be noted that the ‘f ’ value needs to be selected carefully

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Fig. 2 Comparative cleaning system

as if it is too high, it will result in undue wastage of water/energy in every cleaning cycle and if it is too low, there will be a big reduction in power generation due to the panels being dirty for too long. The comparative system is elegant in that it knows exactly when the panel needs cleaning. Thus, the panel will be cleaned if and only if it requires cleaning, and the instant, the efficiency reaches the threshold value, cleaning process is initiated (Fig. 2). Above figure shows a typical comparative cleaning system process. It is evident that the panel is cleaned only when its efficiency drops to f % (80% in this case) that of a clean panel. Thus, the panel efficiency always lies in between 80% and 100%. The comparative cleaning algorithm is illustrated in a flowchart (Fig. 3):

2.2 Components Arduino UNO with the atmega328P IC: It is a micro-controller and the spinal cord of the system. It performs the following functions: • • • •

Switching from main circuit to current measuring circuit at fixed times. Reading the measured short-circuit current (I sc ) values from current sensor. Comparing the I sc values. Signaling solenoid valves corresponding to the panels that need cleaning, to actuate. • Powering auxiliary components. Main PV panels: A set of two identical PV panels is used to determine when the dirty panel needs cleaning. Solenoid Valve: It is a 12 V DC normally closed valve that is actuated when signal from Arduino is given. Upon actuation, it allows the water to flow to the nozzle. Water curtain nozzle: It has a large spray angle and sprays water throughout the PV panel.

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Fig. 3 Comparative cleaning system algorithm

Relay Module: It is an electro-magnetic switch, which switches between the load side and current measuring side of the circuit. It actuates when given signal by the Arduino. Current sensor: It measures the short-circuit current (I sc ) of the PV panel using the principle of Hall-Effect. The current value is then fed to the Arduino. DC-DC Buck type Convertor: This is a Buck convertor that maintains 12 Volts at its output to Power the Arduino board and the solenoid valves. Wires, Pipes, and pipe adaptors: They establish the electric and water networks. Auxiliary PV panel: Source of power for the entire system.

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2.3 Overview of Working of The System The system uses the comparative algorithm as described above. It makes use of two identical PV panels. The cleaning method adopted in our system is a set of water curtain nozzles. They spray water on the PV panels when signaled by the microcontroller. Under ordinary conditions, the panels are connected to their respective loads. Every day, the first function performed by the micro-controller is the cleaning of the reference (the left) panel. It then keeps taking the current values of the two panels, every hour and to do so, it signals the relay to switch from the load circuit to the current measuring circuit. If the current of the right panel reaches a threshold percentage of the left panel, then the cleaning operation is performed for the dirty panel. The circuit is supplied power by a small auxiliary panel and remains ON as long as light falls on it. Thus, it turns OFF every evening and turns ON in the morning again. The solenoid valves turn ON and OFF, the flow of water through the nozzles when signaled by the micro-controller.

2.4 Circuits Circuit 1 (the Input Side Circuit): It controls the relays that switch from the primary circuit to measuring circuit and reads current values from current sensors (Fig. 4). Working: The terminals T1, T2 and T3, T4 go to a battery that the panel charges. They are connected to the normally closed terminal of the relay switch. For normal

Fig. 4 The input side circuit

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Fig. 5 The output side circuit

conditions, the battery (load) circuit is connected to the panel. This is the primary circuit which may also be a grid or an appliance. The hall-effect sensor is connected to the normally open terminal. Digital pins 2 & 4 of Arduino board control actuation of the relays. When a signal is given on these pins, the relays disconnect the battery and connect the sensor. Readings from sensors are given to A0 and A1 pins of Arduino. The two relays and two current sensors work on the power given by Vcc and GND pins of Arduino. Circuit 2 (The output side circuit): (Fig. 5) Working: The output side circuit controls the actuation of the solenoid valves. The 12 V supply comes from the auxiliary solar panel. When pin 12 of the Arduino becomes HIGH, relay switches to the NO terminal, and the circuit gets completed and the solenoid is connected to supply. Thus, if the water supply pressure is right, water will start flowing through the valve, once it is actuated. A snubber diode is fitted to dissipate the current overtime after the valve is turned off. Circuit 3 (The power circuit): The auxiliary PV panel supplies power to the Arduino and two solenoid valves. The DC-DC Buck convertor steps down the voltage to 12 V which is required by the three components.

2.5 Some Images of the Setup Figures 6, 7, 8, and 9.

Automated Cleaning of PV Panels Using the Comparative … Fig. 6 Entire system

Fig. 7 Cleaning in operation

Fig. 8 Circuits

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Fig. 9 Solenoid valve and nozzle

3 Results and Discussion 3.1 Testing Once the set up was ready, an experiment was performed for 23 days to determine the effectiveness of the system. One of the panels was cleaned using the system and the other was allowed to get dirty. A third panel identical to these two was used as the reference panel for the system. Initially, the efficiencies of both panels were about 17.5%. The results of the experiment are plotted below (Fig. 10): The slopes of the efficiency lines of the two panels were 0.13 and 0.3, respectively. Thus, the efficiency of the two panels reduced by 0.13% and 0.3% per day. The efficiency of the former panel reduces due to the sticky particles, bird droppings, etc. That gets adhered and cannot be cleaned by just water spray. However, this problem

Fig. 10 Efficiency curve of the panel cleaned by the system and the panel left to get dirty

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can easily be resolved by employing roller brushes, hydrophobic or hydrophilic films, increasing water pressure, varying nozzle dimensions, etc. [11, 12] Instead of actuating the nozzles in the current system, the Arduino will actuate the new cleaning mechanism now (such as the roller brush), and everything else remains the same. Hence, the comparative cleaning algorithm and Arduino can be applied to any cleaning system and make it automated.

3.2 The System Applied to a Solar Farm (with Thousands of Panels) Even though a field has thousands of PV modules, it is safe to assume that in a given period of time, the dust deposition rates of all modules will be the same, more or less. Thus, it is sufficient to still compare just two panels to know when to clean all of them. Thus, at the cost of cleaning just one panel every day, we can predict exactly when all the remaining panels in the solar farm need to be cleaned. Note: If the reference panel gets damaged or behaves erratically (due to shadows, bird feces, technical problems, etc.), the entire cleaning process will become haywire. Hence it is always safer to keep multiple panels as a reference and let the system decide the cleaning process initiation based on the outputs of all of these reference panels.

4 Conclusions Dust can have a detrimental effect on the efficiency of PV panels. It can be seen from the data that the efficiency drops by about 7% in about 23 days. However, cleaning the panels can be expensive especially when water is used. In that case, it is imperative to create an automated cleaning system that knows exactly when to clean the panels. This is achieved by using the comparative cleaning algorithm which can be applied to any existing or new cleaning system using Arduino. The comparative cleaning algorithm and Arduino in conjunction with an efficient cleaning system can know the exact moment for cleaning and thus prevent the panels from being cleaned too early or late. This leads to major savings as far as cleaning is concerned especially when extended to a field, as at the cost of washing just a few panels every day, we can predict exactly when the remaining need cleaning.

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References 1. Tsao, J., Lewis, N., Crabtree, G.: Solar Faqs. US Department of Energy, pp. 1–24 (2006); Idoko, L., Anaya-Lara, O., McDonald, A.: Enhancing PV modules efficiency and power output using multi-concept cooling technique. Energ. Rep. 4, 357–369 (2018) 2. Energy Informative, https://energyinformative.org/potential-of-solar-energy/ 3. U.S. Energy Information Administration, https://www.eia.gov/energyexplained/ 4. Mani, M., Pillai, R.: Impact of dust on solar photovoltaic (PV) performance: research status, challenges and recommendations. Renew. Sustain Energ. Rev. 14(9), 3124–3131 (2010) 5. Nimmo, B., Seid, SAM.: Effects of dust on the performance of thermal and photovoltaic flat plate collectors in Saudi Arabia: preliminary results. In: Veziroglu, T.N., (ed.) Proceedings of the 2nd Miami International Conference on Alternative Energy Sources. pp. 223–225 (1979) 6. Rao, A., Pillai, R., Mani, M., Ramamurthy, P.: Influence of dust deposition on photovoltaic panel performance. Energy Procedia 54, 690–700 (2014) 7. Salim, A., Huraib, F., Eugenio, N.: PV power-study of system options and optimization. In: Proceedings of the 8th European PV Solar Energy Conference. (1988) 8. Sayigh, A.A.M.: Effect of dust on flat plate collectors. In: de Winter, F., Cox, M., (eds.) Sun: Mankind’s Future Source of Energy; Proceedings of the International Solar Energy Congress, New Delhi, vol. 2, pp. 960–4. Pergamon Press, NY (1978) 9. Kazem, Hussein A., Chaichan, M.T.: The effect of dust accumulation and cleaning methods on PV panels’ outcomes based on an experimental study of six locations in Northern Oman. Sol. Energy 187, 30–38 (2019) 10. Tsamaase, K., Ramasesane, T., Zibani, I., Matlotse, E., Motshidisi, K.: Automated dust detection and cleaning system of PV module. Electrical Department, University of Botswana, Botswana 11. Baquedano, E., Torné, L., Caño, P., Postigo, P.: Increased efficiency of solar cells protected by hydrophobic and hydrophilic anti-reflecting nanostructured glasses. Nanomaterials 7(12), 437 (2017) 12. Anglani, F., Barry, J., Dekkers, W.: Development and validation of a stationary water-spray cleaning system for concentrated solar thermal (CST) reflectors, solar energy. 155, (2017)

Performance and Degradation Analysis of High-Efficiency SPV Modules Under Composite Climatic Condition Shubham Sanyal, Arpan Tewary, Rakesh Kumar, Birinchi Bora, Supriya Rai, Manander Bangar, and Sanjay Kumar

1 Introduction The high-efficiency SPV modules deliver 7–9% more energy per rated watt per year as compared to other modules [1], producing 20% more efficient and less degraded over composite climate over the first 25 years [2]. The modules are designed using back surface contact consist of 96 mono-crystalline cells. They are built using IBC (Interdigitated Back-Contact) and anti-reflective glass. High-efficiency back-contact cells use N-type silicon wafers as a base to exhibit LID (Light Induced Degradation) effect in the module. Being rear contact solar cells they can be easily interconnected and placed close to each other in the module as neglible space is required between them. Using a thin solar cell made from high-quality material increases the electron-hole pairs generation by light that is absorbed in the front surface can still be collected at the rear of the cell [3]. Thus, the space area of installation gets reduced for same energy output as compared to other SPV modules. Moreover, a high-efficiency module use cells built on a solid copper base which makes it is virtually impervious to the corrosion and cracking that is responsible for the degradation of conventional panels [4, 5]. Several works have been studied on solar photovoltaic modules through their longterm monitoring in the field or indoor accelerated ageing tests to match their performance against certain qualification standards and to calculate their annual degradation rates. These studies help us to check the suitability of the individual modules, to set up adequate qualification standards and to determine appropriate guarantee and payback periods. Though not much intensive work has been done particularly in the field of high-efficiency SPV modules, however a work by Smith et al. [6] shows that S. Sanyal · A. Tewary · R. Kumar Central University of Jharkhand, Ranchi, Jharkhand, India B. Bora (B) · S. Rai · M. Bangar · S. Kumar National Institute of Solar Energy, Gurugram, Haryana, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_82

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the high-efficiency Generation III solar cell is in high volume production at 23.4% efficiency. Optimization is presently underway with a median of 24.1% efficiency achieved entirely on production tools. Another work by Birinchi et al. [7] on “Performance of Sunpower Maxeon TM technology PV module in composite climate zone of India after 2 years of commissioning” revealed a degradation 0.5%/year in the normalized power (Table 1). Table 1 The following are some important works performed in the field of degradation of the solar photovoltaic modules Location

Climate Test duration description

Module Degradation Comments technology rate (%/year)

Reference

Perth (Australia)

Temperate climate

16–19 months c-Si p-Si a-Si CIS

0.5–2.7 1.0–2.9 18.8 12.6

Degradation is Carr and evaluated Pryor [8] through indoor I-V measurements

Mesa, Arizona (USA)

Desert climate

2.4–4 years 2.4–2.7 years 2.4–6.7 years

c-Si p-Si a-Si

0.4 0.53 1.16 (6.7 year) to 3.52 (2.7 year)

Degradation is Raghuraman evaluated et al. [9] through indoor I-V measurements. Initial scattering of the performance is high

Trinidad, California (USA)

Cool coastal climate

11 years

c-Si

0.4

Variability in maximum power was found to increase significantly over time

Hamamatsu (Japan)

Temperate climate

10 years

c-Si

0.62

Degradation is Sakamoto evaluated and Oshiro through indoor [11] I-V measurements

Golden, colorado (USA)

Mountain 8 years continental climate

c-Si

0.75

Continuously outdoor recorded data is used to evaluate degradation rate using PVUSA methodology and power equation

Reis at al. [10]

Marion and Adelstein [12]

(continued)

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Table 1 (continued) Location

Climate Test duration description

Module Degradation Comments technology rate (%/year)

Reference

Ispra (Italy)

Temperate climate

22 years

p-Si c-Si

0.3 (Silicone) 0.67 (EVA)

Degradation is Dunlop and evaluated Halton [13] through indoor I-V measurements. Modules encapsulated with silicon sealant showed less power degradation than modules encapsulated with EVA

Lugano Temperate (Switzerland) climate

20 years

c-Si

0.53

Indoor I-V measurements are done to evaluate degradation before and after the outdoor exposure

Realini et al. [14]

Negev desert (Israel)

Desert climate

3.4 years

p-Si

1.3

Tests were performed under concentrated light using mirrors (2.56 ratio)

Berman et al. [15]

NISE (India)

Composite 10 years climate

c-Si

5-16.5 (IEC 61,215 qualified) 17–33 (IEC 61,215 not qualified)

Indoor I-V measurements are done to evaluate degradation before and after the outdoor exposure

Sastry et al. [16]

The main objective of the present study is to find out the performance and degradation losses of the high-efficiency SPV power plant established at NISE Campus, Gurugram, Haryana, India by subjecting the modules of the power plant to I-V tracing, insulation resistance testing, thermal and visual imaging after two years of installation. This study will help us to find a conclusive data on the overall performance of the high-efficiency PV modules and will be helpful for other researchers

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Fig. 1 100 KWp high-efficiency solar PV power plant installed at the NISE Campus in Gurugram, Haryana, India

to carry out further work to choose the particular technology for real-time condition in the future specially in context to Indian climatic conditions.

2 Testing Set Up The 100 KWp high-efficiency SPV power plants at NISE, Gurugram consists of 22 strings with 14 modules in each string, i.e. 308 modules in total. The entire power plant is set up on a mounted structure facilitated with a single axis solar auto tracking system. The testing was done under following conditions: Irradiance- 700 to 1200 W/m2 , Relative Humidity Range- 48% to 86%, Ambient Temperature Range32 to 48 degree Celsius, Time for I-V Measurement- 11:00 h to 13:00 h (Indian Standard Time) at clear sky condition, Clearness index >0.6 (Fig. 1). The performance and degradation analysis of a power plant require long time observation and monitoring and execution of appropriate technologies based on the climatic and weather suitability conditions.

3 Methodologies 3.1 Determination of Temperature Coefficients and Curve Correction SPV module temperature coefficients are commonly required for calculation of shortcircuit current (α), open-circuit voltage (β) and peak power (δ) in real conditions. These parameters may be determined from measurements in natural sunlight or indoor condition. The coefficients so determined are valid at the irradiance at which

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Fig. 2 Module temperature reduction by artificial cooling method

the measurements were made. For linear PV devices, they are valid over an irradiance range of ±30% of this level. For calculating α, β and δ of the module, at first the module was covered with ice, along with a dark cloth (Fig. 2) and was cooled to a minimum possible temperature (about 20 °C) maintaining uniformity of the module temperature (variations less than 10 °C). Then I-V curves for the module were plotted at regular intervals along with an increase in temperature by removing the ice and cloth and exposing the module to direct radiation. The values of I sc , V oc and Pmax were plotted as functions of the device temperature and from the drawn slopes of the least-squares fit for current, voltage and Pmax , the values of α, β and δ for the module were calculated. Calculating of κ (curve correction factor) of the test sample (Fig. 3) is done by curve translation of the plotted current-voltage characteristics at a constant irradiance and at different temperatures, the required temperature range for analysis being (T 1 …T N ) and variation of the considered irradiance values being within ±1% of the measured values. Assuming T 1 as the lowest device temperature, sequentially all other (N − 1) curves recorded at higher temperatures (T 2… T N ) were translated to T 1 using 10 m/K. Starting from 0 m/K the value of κ was changed in steps of 1 m/K in the positive or negative direction until the deviation of the calculated Pmax value reached 0.5%. Thus, the correct estimation of ‘κ’ was calculated.

3.2 Determination of Internal Series Resistance Rs This experiment is performed at constant module temperature along with varying intensity of radiation and hence requires the use of a mesh (Fig. 3). The IEC 60,891

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Fig. 3 Module covered with mesh for variation of solar radiation at constant temperature

standard defines a procedure needed to translate the measured I-V of devices to standard test condition (STC), i.e. 1000 W/m2 irradiance and 25 °C module temperature. Correcting I-V characteristics to STC helps in comparing the performance of different modules and can be done using the following equations: I2 = I1 + Isc ((G 2 /G 1 ) − 1) + α.(T2 − T1 )

(1)

V2 = V1 − Rs (I2 − I1 ) − κ I2 (T2 − T1 ) + β.(T2 − T1 )

(2)

Here I 1 , V 1 are coordinates of points on the measured characteristics; I 2 , V 2 are coordinates of the corresponding points on the corrected characteristic; G1 is the irradiance measured with the reference device; G2 is the irradiance at the standard or other desired irradiance; T 1 is the measured temperature of the test specimen; T 2 is the standard or other desired temperature; The percentage degradation rates (degradation/year) of the six electrical parameters- I sc , V oc , V mp , I mp , Pmax and FF is calculated with respect to the nameplate rating of the representative module provided by the manufacturer using the following formula:

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Fig. 4 Thermal imaging of high-efficiency PV module

Rate of Degradation =

(Name plate data − Data normalized to STC condition) × 100% Name plate data × Time

(3)

4 Results and Discussion 4.1 Thermal Imaging An example of a module having a hotspot is shown in Figure (Fig. 4).

4.2 Insulation Testing This testing procedure is implemented to estimate the leakage current through the module which can further get a chance of PID effect. Due to high leakage current there can be a chance of introducing PID effect in the module (Fig. 5). From the above graph it can be concluded that most of the modules show dry resistance greater than or equal to 40 mega/m2 confirming the fact that the dry insulation conditions of most of the modules are within the desired range. From the above graph, it can be concluded that around 15 modules show wet resistance of less than or equal to 40 mega/m2 , 13 modules show wet resistance of less than or equal to 5,000 mega/m2 and the rest of them show wet resistance to be more than 40 mega/m2 which shows that the wet insulation conditions of the modules are very good.

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No. of Modules

a 30 20 10 0 0

40

500

1000 5000 10000 15000 20000 25000 30000 35000 40000 More

Dry Resistance of Modules in mega ohms/metre2 No. of Modules

b 20 10 0

Wet Resistance of modules in mega ohms/metre2 Fig. 5 a Insulation resistance range shown by the modules in dry case, b Insulation resistance range shown by the modules in wet case

4.3 Temperature Coefficient Determination The calculated values of α, β and γ are 3.52 × 10−4 A/K, −0.2818 V/K and − 3.68 × 10−3 W/K, respectively. The determination of temperature coefficients for high-efficiency modules are depicted in the following graphs (Fig. 6): The range of percentage degradation rates of the six electrical parameters of the high-efficiency SPV power plant is depicted in the following graph (Fig. 7). Considerable degradation can be observed in power (up to 8.6%) and voltage (up to 7.01%.). This could be attributed to the modules with more regions of hotspot as observed in the hotspot result (9 out of every 10 modules is affected by hotspot).

5 Conclusion The low-temperature coefficient proves that high-efficiency modules provide very good performance over wide temperature ranges. Degradation observed in highefficiency modules has a severe effect in terms of power and voltage. The current annual degradation rate of maximum power was found to lie in the range of 2.8% to 4.3% per year with a median of 3.9% for 2 years which shows that degradation has increased (previous degradation rate of the power plant was 0.5% per year). The

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Fig. 6 a Coefficient of I sc , i.e. α, b Coefficient of voc, i.e. β and c Coefficient of Pmax , i.e. γ for high-efficiency modules

25%~75% Range Median Line Mean Outliers

% Dedradation per year

10 8 6 4 2 0 -2 Voc

Isc

Vmp

Imp

Pmax

FF

Fig. 7 Percentage degradation per year of various electrical parameters of high-efficiency modules

main cause of degradation can be its weakest cell which affects its surrounding cells and cause the voltage and power loss in PV modules. From the insulation resistance data obtained it is seen that the modules are offering adequate insulation preventing electrical hazards and minimizing loss of power. However, a few cases were noted wherein adequacy was not seen that caused loss in voltage and power. Acknowledgements The authors would like to extend their sincere gratitude to National Institute of Solar Energy (NISE) (an autonomous institute under the Ministry of New and Renewable Energy,

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Government of India), Gurugram, Haryana, India and Central University of Jharkhand, Ranchi, Jharkhand, India for the help and support provided for this work.

References 1. Typically.: 7–9% more energy per watt, BEW/DNV engineering. SunPower Yield Report. (Jan 2013) 2. Campeau, Z., et al.: SunPower Module Degradation Rate, SunPower white paper, Feb 2013, Jordan, Dirk SunPower Test Report, NREL. (Oct 2012) 3. Verlinden, P.J., Swanson, R.M., Crane, R.A.: 7000 High Efficiency Cells for a Dream. Prog Photovoltaics Res Appl 2, 143–152 (1994) 4. SunPower Module 40-Year Useful Life, SunPower white paper, Feb 2013. Useful life is 99 out of 100 panels operating at more than 70% of rated power 5. Gxashekaa, A.R., van Dyka, E.E., Meyer, E.L., Evaluation of performance parameters of PV modules deployed outdoors. Renew Energ 2005, 30:611e20 (2005) 6. Smith, D.D., Cousins, P.J., Masad, A., Westerberg, S., Defensor, M., Ilaw, R., Dennis, T., Daquin, R., Bergstrom, N., Leygo, A., Zhu, X., Meyers, B., Bourne, B., Shields, M., Rose, D.: (SunPower Corporation, 77 Rio Robles, San Jose, CA, USA) “SunPower’s Maxeon Gen III solar cell: high efficiency and Energy Yield” 7. Bora, B., Sharma, A., Singh, Y.K., Jha, B.M., Singh, R., Rai, S., Bangar, M., Singh, R.R., Chakraborty, S., Singh, D., Saikia, K., Samdarshi, S.K., Sastry, O.S.: Performance of SunPower based Maxeon TM technology PV module in composite climate zone of India after 2 years of commissioning. 32nd European Photovoltaic Solar Energy Conference and Exhibition, At ICM—International Congress Center Munich, Germany, 32, (June 2016) 8. Carr, A.J., Pryor, T.L.: A comparision of the performance of different PV module types in temperate climates. Sol. Energ. 76, 285–94 (2004) 9. Raghuraman, B., Lakshman, V., Kuitche, J., Shisler, W., TamizhMani, G., Kapoor, H.: An overview of SMUDs outdoor photovoltaic test program at Arizona state university. Waikoloa, Hawaii, USA. In: IEEE 4th world conference on PV Energy conversion. pp. 2214–6, (2006) 10. Reis, A.M., Coleman, N.T., Marshall, M.W., Lehman, P.A., Chamberlain, C.E.: Comparison of PV module performance before and after 11-years of field exposure. Proceedings of the 29th IEEE Photovoltaic Specialists Conference, New Orleans, Louisiana, USA, (2002) 11. Sakamoto, S., Oshiro, T.: Dominant degradation of crystalline silicon photovoltaic modules manufactures in 1990. Barcelona, Spain. In: 20th European PV solar energy conference. (2005) 12. Marion, B., Adelstein, J., Long-term Performance of the SERF PV systems. NCPV and Solar Program Review Meeting. (2003), http://www.nrel.gov/docs/fy03osti/33531.pdf 13. Dunlop, E.D., Halton, D.: The performance of crystalline silicon photovoltaic solar modules after 22 years of continues outdoor exposure. Prog. Photovolt Res. Appl. 14, 53–64 (2006) 14. Realini, A., Bura, E., Cereghetti, N., Chianese, D., Rezzonico, S.: Study of 20-year old PV plant (MTBF project). Munich. In: 17th European PV Solar Energy Conference 2001. pp. 447–50, (2001) 15. Berman, D., Faiman, D.: EVA browning and the time dependence of I-V curve parameters on PV modules with and without mirror-enhancement in a desert environment. Sol. Energ. Mater. Sol. Cells 45, 401–412 (1997) 16. Sastry, O.S., Sriparn, S., Shil, S.K., Pant, P.C., Kumar, R., Kumar, A., et al.: Performance analysis of field exposed single crystalline silicon modules. Sol. Energ. Mater Sol. Cells 94, 1463–1468 (2010)

Energy Literacy of University Graduate Students: A Multidimensional Assessment in Terms of Content Knowledge, Attitude and Behavior Divya Chandrasenan, Jaison Mammen, and Vaisakh Yesodharan

1 Introduction Energy has the highest impact on our everyday life, yet not enough people are energy literate. We are constantly debating energy issues; still our society is woefully energy illiterate. Since energy challenges are intimately intertwined with our great environmental, economic, health, and social challenges, energy illiteracy among the society is a great problem for the nation. To create energy literate citizenry, we must address the lack of energy literacy as a matter of urgency in both formal and informal learning environments. Energy literacy requires an understanding of the relationships between people, society and the environment, and the various ways in which different energy resources and its use affect the relationship between them. An understanding of the cognitive, affective and behavioral aspects of energy resources and its related are very important [1]. We have now entered into an age with depleting fossil fuel resources as well as drastic climate changes. The perception regarding energy consumption and management needs a new direction, which should not be determined solely by professionals or politicians, but rather by the public and the future work force of the nation. The Ethiopian energy sector is facing the dual challenges of limited access to modern energy and heavy reliance on traditional biomass energy sources to meet growing demand. While Ethiopia has seen dramatic economic growth in recent years, sustaining this growth into the future will require dramatic expansion of energy supply. So efforts should be promoted to strengthen the energy efficiency D. Chandrasenan (B) University of Kerala, Thiruvananthapuram, Kerala, India e-mail: [email protected] J. Mammen Bahir Dar University, Bahir Dar, Ethiopia V. Yesodharan APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_83

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improvement programs which may help the country meet future energy demand while contributing to sustainable energy development [2]. A knowledge on the extent of energy literacy among the citizens of Ethiopia will enable the policy makers in developing new strategies related to energy security.

2 Ethiopia: Country Context 2.1 Economic and Social Development in Ethiopia Ethiopia’s overarching vision is to become a lower middle-income country by 2025. The Government of Ethiopia (GoE) has a well-articulated set of development strategies and policies. It has set up milestones for political, social and economic development in its growth and transformation plans (GTP). Furthermore, Ethiopia is aiming to become a carbon-neutral economy. Though Ethiopia is predominantly a rural nation, by 2030 a full third of Ethiopia’s population will live in cities, placing increasing pressure on urban service delivery and governance systems. Rapid population growth may increase unemployment, internal tensions and pressures on the use of natural resources. Ethiopia’s energy supply is primarily based on biomass. Even in urban areas, half the households rely on traditional biomass for cooking. Ethiopia’s energy sector must rise to the challenge of providing more reliable, healthy and affordable energy access to Ethiopians (including urban populations) over the next 20 years. The Government of Ethiopia has set goals to become a middle-income country by 2025, Ethiopia now stands as one of the pioneers in climate policy among the most vulnerable countries to climate change.

2.2 Energy Situation in Ethiopia A study of Ethiopian urban trends and demographics reveals that Ethiopia currently has a relatively low level of urbanization and still has a large and dispersed rural population. However, from this low urban base, the country is currently experiencing a relatively rapid rate of urbanization [3]. This expected urban population growth affects energy demand and provision and emphasizes the need for Ethiopian cities to ensure that growing energy needs are supported in a sustainable manner. The current energy profile of Ethiopia reflects a staggering dominance of bioenergy. The current energy mix greatly increases the country’s vulnerability to climate change. The reliance on fuelwood and charcoal in Ethiopia brings widespread land degradation, exposing bare soil to erosive rainfall and gulley erosion. As climate impacts increase, potentially reducing agricultural yields, there is likely to be a higher reliance on forest products for livelihoods, but further denudation and degradation are arising

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from this increased reliance, making climate change impacts such as heavy rainfall even more dangerous, as land degradation could exacerbate flooding. Energy demand is growing rapidly in Ethiopia, as a result of economic expansion and population growth. In Ethiopia, urbanization is currently about 20% but has grown at a considerably higher rate in the past decade. Urbanization may bring both opportunities and risks. Ethiopia has already committed itself to the pursuit of low-carbon development, through its climate-resilient green economy strategy. Ethiopia’s Power Sector Development Plan also targets renewables growth in the coming decade.

3 Motivation From the extensive review of literature, we could hardly find any studies on energy literacy in Ethiopia. Ethiopia is now trying to develop programs to secure its own energy security. In this backdrop, it is the need of the hour to find the extent of energy literacy among the students, especially the university students, who are considered as the future workforce of the nation. As the nation is moving toward the choice of clean energy making use of the abundant renewable energy resources, the nation needs a workforce proficient in energy engineering and management. Also, the increase in urbanization in Ethiopia restricts the society to be more energy efficient and more energy conscious. Hence, it is high time to show the society how energy literacy contributes to sustainable development and how to solve rural and urban energy scarcity and environmental problems by introducing energy development programmes. Hence, it is necessary to find the extent of energy literacy among the youth of Ethiopia, who is considered as the future workforce of the nation.

4 Research Objectives To get a clear understanding regarding the energy literacy among the youth of Ethiopia, we defined the following objectives: • To assess the level of energy literacy among the university students of Ethiopia. • To identify the energy literacy level of university students with respect to demographic variables. • To classify the students into groups based on their attitude toward energy. • To assess the behavior of students toward energy conservation.

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5 Methods 5.1 Data Collection The reported research is based on a campus-wide survey among students of the university during the 2018 spring semester. The sample included 170 students from the Department of Teacher Education and Curriculum Studies, College of Education and Behavioral Sciences (CEBS) and, 250 Students form the Engineering College, Bahir Dar University. We adapted the national energy literacy survey developed and launched by National Energy Foundation, USA [4]. The survey instrument consists of questions related to knowledge, attitude and behavior toward energy. The final survey instrument consisted of a questionnaire with questions divided into four categories: demographics, behavior, knowledge and attitudes. The survey instrument has a total of five questions to know about the demographic details including age, gender, the location of the home, annual family income and field of study. The behavior-related section included three questions to understand common actions and behaviors related to energy conservation and management. The knowledge section, termed as the energy literacy concepts, included 24 questions under five subsections, each related to the core topics as suggested by NEF in their study. About 22 Likert scale type questions were included under the section that focused on their attitudes. In addition to that, it included twelve questions to collect the opinion related to energy.

5.2 Data Analysis Data analysis was done for energy literacy scores, demographic differences, attitudes and behavior.

5.3 Students Demographics The participants of the study included engineering as well as education students of Bahir Dar University, Ethiopia. Demographics included students’ gender, school location, annual family income, the discipline of study. A total of 420 students took the energy literacy survey (Table 1).

Energy Literacy of University Graduate … Table 1 Frequency statistics of participants by gender, the locale, faculty and annual family income (N = 420)

883

Characteristics

Level

No. of respondents

% of respondents

Gender

Male

210

50

Female

210

50

Locale

Urban

165

39.3

Rural

255

60.7

Engineering

250

59.5

Education

170

40.5

10,001

5

1.2

Faculty Annual family income (Birr)

6 Result 6.1 Energy Literacy Score The analysis of the data indicates that the literary score of the undergraduate is 48.83 out of a maximum score of 100. The value indicates that the energy literacy among the university students is below average. From the total 420 respondents, only 60 (14.29%) of them come under an above average literacy score ranging between 60 and 70. Majority of the respondents (38.10%) belong to the average group which ranges between 50 and 60. But 36% of the respondents belong to the energy literacy score which ranges between 40 and 50, which is below average. Among the respondents, 3% have the least literacy score. Literacy scores of the group varied with respect to the five core energy topics. The students scored highest for the topic basic energy concepts (54.96) and least score was regarding sources and types of energy (39.48). A high score in the basic energy concepts reveals that the students are familiar with general definition and laws of energy. They were able to answer those questions related to energy transformations. But most of them failed to answer the questions related to renewable energy, consumer energy usage and general energy consumption trends. Figure 1 gives a detailed analysis regarding the overall assessment score. An analysis of the energy literacy score based on various demographic groups by gender, locale, field of study, annual family income and age was studied and a substantial variation in the scores was found. A graphical representation of Energy literacy scores of students by demographic variables is shown in Fig. 2. • Gender: The energy literacy score of male students (51.21) was found greater than the energy literacy score of female students (46.56). There is a substantial difference in the energy literacy scores with respect to gender. While male students show the maximum literacy score for basic energy concepts (61.51), the

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Fig. 1 Assessment scores

Fig. 2 Overview of energy literacy score with respect to demographic variables

female respondents score maximum in energy efficiency and conservation. Both the gender shows the least energy literacy score for the core topic, sources and types of energy. • Locale: With reference to the locale of the school, the respondents from the rural area are more energy literate than those from urban areas. This can be partially explained by the socioeconomic differences between urban and rural communities. This can be more explained with the direct experiences they have with respect to the topics discussed in the questionnaire. Though the urban students have

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knowledge regarding basic energy concepts (51.31), they need more knowledge in the area of sources and types of energy and energy efficiency and conservation (39.39). • Field of Study: It is found interesting that both the engineering and the education graduates show equal overall literacy score (48.5), with a greater score for energy use for engineering graduates (61.2) and energy efficiency and conservation for education graduates (66.91). From this finding, we may assume that the respondents were knowledgeable in the core themes they were taught in their field of study. • Annual Family Income: Students who belong to family with household income less than 2000 Birr were found to have a higher energy literacy score (50.13) than others having family income more than 2000 Birr. Those with the least energy literacy were found among students with family income between 10,000 and 20,000 Birr (32.33). Analysis of the energy literacy scores with respect to annual family income was done under five heads and it was found that as the higher the family income lower the energy literacy score.

7 Energy Attitudes The survey questionnaire consisted of 22 questions focused on the attitudes and 15 questions related to the opinion of university students related to energy conservation and management. It was intended to categories the broad thought patterns regarding energy. Hierarchical cluster analysis was used to classify the sample based on their attitude toward energy. Wards method with squared Euclidean interval was used for the study. The analysis grouped the sample into four clusters or group, each with different levels of attitude and perception. The four groups were named as agents of change, mindful wanderer, big talker and indifferent onlooker [5]. • Agents of Change: Cluster 4 had the highest mean of attitude score (2.68) and hence labeled as agents of change. They give higher priority on energy than others do and are involved in solving energy-related issues. About 7.14% of the total sample belong to this group. They are more knowledgeable about energy efficiency and conservation, energy tradeoffs and implications and basic energy concepts. • Mindful Wanderer: Cluster 2 has a mean attitude score of 2.45 and hence named as mindful wanderer. Around 52% of the whole sample belong to this group. These students know the significance of energy but are unaware of the ways and mean to make a difference by themselves. They keep an average score with respect to all core themes of energy. • Big Talker: This group agrees that energy is important but is unwilling to change their energy-related behavior or they would not compromise their lifestyle for energy conservation or management. It is found that around 21% of the sample are big talkers with a mean of attitude score of 2.33. They have high literacy score

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Fig. 3 Energy literacy across categories and groups

for basic energy concepts (74.07), but they have low score for energy efficiency and conservation (37.5). These groups talk a lot but hard to put it into practice. • Indifferent Onlooker: The 1st cluster is labeled as indifferent onlooker and it covers around 19% of the sample. They do not have opinion or concern regarding energy or energy-related issues. They always wish to be in the comfort zone. The group has the least mean of attitude score of 2.11, whereas all others come under above average score. The indifferent onlooker has least energy literacy score for energy use (23.75) and sources and types of energy (33.33) (Fig. 3).

8 Energy Behaviors There were seven questions which reflect the energy behavior of the respondent. The analysis shows that 60% of the sample is particular in turning off all the lights before leaving a room. But only 34% of the respondents were concerned about unplugging the devices that are not being used. A highly interesting finding is that though only 28% of the sample search for energy efficient products, 60% of them is interested in encouraging friends or families to be more energy efficient (Table 2). Also, the study revealed that most of the respondent (67%) was interested to find information regarding energy from search engines rather than from social media or government websites. They also confirmed family as the next choice (62%). The survey also included a question to gather information on their choice of research in

Energy Literacy of University Graduate … Table 2 Percentage of students showing positive energy behavior

887

Energy behaviors

Percentage (%)

Turn off all lights before leaving a room

60.71

Unplug electronic devices that are not being 34.52 used Encourage friends or family to be more energy efficient

60.71

Consciously choose to travel without a car

35.71

Actively search for products that are more energy efficient

28.57

the next six months. Out of the nine choices, they were asked to pick two. Around 67% of the sample focused on energy efficiency, whereas 33% focused on energy resources. Only 5% are interested in energy safety-related research.

9 Discussion of Results Regarding the energy literacy concepts, it was observed from the survey that students tended to score worse with respect to those related to sources and types of energy questions. The section included questions related to renewable energy resources. A low score under this category points out that the Ethiopian students lack knowledge regarding renewable energy sources. The Ethiopian government is trying hard to make rapid progress in its renewable energy sector. Their target is to become the wind power capital of Africa. In this backdrop, it is necessary that the future work force should be aware of all possible renewable energy resources and the curriculum for school as well as higher education should include this area with utmost importance. The curriculum planners should systematically incorporate active pedagogical techniques, such as the issue-based teaching approach, to increase the diversity of activities for engaging students and ultimately improve their civic knowledge, skills and participation. Many studies done in Ethiopia regarding environmental education indicated some gaps in curriculum design and teacher preparation [6]. The present survey reports that male students demonstrated higher energy literacy as compared to their female counterparts. The female students showed their expertise in energy efficiency and conservation which indirectly shows their positive attitude toward energy conservation. This can be substantiated by the report by De Waters and Powers [7]. They found that females had significantly more positive energy-related attitudes and values than males, but there found no difference in their cognitive or behavior scores. Unlike the present result, many studies have shown that females tend to have a greater positive attitude toward energy issues than males (e.g., [8, 9]). Many studies report gender disparities in energy and environmentally related knowledge [10]. Generally, urban students demonstrated higher energy-related knowledge as compared to their rural secondary school counterparts. But unlike other studies,

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the rural students of Ethiopia showed a very higher score of energy literacy than the urban students. Though the urban students excelled the rural students with respect to the knowledge related to basic energy concepts, in all other sections related to knowledge the rural students scored better. Urban students showed very low literacy score regarding energy efficiency and conservation. Ethiopia is a country where rapid urbanization is taking place. A lack of awareness regarding the conservation and management of energy among the urban population will lead to disastrous effect on energy security.

10 Conclusions and Recommendations The above findings and discussions based on the data collected from university students in Ethiopia show that the energy literacy level among them is low. The overall energy literacy score of the students from this research is 48.83, which is below average. The result focuses on the urgent need of equipping students, who are the future work force of the nation with adequate energy knowledge for the energy future of the country. Though Ethiopia has ample scope of renewable energy-related job possibilities the future workforce is relatively unaware of its scope and prospects. Based on the research, we recommend the implementation of an education policy which should make energy education a core part of curriculum at all levels of education. It has been argued that energy-related knowledge would be best learned in schools because “energy awareness” is mostly developed at a young age [11]. So considerable importance should be given for the curriculum refinement of school education with respect to energy knowledge. E. Professional development programs for educators based on energy should be implemented at all levels of education. Engineering students should be given training on technologies associated with renewable energy. Compliance with Ethical Standards Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Conflict of Interest The authors declare that they have no conflict of interest. Informed Consent Informed consent was obtained from all individual participants included in the study.

References 1. Bodzin, A., Fu, Q., Peffer, T., Kulo, V.: Developing energy literacy in U.S. middle level students using the geospatial curriculum approach. Int. J. Sci. Educ. 35(9), 1561–1589 (2013)

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2. Mondal, M.A.H., Bryan, E., Ringler, C., Mekonnen, D., Rosegrant, M.: Ethiopian energy status and demand scenarios: prospects to improve energy efficiency and mitigate GHG emissions. Energy 149, 161–172 (2018) 3. Schmidt, E., Kedir, M.: Urbanization and Spatial Connectivity in Ethiopia: Urban Growth Analysis Using GIS. ESSP Discussion Papers 3. International Food Policy Research Institute (IFPRI) (2009) 4. Questionnaire, A.: National Energy Literacy Survey, 1–12 (2017) 5. Richards, E., Foundation, N.E.: National Energy Literacy Among High School Seniors and Recent Graduates (n.d.) 6. Dalelo, A.: Efforts to empower teachers in Ethiopia to address local environmental problems: achievements and limitations. Int. Res. Geogr. Environ. Educ. 18(3), 211–226 (2009) 7. De Waters, J.E., Powers, S.E.: Energy literacy of secondary students in New York State (USA): a measure of knowledge, affect, and behavior. Energy Policy 39(3), 1699–1710 (2011) 8. Barrow, L.H., Morrisey, J.T.: Ninth-grade students’ attitudes toward energy: a comparison between Maine and New Brunswick. J. Environ. Educ. 18, 15–21 (1987) 9. Lawrenz, F., Dantchik, A.: Attitudes toward energy among students in grades 4, 7 and high school. Sch. Sci. Math. 85(3), 189–202 (1985) 10. Yutaka, A., Keiichi, N.I., Hideyuki, O., Eiji, Y.: Investigating energy literacy and its structural model for lower secondary students in Japan. Int. J. Environ. Sci. Educ. 12(5), 1067–1095 (2017) 11. Zografakis, N., Menegaki, A.N., Tsagarakis, K.P.: Effective education for energy efficiency. Energy Policy 36, 3226–3232 (2008)

Waste-to-Energy: Issues, Challenges, and Opportunities for RDF Utilization in Indian Cement Industry Prateek Sharma, Pratik N. Sheth, and B. N. Mohapatra

1 Introduction Cement industry plays a decisive role in the growth of the economy of a country resulting in the development of the nation. Cement consumption in India is still around 235 kg per capita against a global average of 520 kg per capita which shows significant potential for the growth of dynamic cement industry [1]. The current annual installed capacity of cement industry in 2018 is 509 million tonnes with cement production is around 297.5 million tonnes, which comprises of 143 integrated large cement plants, 102 grinding units, 62 mini cement plants, and 5 clinkerisation units [1]. The nation’s cement demand is expected to reach 550–600 million tonnes per annum (MTPA) by 2025 [2]. The Indian cement sector is one of the most energy-efficient in the world and continuously adopting the latest technologies for energy conservation. The reported average specific thermal energy consumption of Indian cement industry is around 3.1 GJ/tonne clinker while the global average is 3.5 GJ/tonne clinker. Similarly, the reported average specific electrical energy consumption is 80 kWh/tonne cement while the global average is 91 kWh/tonne cement [3]. One of the pressing issues of the industry for last one decade is enhanced utilization of alternate fuels for co-processing with coal to achieve high percentage thermal substitution rate by substituting conventional fuels. However, with persistent efforts of cement industry, Government of India and other stakeholders, percentage thermal substitution rate for Indian cement industry has improved to around 4% now as compared to dismal 1% approx 4 years back [1]. Many cement plants are installing co-processing and preprocessing platforms to increase alternative fuel utilization. A P. Sharma · B. N. Mohapatra National Council for Cement and Building Materials, Ballabgarh, Haryana, India P. N. Sheth (B) Birla Institute of Technology and Science, Pilani Campus, Pilani, Rajasthan, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_84

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cement plant in Madhya Pradesh has installed a preprocessing system along with dedicated material handling and transportation and their TSR increased from 0.64 to 8.8% [4].

2 Current Status of MSW Municipal solid waste is defined as household waste, commercial and market area waste, slaughterhouse waste, institutional waste (e.g., from schools, community halls), horticultural waste, waste from road sweeping, silt from drainage, and treated biomedical waste. Net generation of MSW annually in India is around 62 million tonne [5]. The major metropolitan cities (Delhi, Mumbai, Kolkata, Chennai, Bengaluru, and Hyderabad) generate the maximum volume of solid waste, ranging from 4000 TPD (tonne per day) in Hyderabad to 9260 TPD in Delhi. Modification in Municipal Solid Waste (Management and Handling) Rules 2000 further highlighted the need for source segregation and assigning the responsibility to waste generators themselves. Bengaluru and Pune are the only two large cities with around 50% of the waste segregated at source. Among mid-sized cities, Indore and Mysore have achieved 90 and 95% segregation at source, respectively [6]. Recyclable waste having combustible fractions are picked up by waste pickers has been a traditional practice in the country which is being processed in the informal sector.

2.1 Composition of MSW The quantity and characteristics of solid waste may vary from place to place depending upon the type of population and their living style. Physical characteristics of municipal solid waste in some of the Indian cities are reported in Table 1. Table 1 Physical characteristics (amount in %) of municipal solid wastes in Indian cities [7] Population range (in million)

No. of cities surveyed

Paper

Rubber, leather, synthetics

Glass

Metal

Compostable matter

Inert material

0.1–0.5

12

2.91

0.78

0.56

0.33

44.57

43.59

0.5–1.0

15

2.95

0.73

0.56

0.32

40.04

48.38

1.0–2.0

9

4.71

0.71

0.46

0.49

38.95

44.73

2.0–5.0

3

3.18

0.48

0.48

0.59

56.57

49.07

5.0 and above

4

6.43

0.28

0.28

0.80

30.84

53.90

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2.2 Treatment and Disposal of MSW Disposal of MSW is still an issue of concern in India despite the enactment of various legislations. There are mainly six types of MSW disposal practices in India; open area land filling, sanitary landfills properly designed with lining and leachate collection wells, composting, waste to energy and RDF [8]. Composting and waste to energy (WTE) are significant waste disposal methods in India. The combustibles consisting of paper, textile, polythenes, diapers, sanitary napkins, rags, leather, rubber, nonrecyclable plastic, and other non-biodegradable fraction of MSW are processed into refuse-derived fuel [9]. RDF plants were set up in India and one of the few RDF plants still under operation is MSW processing plant at Jaipur set up by M/s Ultratech Cement Ltd. with Jaipur Municipal Corporation. The facility was designed to process about 500 TPD of MSW and to generate around 150 TPD of refuse derived fuel (RDF) in the form of fluff [10]. This RDF is further transported to different Ultratech cement plants for co-processing as fuel.

3 RDF Utilization in Cement Industry Cement industry has always acted as a backbone for utilization of the industrial waste like fly ash from thermal power plants, slag from steel plants and other types of hazardous/non-hazardous waste but same is not happening in the case of RDF. Even after consistent efforts of cement industry, government, and other stakeholders, % TSR based on RDF is very low. As per MoHUA report, the contribution of MSWbased RDF in the Indian cement industry is only around 0.6% out of the total thermal substitution of 4%. The industry aims to achieve total TSR of around 25% by the year 2025 which could be possible only by having a significant share of RDF as an

Fig. 1 Waste availability for achieving 25% TSR by 2025 in the Indian cement industry [11]

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alternative fuel. Figure 1 shows the waste availability for achieving 25% TSR by 2025 in the Indian cement industry [11]. Mohapatra et al. [10, 12] shared his experiences of RDF utilization, agro waste, and tire chips as co-fuels for coal in cement manufacturing process based on a trial run at M/s Vikram Cements Ltd. RDF was brought from Jaipur MSW processing plant. M/s Vikram Cement was able to achieve around 3% TSR from RDF out of the total 5% TSR. Initially, the yield of RDF was around 12–13% with calorific value (CV) of only 1500 kcal/kg which was increased to 1900–2200 kcal/kg after reprocessing and double refining. Mixing waste polythene and plastics with RDF enhanced CV further to around 2500–2700 kcal/kg. The clinker mineralogy without using RDF and during the use of RDF indicated normal clinker phases like C3 S, C2 S, C3 A, C4 AF and free lime. In a nutshell, it was concluded that it does not have any negative impact on the engineering properties of cement [10, 12, 13].

3.1 Global Scenario Co-processing in cement plants is being practiced since 1970 in developed countries. Countries like Germany, Poland, and Austria have vast experiences in RDF utilization. Some European countries like the UK and Ireland are having high landfill tax thus exporting RDF to other countries [5]. In Germany, landfilling was banned in Germany in 2005 and by 2008; Germany replaced 54% of conventional fuel usage by RDF in cement plants [5]. Alternative fuels utilization in Polish cement industry gained momentum in the last two decades. Landfill tax on MSW and a landfilling ban on separately combustible waste in 2013 forced waste generators and waste management companies to look for other disposal options. Thermal substitution rate of Poland cement industry is very high at above 60% and some plants have TSR of more than 85% and their major share is RDF. 1 million tonne of coal was replaced by RDF in Poland cement industry in 2016 [5]. The Kujawy Cement Plant in Poland of 4500 TPD has achieved 75% TSR by solid alternative fuels [14]. Preprocessed RDF is being received by the plant. The plant adopted latest laboratory facilities for quick and accurate assessment of key parameters, i.e., Hg, Cl, moisture, size, etc. for acceptance of RDF. A company called Novago has an annual RDF production capacity of 200,000 tonnes and the fuel is supplied to cement plants and power plants [14]. CBR Heidelberg Cement at Lixhe, Belgium, is utilizing 35% coal and 65% alternative fuel. RDF is received from M/s Recyfuel in processed form and is being utilized in kiln and calciner burners. Austrian cement industry also has high % TSR to the tune of 80%. There are legal compliance, quality checks, and quality assurances which have helped increasing RDF utilization in cement plants. Japan has a different scenario. Due to land scarcity, the landfill is not a valid option hence Japan mostly relies on thermal treatment of RDF. Around 43 million tonnes of MSW was generated in 2015 and 81% is incinerated or gasified [5]. China which is the largest cement producer is using gasification technique to process MSW to combustible gas before firing it in calciner [15].

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3.2 RDF Issues RDF producers process MSW and segregation, shredding and screening of the waste is done to make it worth utilization for industries. This becomes part of the RDF production cost. Cement plants are mostly located far from cities while RDF is mostly available near cities resulting in the high cost of transportation [16]. These factors bring the overall cost close to the price of conventional fuel in India and sometimes even higher in cement plants. RDF is generally associated with high ash content which has no heat value and is undesirable for the user [6]. Further to be utilized as fuel replacing conventional fuel, cement plants have to make a certain investment for handling, storage, and feeding of RDF. Ministry of Environment, Forest and Climate Change (MoEFCC) notified emission norms for co-processing of waste by cement plants vide Gazette Notification dated 10th May 2016 [17, 18]. Apart from the parameters criteria pollutants like PM, SO2 and NOX , emission limits for other pollutants, i.e., HCl, SO2 , CO, TOC, HF, NOX , total dioxins and furans, Cd, Tl their compounds, Hg and its compounds, Sb, As, Pb, Co, Cr, Cu, Mn, Ni, V their compounds were also notified. The real challenge lies ahead to meet these emission norms along with consistent clinker quality during higher percentage TSR based upon RDF with high chloride content and heavy metals. The technology employed for MSW processing, an economic model based on compost and RDF and lack of appropriate market policy for RDF and compost is some of the reasons due to which RDF concept never got materialized in India [19].

3.3 Operational Issues Faced by a Cement Plant Plants utilizing RDF in India are facing some operational issues. It has been observed that there is an increase in CO formation at preheater outlet which is indicative of incomplete combustion. Operators resort to increasing in air input to the system with optimized fuel ratio to optimize combustion. This results in an increase in preheater fan power consumption which handles more gas volume. Most of the cement plants consist of screw weigh feeding systems for continuous feeding of RDF which often face jamming issues due to heterogeneous and sticky nature of the waste leading to operational disturbances. Shredders which are part of the preprocessing systems for RDF preparation in cement plants also have jamming issues. Some of the plants having high % TSR due to RDF are facing coating issues on the refractory lining inside the kiln system. RDF ash has a high content of chlorine and alkalies which gets combined with petcoke sulfur resulting in coating formation. Circulation of volatile salts increases and clogging arises in lower preheater cyclones and riser pipe [20]. If plants are using multi fuels and RDF is one such fuel, plants find it difficult to finalize the exact location of alternative fuels firing in calciner due to its varying characteristics. However, the injection point should be such to maximize the

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residence time in an O2 -rich atmosphere. One more problem faced by cement plants while handling multi-fuel is plugging of chutes at impact points resulting in high wear on chute surfaces, thereby maintenance issues [21]. It is anticipated that with the increase in % TSR, plants in coastal regions and regions having high chloride, alkali content may have to go for kiln bypass systems. The challenge will be to utilize/dispose the huge quantity of bypass dust. Since RDF has high ash content which becomes part of clinker matrix, some modification in the raw mix (either high-grade limestone or some other corrective) is required to lower down the lime saturation factor depending upon the ash quality. Skill development of control room operators and laboratory analysts is also important to handle fuels of different nature having varying characteristics which is still not at par with global standards. Rajamohan et al. [22] shared his experiences and challenges faced during RDF utilization at Dalmia Cement (Bharat) Limited, Dalmiapuram, in which 80 TPD RDF was used by the plant. CV achieved was around 1800 kcal/kg with moisture over 25% while the expected CV was 2000–2500 kcal/kg with a moisture content of around 15–20%. The main technical challenges faced by the plant are inconsistent quality, less calorific value, higher moisture which needs additional heat for drying and odor. In financial terms, the landed cost of RDF along with feeding/handling cost in the plant could not reach breakeven as compared to conventional fuel.

4 Initiatives taken by the Government of India The Ministry of Environment, Forest and Climate Change (MoEFCC) notified its new solid waste management (SWM) rules 2016. Waste-to-energy in cement plants using co-processing systems got due importance in new rules. All industrial units using fuel and located within 100 km of a solid waste-based RDF plant have to replace at least 5% of their fuel requirement by RDF. Non-recyclable wastes having a calorific value of 1500 kcal/kg or more are to be used to generate energy at wasteto-energy plants or as feedstock for the preparation of refuse-derived fuel for cement kilns. High-calorific wastes shall be used for co-processing in cement or thermal power plants [20]. Recently, certain guidelines have been released by the Ministry of Housing and Urban Affairs (MoHUA) in October 2018 to promote RDF utilization in various industries inline with the objectives of Swachh Bharat Mission. RDF grading has been done into RDF Grade I, II, and III and SCF based upon various quality parameters where RDF Grade I NCV > 4500 kcal/kg, Grade II NCV > 3750 kcal/kg and Grade III NCV > 3000 kcal/kg. Ash, moisture, chlorine, sulfur are other parameters being notified. This can be compared to ASTM RDF classification as shown in Table 2. The commercial acceptability of processed RDF was agreed at a minimum Rs. 0.4 per 1000 kcal/kg with cost/tonne varying from Rs. 600 to 1800 per tonne while maximum at Rs. 0.8 per 1000 kcal/kg with cost/tonne varying from Rs. 1200 to 3600 per tonne. However, the transportation cost of RDF within 100 km to be borne by cement plants and beyond 100–400 km to be borne by ULBs [5].

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Table 2 ASTM refuse derived fuel classifications [23] ASTM classification

Description

RDF-1

MSW used as fuel in the as-discarded form

RDF-2

MSW processed to a coarse particle size with or without ferrous metal separation

RDF-3

MSW processed to a particle size such that 95% by weight passes through a 55 mm square mesh screen and from which most metals, glass and other organics have been removed

RDF-4

MSW processed into a powdered form, 95% by weight passes through a 10 mesh screen and from which most metals, glass, and other organics have been removed

RDF-5

MSW that has been processed and densified (compressed) into the form of pellets, slugs, cuvettes or briquettes

RDF-6

MSW that has been processed into a liquid fuel

RDF-7

MSW that has been processed into a gaseous fuel

ASTM classifies RDF into seven forms based on solid, liquid, and gaseous state while MoHUA guidelines define RDF as four grades as per end-user requirements. RDF1 in ASTM does not represent any heat value as it is fuel in discarded form but MoHUA starts grading with segregated combustible waste (SCF) of >1500 kcal/kg. RDF 2, 3, and 4 as per ASTM have heat values ranging from 2495 to 4159 kcal/kg which are comparable to Grade 1, 2, and 3 heat values ranging from 3000 to 4500 kcal/kg in MoHUA guidelines. RDF 6 and RDF 7 in ASTM standards are solid RDF to liquid or gaseous fuel with heat value in the range of 5545–8317 kcal/kg and 1763–4400 kcal/m3 , respectively. There is no such categorization in MoHUA guidelines since RDF usage has been considered as solid fuel.

5 Cement Industry Perspective Considering worst-case scenario in which a cement plant within 100 km of RDF plant is providing Grade I RDF @ Rs. 3600 per tonne and assuming Rs. 500 per tonne transportation cost, the total cost at plant gate works out to Rs. 4100 per tonne which is comparable to landed cost per tonne of coal and becomes economically unviable. However, the minimum scenario appears viable as a total cost at plant gate works out to Rs. 2300 per tonne which can be feasible. But the consistent availability of Grade I RDF is also not certain and only Grade I and Grade II are acceptable for the cement industry. Impact on specific heat consumption, clinker production, and more utilization of higher grade limestone are some other cost implications apart from the capital investment while utilizing RDF. Thus, high % RDF utilization always remains a challenge for the cement industry.

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6 Recent Trends In the commercial market, RDF is often classified under different grades depending upon the levels of sorting and segregation, size and homogeneity and moisture level. Government and RDF plants are exploring opportunities to produce very high-quality fuel of GCV value ranging from 2500 to 3500 kcal/kg for a different industrial application other than cement plants where RDF can be utilized in different forms. Improving the RDF quality on the basis of size and density separation from heterogeneous waste can be done using Ballistic separator. Two-dimensional fraction like flexible cardboard, paper, and the plastic film will carry over the top to the front of the machine. Rigid and three-dimensional plastic and metal containers will exit at the back of the machine. The third fraction including fines sorted will fall through the sieve mesh of the paddles to ensure minimal loss of recyclables [24]. Converting RDF into RDF briquettes or briquetting RDF along with sawdust has the potential to be utilized in different boilers. A new concept flourishing is an integrated waste management plant for MSW processing which adopts composting, RDF with the sanitary landfill at one place enabling scientific management of MSW [25]. It is anticipated that such types of plants will be coming up soon in Pali, Alwar, Bhilwara, and Bharatpur regions of Rajasthan with the support of Government and Municipal Corporation under Swachh Bharat Mission [26]. Other techniques like RDF gasification to produce synthesis gas are also in the pipeline to improve the quality of RDF and further research options are open in this area.

7 Conclusions It is anticipated that once the quality of RDF will get improved with envisaged new techniques, its demand will increase as it can be directly sold to cement plants or can be used in boilers for a steam generation as well as for other industries. Hence, cement plants need to be more proactive as the market is now open for all industries for RDF usage and 5% TSR mandate will push all industries to take up the RDF. Losing RDF to other industries will be a huge setback for the Indian cement industry to achieve their targets of 25% TSR by 2025 as RDF has already been identified as one of the promising fuel for co-processing. A continual improvement in the quality of RDF is the need of the hour and Research institutes like National Council for Cement and Building Materials (NCCBM) may take lead in this direction and further explore different alternative fuels which can be mixed with RDF or conversion of RDF to liquid/gaseous fuel to achieve a consistent good quality fuel.

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References 1. Mohapatra, B.N., Ashutosh, S., Chaturvedi, S.K., Devendra, Y., Ankur, M., Kumar, S.S., Prateek, S., Anand, B., Ramchandra, R.M.V., Giasuddin, A., Veddy, V., Vivek, S.: The Cement Industry 2018, 1st edn, p. I. National Council for Cement and Building Materials (2018) 2. Jayanta, D., Sanjeev, S., Krishna, G., Jamshed, C., Shukla, R.C., Rakesh, R., Ahluwalia, S.C., Rajesh, S., Anand, M., Jacob, M.: Cement Vision 2025: Scaling New Heights, p. 3. A.T. Kearney (2014) 3. Abhay, B., Pankaj, K., Ashok, K., Sunil, K., Vivek, N., Kumar, A.A., Rita, A., Nitin, J., Piyush, S., Ravindra, K., Venkatagiri, K.S., Kiran, A.P.V., Muralikrishnan, K., Balasubramanian, M.B.: Enhancing Energy Efficiency Through Industry Partnership. Perform Achieve and Trade, BEE, GIZ, New Delhi (2018) 4. Low Carbon Technology Roadmap for the Indian Cement Sector: Status Review 2018. WBCSD, CSI, CII, IFC, CMA (2018) 5. Guidelines on Usage of Refuse Derived Fuel in Various Industries. Ministry of Housing and Urban Affairs, Government of India (2018) 6. Judge, A.I., Utkarsh, P.: Solid Waste Management in India: An Assessment of Resource Recovery and Environmental Impact, p. 5. Indian Council for Research on International Economic Relations (2018) 7. Neha, G., Vinit, K., Kumar, Y.K.: A review on the current status of municipal solid waste management in India. J. Int. Sci. (2015) 8. Action Plan for Enhancing the Use of Alternate Fuels and Raw Materials in the Indian Cement Industry, pp. 8–9. Institute for Industrial Productivity, Holtec Consulting Private Limited (2013) 9. Regina, D., Vaishali, N., Bineesha, P., Shweta, D.: Status Paper on Utilisation of Refuse Derived Fuel (RDF) in India, p. 12. GIZ, New Delhi (2013) 10. Mohapatra, B.N., Saji, J., Gupta, R.M.: Successful utilization of processed municipal solid waste (RDF) as an alternate fuel in cement kiln. In: 11th NCB International Seminar, National Council for Cement and Building Materials, pp. 88–90. New Delhi (2009) 11. Status Paper on Alternate Fuel Usage in the Indian Cement Industry. CII (2018) 12. Mohapatra, B.N., Kumar, V.S., Chander, S.: Indian experience of using AFR in cement kiln. Ind. Angles 3, 7–13 (2014) 13. Gautam, S.P., Jain, R.K., Mohapatra, B.N., Joshi, S.M., Gupta, R.M.: Energy recovery from solid waste in cement rotary kiln and its environmental impact. In: 24th International Conference on Solid Waste Technology & Management, Philadelphia, PA, pp. 15–18 (2009) 14. NCCBM Databank (2016) 15. Garbage Incineration via Cement Kiln (CKK System). Anhui Conch Kawasaki Engineering Co. Ltd (2013) 16. https://www.dailypioneer.com/2017/columnists/making-wealth-from-waste.html. Accessed 2019/03/10 17. Gazette Notification No. GSR 497 (E), pp. 5–6 (2016) 18. Guidelines for Pre-Processing and Co-Processing of Hazardous and Other Wastes in Cement Plant as per H&OW (M & TBM) Rules, 2016. Central Pollution Control Board (Ministry of Environment, Forest & Climate Change, Government of India), p. 17 (2017) 19. Rajkumar, J., Ahmed, S.: Status and challenges of municipal solid waste management in India: a review. Cogent Environ. Sci. 8 (2016). ISSN: 2331-1843 20. Alternate fuels and raw materials in cement industry. Special Publication. In: 3rd International Conference on Alternate Fuels and Raw Materials in Cement Industry, New Delhi, pp. 24–25 (2017) 21. Kapil, K., Mohapatra, B.N., Ashutosh, S.: Transfer chute design for solid alternative fuels. Indian Cem. Rev. 33, 52–53 (2019) 22. Rajamohan, R., Vinayagamurthi, K., Kumar, R.A.K.: Municipal solid waste (MSW) usage in Indian cement plants. In: 15th NCB International Seminar, National Council for Cement, Concrete and Building Materials, p. 19. New Delhi (2017)

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23. Robinson, W.D.: The Solid Waste Handbook: A Practical Guide. Wiley-Interscience Publication (1986) 24. www.metaltechsystems.com. Accessed 2019/03/11 25. Detailed Project Report Municipal Solid Waste Management Municipal Council—Pali, Rajasthan. Rollz Material Handling Systems Pvt. Ltd., New Delhi (2017) 26. https://municipalika.com/media_files/Bhupendra_Mathur.pdf. Accessed 2019/03/11

Predict the Effect of Combustion Parameter on Performance and Combustion Characteristics of Small Single Cylinder Diesel Engine D. K. Dond and N. P. Gulhane

1 Introduction There are a very large number of small single cylinder diesel engines used in many areas such as decentralized power generation sector, agricultural farm machinery and irrigation purposes, which have not yet reaped the benefits of the new technology. Many of these engines use mechanical fuel injection system for delivering the required quantity of fuel depending upon varies conditions like for different speed and load requirement. Such engines does not having precise control over the delay period, injection pressure, duration of injection and rate of injection. Use of high fuel IPs and optimizing the injection strategies are extremely important for further improvements as well in order to optimize compression ignition (CI) engines. In order to observe the effect of change in combustion parameter on engine performance before retrofitting the new technology in the available diesel engine, mathematical modelling is an important tool. Performance and emission characteristics of a single cylinder diesel engine for different EGR rate and temperature when the engine is operated at different IT and load was investigated by Rakopoulos et al. [1]. For the same two zones, mathematical modelling was done using the first law principle of thermodynamics. Datta et al. [2] had done the mathematical modelling by using semi-empirical correlation, which was derived from experimental results. The aim of the study was to analyze the performance and emission characteristic of a diesel engine which was fuelled with palm stearin biodiesel with the addition of ethanol and methanol separately. A mathematical model has been developed by Ambrós et al. [3] with which it is able to predict the performance of single cylinder diesel engine fuelled with ethanol. Water D. K. Dond (B) · N. P. Gulhane Veermata Jijabai Technological Institute, Mumbai, India e-mail: [email protected] N. P. Gulhane e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_86

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was added into it in different volume fraction. Mauro et al. [4] carry out engine performance evolution for different engine parameters by using a combined single zone zero-dimensional mathematical model and CFD three-dimensional model. Microscopic fuel spray characteristics of mineral diesel and rapeseed oil biodiesel had been investigated by Kegl and Lešnik [5] using a constant volume spray chamber which was maintained at high pressure using nitrogen cylinder and pressure regulating valve. With performed experiment on above-said setup and observation data, mathematical model had been developed for spray tip penetration and spray angle, in order to find out the effect on NOx emission due to EGR and split injection technique for single cylinder engine. Yu et al. [6] developed a new model for spray formation and their behavior during atomization and also investigate the influence of cavitations and turbulence on the high-pressure fuel spray. Model results were found good agreement with experimental data. Wang et al. [7] investigate macro- and microlevel diesel spray characteristic with a split injection strategy by using high-speed imaging and phase doppler particle analyzer technique and also suggest that high injection pressure with a split injection has been effective for complete evaporation of the fuel. Agarwal et al. [8] conducted experiments on single cylinder CRDI diesel engine for different IP as well as injection timing for diesel and karanja biodiesel. Experimental results show that the utilization of up to 10% karanja biodiesel blends in a CRDI diesel engine can be done for improving thermal efficiency and reducing emission without any significant hardware changes or electronic control unit recalibration. Sindhu et al. [9] develop a quasi-dimensional model by using first law and ideal gas equation. In this, they focused on combustion analysis to get information mainly about heat release rate, heat transfer losses, ignition delay, etc. Eloisa Torres-Jimeneza et al. [10] paper represents the development of a complete simulation model for a mechanical inline injection system feed with diesel, biodiesel, bio-ethanol blends. The comparison was made at full load and 75% partial load for various pump speeds (600, 800 and 1100 rpm). As per available literature, most of the research work concentrated on either of performance or emission characteristics or both of the diesel engines and not focused on combustion characteristic, which is important in order to get the accurate picture. This paper covers developed mathematical model, particularly for the small diesel engine. By using this, parameters of combustion like peak pressure, ignition delay, and heat release together with performance and combustion parameters are investigated theoretically. After making changes, the same model can be further utilized to predict the effect of the combustion parameters and their values for the newly developed CRDI small single cylinder diesel engine, which is the main objective of the work. Abbreviations bTDC

Before top dead centre

CR

Compression ratio

TDC

Top dead centre

IT

Injection timing

CFD

Computational fluid dynamics

IP

Injection pressure (continued)

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(continued) EGT

Exhaust gas temperature

SOI

Start of main injection

CRDI

Common rail injection system

rpm

Revolution per minute

BTE

Brake thermal efficiency

CO

Carbon monoxide

BSFC

Brake specific fuel consumption

NOx

Nitrogen oxides

EGR

Exhaust gas recirculation

CA

Crank angle

1.1 Description of the Model The developed mathematical model was divided into three parts. First is a closed phase, which considered compression stroke and power stroke that means from inlet valve closing time to exhaust valve about to open time. Applied the first law of thermodynamics and ideal gas equation to solve the equation and getting cylinder pressure and temperature generated inside the cylinder at every crank angle as well as combustion dynamics. The second part is an open phase, which is taken from exhaust valve opening to inlet valve closing time that means exhaust gases expel out from cylinder and entering fresh air inside the cylinder are taken into account. Thermodynamics first law equations of an open system are used and simplified to get the pressure and temperature variation inside the cylinder [11]. In this, valve movement with respect to cam geometry, valve area, air and gas properties inside the manifold also considered, which will help to get accurate values. The third part is for performance and combustion analysis from the data generated during the closed phase and open phase. The combustion characteristics such as pressure rise, heat release, rate of pressure rise, temperature variation inside the cylinder can be calculated. As well as performance characteristics such as thermal efficiency, volumetric efficiency, specific fuel consumption, mean effective pressure, a heat balance sheet can be calculated through this modelling [12]. Forms the main programme consisting of closed and open phase, with function assembled in MATLAB language. A crank angle is taken as zero when the piston is at TDC just before inlet valve closing. The programme calls the energy function, which gives the rate of change of work and heat transfer. The solution of the differential equation for temperature change is carried out numerically step by step using the Runge–Kutta method [12]. This evaluation is repeated for 720° CA, which will give the result for one complete combustion cycle. A sequence of the programme evolution is as per flow chart of close phase and open phase as shown in Figs. 1 and 2 respectively.

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Fig. 1 Flow chart of close phase

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Predict the Effect of Combustion Parameter on Performance …

Fig. 2 Flow chart of open phase

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2 Experimental Test Facility The test engine used is a constant speed, single cylinder, four-stroke, water cooled, direct injection diesel engine having capacity 3.5 kW running at 1500 rpm (Kirloskar). The pictorial view of the experimental setup for single cylinder diesel engine is as shown in Fig. 3. The engine is attached to an eddy current dynamometer with speed sensing unit incorporated. Piezo-electric transducer is flush mounted in the cylinder head and used to measure cylinder pressure. An optical encoder is employed to capture the rpm of the crankshaft. Volumetric fuel flow rate and intake air flow rate were also measured by using fuel flow metre and air transmitter, respectively. The data acquisition system is used for acquisition and analysis of pressure crank-angle data is completed by the software.

2.1 Engine Specification • • • • • • •

No. of Cylinders = 1 Diameter of Cylinder = 80 mm Stroke Length = 110 mm Connecting Rod Length = 234 mm Compression Ratio = 18:1 No. of Strokes = 4 Rated Power = 3.5 kW @ 1500 RPM.

3 Result and Discussion A trial conducted on a single cylinder diesel engine for a variable load. The averages values are considered (form the repeated trials) for further calculations in order to avoid errors. With obtaining results, performance characteristics such as brake thermal efficiency and brake specific fuel consumption with respect to load graphs were plotted. Results obtain through a mathematical model for different load were plotted on a similar plot in order to make the comparison. Figure 4 shows the variation of cylinder pressure with respect to CA for modeling as well as experimental values for 12 kg load. A modelling curve has getting shifted by some CA towards TDC; this is might be due to round off the delay period value during calculations. Also, modelling curve spread was slightly more than experimental, but both acquire the same area under the curve; hence, results are not affected. Figure 5 shows the variation of BSFC with respect to load for experimental as well as modeling values. While Fig. 6 shows the variation of BTE with respect to load for experimental values as well as modelling values. It is clear from Figs. 5 and 6 that modelling results are very well matches with experimental results for the different engine load. Whereas

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Fig. 3 Experimental setup with a mechanical fuel injection system

Fig. 4 Cylinder pressure versus crank-angle plot by experimental and modelling

modelling results are somewhat on the higher side, this might be due to assumptions made during it. Further, changes have been done in the mathematical model in order to predict the effect of IP and IT on the performance and combustion characteristics of a diesel

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Fig. 5 Comparison plot of load versus BSFC by experimental and modelling

Fig. 6 Comparison plot of load versus BTE by experimental and modeling

engine. Combustion parameters such as IP (400, 500, 600 in Bar) and the start of main IT (25°, 20°, 15° bTDC) are considered by keeping compression ratio at 18, while fuel consumption readings were taken for 12 kg load. Run the MATLAB programme for all these possible combinations, and based on the outcome values, the performance characteristics were plotted. The delay period is the parameter which is largely affected due to this, which will change further combustion characteristics and piston work of the engine.

3.1 Performance Characteristics Figure 7 shows the variation of BTE with respect to IT for different IP and Fig. 8 shows the variation of BSFC with respect to IT. 600 bar and 15° bTDC SOI gives

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Fig. 7 BTE versus IT for different IP

Fig. 8 BSFC versus IT for different IP

best thermal efficiency and lower BSFC. This is may be due to break down of fuel particle at a higher pressure and result in properly mixing with air, which result into nearly complete combustion of fuel.

3.2 Combustion Characteristics In-Cylinder Pressure: The in-cylinder pressures versus crank angle at various SOI timings are shown in Fig. 9 for different IP. From these graphs, it is generally observed that advancing the SOI leads to higher in-cylinder pressures at 12 kg load, particularly at an IP of 600 Bar. For SOI timing towards TDC, due to high cylinder pressure and temperature, physical delay decreases, which further reduces the span of uncontrolled combustion. Due to all this rise in-cylinder pressure mainly depends on fuel-injected

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Fig. 9 Cylinder pressure variation for different IP and IT

quantity. Hence at higher load with high injection pressure shows maximum cylinder pressure. But at the same time due to higher injection pressure span of fuel injection get decreases, which further affect the work done. Heat Release Rate: Fig. 10 shows the heat release rate with respect to crank angle for different IP and IT. A higher value of the heat release rate curve was observed for advance SOI, and it decreases with decrease in IP as well as for retarded SOI. For advance SOI, the value of delay period is more. Hence, fuels accumulated in delay period suddenly get burn in the next phase. While for retarded SOI, sufficient pressure and temperature present inside the cylinder, which decreases the delay period. Hence, fuel is getting burn as soon as injected inside cylinder due to which the heat release rate curve get lowered and spread over that combustion duration.

Fig. 10 Heat release rate for different IP and IT

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Fig. 11 Exhaust gas temperature variation for different IP and IT

Exhaust Gas Temperature: The exhaust gas temperature for different SOI timings for varying IP is as shown in Fig. 11. Exhaust gas temperature increases with increasing IP. High fuel injection pressure and high engine load are the responsible parameter for the rise in-cylinder pressure as well as temperature. This further increases the exhaust gas temperature. But the rise in cylinder temperature also creates the chances of dissociation as well as the formation of NOx in the exhaust, which is not taken into consideration during modelling.

4 Conclusion The thermodynamical cylinder model has been derived and implemented in MATLAB–Simulink. This model allows simulating the cylinder pressure and temperature development in the crank-angle domain. Model is effectively used in order to predict the effect of modified CRDI small single cylinder diesel engine performance and combustion characteristics. From the obtained result through modeling, it can be concluded that IP and IT are the important parameters in order to enhance the engine performance. Advanced SOI timings result into higher in-cylinder pressures; higher pressure rise rates and higher heat release rates this is due to comparative large delay period. Also, due to high IP break down of fuel particle is taken place, which result in properly mixing with air. Due to all this delay period get decreases, which results into decrease in the span of uncontrolled combustion phase and heat release inside the combustion chamber depend on controlled phase. Hence, with high fuel IP at appropriate IT definitely, improves combustion characteristics of the small single cylinder diesel engine.

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References 1. Rakopoulos, C.D., et al.: Development and validation of a comprehensive two-zone model for combustion and emissions formation in a DI diesel engine. Int. J. Energy Res. 27, 1221–1249 (2003) 2. Datta, A., et al.: Engine performance, combustion and emission characteristics of a compression ignition engine operating on different biodiesel-alcohol blends. Energy (2017) 3. Ambrós, W.M., et al.: Experimental analysis and modelling of internal combustion engine operating with wet ethanol. Fuel 158, 270–278 (2015) 4. Mauro, S., et al.: Internal combustion engine heat release calculation using a single zone and CFD 3D numerical models. Int. J. Energy Environ. Eng. 9, 215–226 (2018) 5. Kegl, B., Lešnik, L.: Modelling of macroscopic mineral diesel and biodiesel spray characteristics. Fuel 222, 810–820 (2018) 6. Yu, Y., et al.: Modelling the atomization of high-pressure fuel spray by using a new breakup model. Appl. Math. Model. 40, 268–283 (2016) 7. Wang, Z., et al.: Experimental study on microscopic and macroscopic characteristics of diesel spray with split injection. Fuel 174, 140–152 (2016) 8. Agarwal, A.K., et al.: Effect of fuel IP and IT on spray characteristics and particulate size– number distribution in a biodiesel fuelled common rail direct injection diesel engine. Appl. Energy 130, 212–221 (2014) 9. Sindhu, R., et al.: Effective reduction of NOx emissions from a diesel engine using split injections. Alex. Eng. J. (2017) 10. Torres-Jimeneza, E., et al.: One-dimensional modelling and simulation of injection processes of bioethanol-biodiesel and bioethanol-diesel fuel blends. Fuel 227, 334–344 (2018) 11. Heywood, J.B.: Internal Combustion Engine Fundamentals. McGraw-Hill Series in Mechanical Engineering (1988) 12. Dond, D.K., et al.: Mathematical modelling and MATLAB simulation of diesel engine. In: International Conference on Advances in Thermal Systems, Materials and Design Engineering (ATSMDE 2017), pp. 1–7. SSRN, VJTI (2017)

Experimental Investigation of a Biogas-Fueled Diesel Engine at Different Biogas Flow Rates Naseem Khayum, S. Anbarasu, and S. Murugan

1 Introduction Growing requirements for unsustainable nature of fossil fuels are driving the interest for utilization of renewable fuels in internal combustion (IC) engines. The use of renewable energy systems from biomass resources is increasing rapidly in the world [1, 2]. Further increment in the utilization of the renewable sources can be made possible only after mechanical improvements, a strategic planning, and usage of integration technologies. By utilizing the innovations took place in the era of energy technologies, the bioenergy and geosequestration can offer a low-carbon footprint, better efficiency, recycling, CO2 capture and storage in many food and fiber industries (paper and paper), etc. [3–5]. Production of bioenergy from agricultural waste, energy crops, residues, and animal wastes are considered as the possible path way for sustainable energy [6]. A suitable thermal or biochemical conversion technologies ought to be implemented to transform biomass resources for producing bioethanol, biogas, methane, methanol, dimethyl ether (DME), and hydrogen energy systems. Usage of low-grade feedstocks and satisfactory conversion efficiency makes anaerobic digestion technology most suitable for conversion of organic wastes to bioenergy [7–9]. Anaerobic digestion is a chemical process in which the feedstock under a microbial digestion releasing heat, methane (CH4 ), hydrogen sulfide (H2 S), and carbon dioxide (CO2 ) and under particular conditions of oxygen-free environment. This process takes place for few days in the digesters depending on the conditions on which digestion carried out. The another striving feature of this technology is that anaerobic digestion occurs in a controlled digester and exits a potential organic fertilizer, which is advantageous for growth of plants. Hence, the potential usage of it is to use in IC engine as fuel, since it contains a maximum portion of CH4 in it [10, 11]. N. Khayum (B) · S. Anbarasu · S. Murugan Department of Mechanical Engineering, National Institute of Technology, Rourkela, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_87

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The high CO2 content in the biogas affects the calorific value, flammability range, and stoichiometric air requirement, which may result in a poor combustion efficiency and higher unburned hydrocarbon emission when it is used as a fuel [12]. It is reported that up to 40% of CO2 concentration in biogas did not affect the performance of the engine [13]. Biogas has high auto-ignition temperature, and thus, it is desirable to use it in higher compression ratio engines (i.e., CI engines). However, in CI engines, biogas can be used only in dual fuel mode, where biogas is allowed along with the air and diesel or diesel-like fuel is injected as a pilot fuel. The induction of primary fuel replaces the diesel/diesel-like fuel quantity [9]. The use of biogas as a primary fuel will exhibit lower carbonaceous emissions. Consequently, this also allows to operate at lean mixtures, resulting in lower combustion temperature. Hence, a decrease in NOx can be possible [10, 11]. Therefore, biogas dual-fuel diesel engine is well suited for CI engines when considering both environmental and economical aspects. Utilization of biogas on DF mode in CI engines has been reported by [9–24]. It is reported that the increase in brake specific fuel consumption (BSFC) might be due to the presence of CO2 , which diminishes the combustion chamber temperature and affects the flame velocity of biogas–air mixtures [13]. Researchers tried to explore the possibility of using biogas in CI engines. It is pertinent from the literature that there are significant works reported by various researchers using biogas in CI engines. However, there are very less works reported on the effect of different flow rates of biogas. Also, production and utilization of biogas from spent tea waste with co-digestion of cow manure was not reported yet. Therefore, in this study, an attempt was made to assess the performance of a CI engine run on a dual fuel mode using diesel as injected fuel and biogas as primary fuel. Biogas produced from co-digestion of spent tea waste (STW) and cow manure (CM) was inducted into intake of engine manifold at various flow rates viz., (0.25, 0.5, 0.75, 1.0 kg/h). The performance assessment was carried out and compared with the standard diesel operation, and the results are discussed in this paper.

2 Materials and Methods 2.1 Test Fuels In this study, diesel was purchased from a local fuel station at Rourkela, while biogas was produced in a floating-type dome digester from the anaerobic co-digestion of STW (30%) and CM (70%) on a mass basis. STW from various hostels, refreshment stalls, and canteens brought together, and cow manure was collected from the back post of the same institute. The physical properties of feedstock used in this study are given in Table 1. Using a GC-MS, the composition of gas produced was analyzed and compared with the available literature, given in Table 2.

Experimental Investigation of a Biogas-Fueled … Table 1 Physical properties of feedstock

915

Specifications

Details

Feedstock used

STW (30%) + CM (70%) on mass basis

C/N ratio

24.96:1

Volatile solid (VS), wt%

39.54

Total solid (TS), wt%

42.67

pH

5.8–7.5

STW: water ratio

1:3

CM: water ratio

1:1

Table 2 Comparison of biogas constituents from STW + CM with available literature [8] Feedstock

Test method D 7833* Gas constituents CH4

CO2

H2 S

O2

N2

H2

99% energy extraction efficiency can be obtained using the optimized solar panel sizes and divergence angle. Further, it is noticed that owing to the higher packing fraction (ratio of actual solar panel area to the net projected ground footprint area), designed solar tree based on Archimedes spiral offers higher energy density. However, Fermat’s spiral provides better performance for solar tree design with all solar panels of equal size.

2 Design of Structure The objective of design of solar tree structure is to extract maximum energy from the limited available space. Various optimized spiral patterns are commonly found in nature. Fermat’s spiral based on the packing pattern of sunflower seed has been used in engineering designs. The analytical expression for this spiral was formulated by Vogel [16] and can be expressed in polar coordinates (r, θ ) as √ r = s n, θ = nϕ

(1)

where s is the constant scaling factor, n is the index number, and ϕ is the divergence angle. Divergence angle is the angle made between two consecutive points in the spiral. This gives an equation in the form of r 2 = s2 θ which represents a parabolic spiral. However, Fermat’s spiral belongs to a specific group of spiral called Archimedean spiral which is represented in the form of r = a + bθ 1/c

(2)

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where a and b are constants which determine the initial radius of the spiral and distance between successive spiral elements, respectively. The value of c = 1 gives the normal Archimedes spiral. Similarly, other spirals belonging to this group include Hyperbolic spiral (c = −1), Fermat’s spiral (c = 2), Lituus spiral (c = −2). These nature-inspired spirals are explored for the design of 3D solar photovoltaic structures in the form of solar tree. The coordinates obtained from these spirals represent the centers of the solar panels in the solar tree. Equations (3), (4), (5), and (6) represent the cylindrical coordinates of the centers of the solar panels using the Archimedes, Fermat’s, Hyperbolic, and Lituus spiral, respectively. r = sn, θ = nϕ

(3)

√ r = s n, θ = nϕ

(4)

r = s/n, θ = nϕ

(5)

r =s

√

n, θ = nϕ

(6)

There have been numerous research papers analyzing the optimal divergence angle called golden angle (137.5°). This angle is said to optimize the available space such that each point of the spiral is equidistant from each other. Therefore, in order to understand the feasibility of using these spirals for the design of solar tree, the top view of the spirals is analyzed with divergence angle equal to the golden angle and 400 number of solar panels as shown in Fig. 1. The value of scaling factor (s) is maintained constant at 0.5 for all the cases. In case of hyperbolic and Lituus spiral, the radius of consecutive solar panels keeps decreasing. It can be seen from the figure that 400 solar panels are positioned in a very small area. However, Archimedean and Fermat’s spiral offer the advantage of positioning solar panels uniformly in the space. Hence, these spirals are considered for further analysis.

3 Analysis of Spiral Parameters For power maximization, it is essential that the shading loss of solar panels is minimized. In the present study, the solar panels are considered to be arranged horizontally in a 2D plane. Based on actual solar insolation data of a location and movement of Sun in the sky around the year, the shading losses for a given arrangement of solar panels are calculated. The divergence angle and solar panel size providing the maximum energy extraction are evaluated.

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(a)

200 100

5

0

0

-100

-5

-200 -200

-100

0

100

200

(c)

0.2

-10 -10

0.1

0

0

-0.1

-0.1

-0.1

0

-5

0.1

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0.2

(d)

0.2

0.1

-0.2 -0.2

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10

-0.2 -0.2

-0.1

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Fig. 1 Top view of arrangement of solar panels based on a Archimedes spiral. b Fermat’s spiral. c Hyperbolic spiral. d Lituus spiral

3.1 Energy Extraction Ratio The present study is based on solar insolation data obtained from Meteonorm database. The database provides hourly values of global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) for a typical year. The Sun trajectory consisting of solar altitude angle (α) and solar azimuth angle (γ s ) are calculated to determine the direction of solar radiation based on the equation: − → sr = cos γs cos α + sin γs cos α + sin α

(7)

Thereafter, the shaded area of solar panels in the solar tree configuration is calculated by projecting the coordinates (image) of one solar panel on another in the direction of solar radiation. The intersection area (Ashad ) of this solar panel with the images gives the shaded area of solar panel for the given time instant. Similarly, the net shading loss (E a_loss ) is evaluated for each hour 7 a.m.–5 p.m. for the entire year using the equation: E a_loss =

365  17  N  N  day=1 h=7 i=1 j=i+1

Ashad (i, j) × Ib_h

(8)

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where I b_h is the direct radiation on the horizontal surface. Only beam radiation is considered for the evaluation, and it is assumed that diffuse radiation does not play a role in shading loss. This methodology of calculation of shading loss offers the advantage of providing higher weight to the periods of high insolation while optimizing for minimized shading loss. The total available energy (E a ) is calculated based on the total area of horizontally oriented solar panel and instantaneous beam radiation. Energy extraction ratio (E r ) is given by: Er =

E a − E a_loss Ea

(9)

The value of E r ranges from 0 to 1. Value of 1 implies 100% energy extraction efficiency, and 0 implies 100% shading loss. The parameter energy extraction ratio is further used for evaluation of designed solar tree performance.

3.2 Optimal Solar Panel Size The study is conducted for Chennai geographically located at 13.08° N and 80.27° E. With the scaling factor set to 0.5, energy extraction ratio is evaluated for different divergence angles ranging from 0° to 180° in steps of 0.5° for 50 number of solar panels with dimension 1 m × 1 m. Since Archimedes spiral expands at an arithmetic rate, it is observed that equalsized solar panels of unit dimension are sparsely distributed in the given area as shown in Fig. 2a. This has resulted in sub-optimal usage of the available space, very low packing fraction (ratio of solar panel area to projected ground footprint area) and nearly 100% energy extraction efficiency at all divergence angles (Fig. 2c). However, the same is not true in case of Fermat’s spiral as shown in Fig. 2b. Fermat’s spiral results in uniformly packed positions of solar panels such that the distance of nearest neighboring point of each solar panel is nearly equal. Therefore, equal sized solar panels are suitable in this case. However, for a given scaling factor, the size of solar panels and the divergence angle has to be optimized in order to maximize energy extraction. The maximum energy extraction efficiency that is obtained using solar panels of unit dimension and scaling factor of 0.5 is only 87% at 82° divergence angle as shown in Fig. 2c. This is clearly due to large overlapping areas of the solar panels resulting in huge shading losses. In order to find the optimal solar panel size for the given scaling factor, the dimension of the square solar panels of side 1 m is reduced in steps of 0.05 m in case of Fermat’s spiral. Square solar panels of multiple dimensions are used in the solar tree design based on Archimedes spiral. The dimensions of the solar panels are increased arithmetically along with the increasing radius of the spiral. The dimension of the solar panel at each point is determined by multiplying a constant ‘a’ to the radius (r). The value of ‘a’ is reduced in steps of 0.025 to determine the optimal constant factor. For each solar panel dimension, the values of energy extraction efficiency are

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(a)

25

(b)

4

15

2

5 0

-5 -2

-15 -25 -25

-15

-5

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-4

-4

-2

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1

Energy Extraction Ratio (Er )

Archimedes spiral 0.8

0.6

0.4 Fermat's spiral 0.2

0 0°

30°

60°

90°

120°

150°

180°

Divergence Angle

Fig. 2 Top view of 50 solar panels of dimension 1 m × 1 m arranged using a Archimedes spiral. b Fermat’s spiral with scaling factor 0.5 and divergence angle 137.50°. c Energy extraction ratio obtained by the spirals for different divergence angle

evaluated for divergence angles from 0° to 180°. The minimum solar panel dimension and the corresponding divergence angle for which the energy extraction efficiency is greater than 99% are considered as the optimized parameter for the given scaling factor. Figure 3 shows the top view of the solar tree design obtained using the optimized solar panel sizes and divergence angles. In case of Archimedes spiral, maximum energy extraction efficiency of 99.4% is obtained using the value of constant ‘a’ as 0.375 at 157.5° divergence angle for scaling factor of 0.5. Similarly, square solar panels of dimension 0.7 m result in maximum energy extraction efficiency of 99.8% at 99.5° divergence angle for scaling factor of 0.5. Total solar panel area in case of Fermat’s spiral is only 24.5 m2 while the same scaling factor value and energy extraction efficiency gives a total solar panel area of 1510 m2 in case of Archimedes spiral for 50 number of solar panels. However, the net projected ground footprint area is 2252 m2 and 45 m2 for Archimedes and Fermat’s spiral, respectively. Therefore, due to the higher packing fraction (Archimedes spiral:

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20 2 10 0

0

-10 -2 -20 -4

-30 -30

-20

-10

0

10

20

30

-4

-2

2

0

4

Fig. 3 Top view of solar tree designed with optimized solar panels size and divergence angles for a Archimedes spiral. b Fermat’s spiral

Fermat's Spiral Archimedes Spiral

2

2

Energy Density (kWh/m /day)

2.5

1.5

1

0.5

0

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Fig. 4 Comparison of annual energy density (considering only DNI) using Archimedes and Fermat’s spiral

0.59, Fermat’s spiral: 0.54), it is observed that the Archimedes spiral offers higher energy density in all months (Fig. 4). Also, it is worth noting that, there are multiple divergence angles which result in optimal energy extraction efficiencies.

4 Conclusion Nature optimizes the placement of leaves and stems in order to favor various physiological processes. Some of the primary reasons is being the optimal utilization of available space and the maximization of light capture efficiency. Since the operation of solar PV also depends on the light capture efficiency, nature-inspired spiral

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patterns are explored in this study for the design of high energy density solar tree. Feasibility of using Archimedes spiral and Fermat’s spiral has been demonstrated. The applicability of these spirals in design of solar tree of different kinds is established. Fermat’s spiral is shown to be the preferable pattern when solar panels of equal size are used. This is because of the uniform packing density offered by the spiral. Similarly, Archimedes spiral can be used in cases where solar tree consists of multiple solar panels of varying sizes. Further, the size of the solar panels and the divergence angles for maximized energy extraction efficiency is optimized. It has been shown that for a particular value of scaling factor (s = 0.5), square solar panels of size 0.7 m at divergence angle of 99.5° can offer 99.8% energy extraction efficiency for Fermat’s spiral. Similarly, solar panels of gradually increasing size by a factor of 0.375 at divergence angle of 157.5° can offer energy extraction efficiency of 99.4% for Archimedes spiral. It is shown that for the equivalent energy extraction efficiencies (>99%), the optimized solar tree design based on Archimedes spiral offers higher energy density due to the higher packing fraction obtained. However, if solar panels of equal dimensions are arranged according to the spiral patterns, Fermat’s spiral yields higher energy density due to the uniform distribution of points. Moreover, it is also noticed that there are multiple divergence angles that can offer equivalent energy extraction efficiencies. Hence, the selection of divergence angles can be based on the structural constraints and consideration of height factor. Acknowledgements The authors acknowledge the funding support of CSIR through MLP-0113 grant. Author Sumon Dey thanks CSIR-SRF fellowship for the financial support.

References 1. Benda, D., Chu, X., Sun, S., Quek, T. Q. S., Buckley, A.: PV cell orientation angle optimization for a solar energy harvesting base station (2017) 2. Hafez, A.Z., Soliman, A., El-Metwally, K.A., Ismail, I.M.: Tilt and azimuth angles in solar energy applications—A review. Renew. Sustain. Energy Rev. 77, 147–168 (2017). https://doi. org/10.1016/j.rser.2017.03.131 3. Litjens, G. B. M. A., Worrell, E., van Sark, W. G. J. H. M: Influence of demand patterns on the optimal orientation of photovoltaic systems. Sol. Energy 155, 1002–14 (2017) https://doi.org/ 10.1016/j.solener.2017.07.006 4. Soulayman, S., Hammoud, M.: Optimum tilt angle of solar collectors for building applications in mid-latitude zone. Energy Convers. Manag. 124, 20–28 (2016). https://doi.org/10.1016/j. enconman.2016.06.066 5. Jamil, B., Siddiqui, A.T., Akhtar, N.: Estimation of solar radiation and optimum tilt angles for south-facing surfaces in Humid subtropical climatic region of India. Eng. Sci. Technol. an Int. J. 19, 1826–1835 (2016). https://doi.org/10.1016/j.jestch.2016.10.004 6. Natarajan, S.K., Thampi, V., Shaw, R., Kumar, V.S., Nandu, R.S., Jayan, V., et al.: Experimental analysis of a two-axis tracking system for solar parabolic dish collector. Int. J. Energy Res. 43, 1012–1018 (2019). https://doi.org/10.1002/er.4300 7. Kumar, N. M., Sudhakar, K., Samykano, M., Jayaseelan, V.: BIPV Market growth: SWOT analysis and favorable factors. In: 2018 4th International Conference on Electrical Energy Systems (ICEES), pp. 412–5 (2018). https://doi.org/10.1109/icees.2018.8443227

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8. Myers, B., Bernardi, M., Grossman, J.C.: Three-dimensional photovoltaics. Appl. Phys. Lett. (2010). https://doi.org/10.1063/1.3308490 9. Bernardi, M., Ferralis, N., Wan, J.H., Villalon, R., Grossman, J.C.: Solar energy generation in three dimensions. Energy Environ. Sci. (2012). https://doi.org/10.1039/c2ee21170j 10. Boivin, A. B., Westgate, T. M., Holzman, J. F.: Design and performance analyses of solar arrays towards a metric of energy value. Sustain. Energy Fuels, pp. 2090–9 (2018). https://doi.org/ 10.1039/c8se00333e 11. Dey, S., Lakshmanan, M.K., Pesala, B.: Optimal solar tree design for increased flexibility in seasonal energy extraction. Renew. Energy 125, 1038–1048 (2018). https://doi.org/10.1016/j. renene.2018.02.017 12. Grigas, A.: The Fibonacci sequence: Its history, significance, and manifestations in nature. Lib Univ (2013) 13. Search, H., Journals, C., Contact, A., Iopscience, M., Address IP, Ramaekers, A. M., et al. d M us 2017 14. Martínez-graullera, O., Martín, C.J., Godoy, G., Ullate, L.G.: 2D array design based on Fermat spiral for ultrasound imaging. Ultrasonics 50, 280–289 (2010). https://doi.org/10.1016/j.ultras. 2009.09.010 15. Huang, X., Yan, H., Harder, R., Hwu, Y., Robinson, I. K., Chu, Y. S.: Optimization of overlap uniformness for ptychography 22, 12634–44 (2014). https://doi.org/10.1364/OE.22.012634 16. Vogel, H.: A better way to construct the Sunflower head. Math. Biosci. 44, 179–189 (1979). https://doi.org/10.1016/0025-5564(79)90080-4

Effective Use of Existing Efficient Variable Frequency Drives (VFD) Technology for HVAC Systems—Consultative Research Case Studies Rahul Raju Dusa, Atulkumar Auti, and Vijay Mohan Rachabhattuni

1 Introduction Bureau of Energy Efficiency (BEE), Ministry of Power, India, estimated total installed air conditioner capacity for space conditioning alone is about 80 million Ton of Refrigeration (TR). Cooling load is projected to rise to about 250 million TR and total connected load in India due to air conditioning is expected to be about 200 GW by 2030 [1]. Also the effect of HVAC systems on the environment is significant in mainly two ways. Refrigerants create a greenhouse effect that leads to global warming. Carbon dioxide generated from the energy used to power HVAC systems also leads to greenhouse effect. This indicates the inevitable need to incorporate energy efficiency measures, reducing energy requirements and size of HVAC systems which contributes our efforts in global warming mitigation [3]. Advanced technological developments for various systems in the utilities section of any industry or commercial entity has been rapid and promising over time. In the view of rising energy charges which have a significant impact on production costs, the utilities area, thus happens to be the primary target area for energy optimization activities. One of the major energy-consuming areas of the utilities section in many industries and commercial entities is the HVAC system. HVAC system may comprise of a. Different types of chillers, associated chilled water pumps under cooling load generation side. b. Cooling towers and condenser water pumps on the heat rejection side. c. Air Handling Units, Fan Coil Units, etc. on the distribution user end side. HVAC systems are equipped with various sensors and control systems from inception for monitoring and efficient system operations. Variable Frequency Drives (VFD) R. R. Dusa (B) · A. Auti · V. M. Rachabhattuni Industrial Energy Group, The Energy and Resources Institute (TERI), Bangalore, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_112

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is one such option that is extensively being used at entities operating under dynamic cooling load conditions to optimize their energy consumption. The VFDs are used for capacity regulation of chillers, associated pumps and cooling towers. The case studies illustrated in this paper are purely based on our site research outcomes of two different facilities during comprehensive energy audits. The HVAC system for space conditioning and process cooling in the industrial and commercial buildings of the considered case studies was consuming around 54.5% [1] to 60% [2] of total electricity. The main goal of this study is to demonstrate result-oriented case studies particularly for the chillers and cooling tower areas from an energy point of view. The study also aims to reduce energy consumption in industrial and commercial spaces whose utilities are particularly dominated by HVAC systems and to analyze the influence of related parameters.

2 Methodology The study involved carrying out various measurements and analysis to assess energy consumption and Specific Power Consumption (SPC) at two different facilities. The analyses included the following parts: a. Case–1: 250 TR Chiller operations at Facility A i. Scenario–1: Performance evaluation comparison of VFD versus non-VFD chillers for the plant operating conditions during the study period. ii. Scenario–II: Rectifying VFD logic restriction on capacity utilization due to high condenser water temperatures. b. Case–II: Cooling tower optimization with VFD to improve chiller performance at Facility B. A wide array of latest, sophisticated, portable, diagnostic and measuring instruments as listed in Table 1 were used to support our study and analyses. Table 1 Details of instruments used for the study S. No

Description

Functions

1

Krykard ALM 32

Three-phase power and harmonic analyzers

2

Krykard ALM 10

Single-phase power and harmonic analyzers

3

Fluke-41B

Power and harmonic analyzers

4

Anemometer

Airflow measurements

5

Multifunction kit

Relative humidity (RH%), temperature, pressure

6

Ultrasonic flowmeter

Water flow measurements

7

Infra-red pyrometer

Surface temperature measurements

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3 Case–1: 250 TR Chiller Operations with VFD at Facility a The plant has four TRANE make Heliorotory compressor-type water-cooled chillers with a rated capacity of 250 TR each. Chiller #1 (with VFD) and Chiller #2 (without VFD) were made to operate keeping constant plant load conditions at both chiller generation and user end area.

3.1 Performance Evaluation of Chillers at Facility A Scenario–1: Performance evaluation comparison of VFD versus non-VFD chillers In the 24 h duration trend, hourly energy consumption (see Fig. 1) for chiller #1 (with VFD) is lower than chiller #2 (without VFD). Comparative performance trend between chiller #1 and #2 for a 4 h stable load conditions were monitored (see Fig. 2) by maintaining similar operating parameters (pressures, temperatures, flows, etc.). Chiller #1 cooling load was varying around 160TR–175TR and Chiller #2 cooling load was varying around 180 TR–190 TR. At these conditions, chiller #1 VFD was able to adapt for the required cooling load by operating at 30 Hz. The chiller #2 without VFD does not have this option and thus was found to be performing with higher SPC as compared to chiller #1.

180.0 160.0 140.0 120.0 100.0 80.0 60.0 40.0 20.0 0.0

120

80 60 40 20 0

Time CH1 kWh

CH2 kWh

CH1 RLA

CH2 RLA

Fig. 1 Hourly energy consumption trend (kWh/hour) for chiller #1 and #2 for 24 h duration

% RLA

100

13:05 14:05 15:05 16:05 17:05 18:05 19:05 20:05 21:05 22:05 23:05 00:05 01:05 02:05 03:05 04:05 05:05 06:05 07:05 08:05 09:05 10:05 11:05 12:05

Energy consump on, kWh

Scenario–II: Rectifying VFD logic restriction on capacity utilization of Chillers due to high condenser water temperatures at Facility A Chiller #1 (VFD) was found to be performing efficiently compared to chiller #2 (non-VFD) during test operations (see Fig. 3) at different loading conditions (59, 70, 80 and 95% RLA).

R. R. Dusa et al. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

14:00 14:15 14:45 15:00 15:15 15:45 16:00 16:15 16:30 17:15 18:00

220.00 200.00 180.00 160.00 140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00

Cooling Load TR

Specific Power Consump on (kW/TR)

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Time Chiller #1 VFD Cooling load

Chiller #2 non VFD Cooling load

Chiller #1 VFD SPC

Chiller #2 non VFD SPC

0.9

200 180 160 140 120 100 80 60 40 20 0

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 RLA 60%

RLA 70%

RLA 80%

RLA 95%

RLA % CH 1 TR

CH 2 TR

Energy savings

CH 1 SPC

Specific Power Consumption, kW/TR

TR Generation/ Energy savings (kWh/hour)

Fig. 2 Specific power consumption (SPC) and cooling load details comparison of chiller #1 (VFD) and chiller #2 (non-VFD)

CH 2 SPC

Fig. 3 Cooling load generation and specific power consumption (SPC) comparison between chiller #1 (VFD) and chiller #2 (non-VFD) at different operating loads

The SPCs of both the chillers were found to be increasing with that of the increased operating load. This is mainly due to the condenser water temperature. The water flow is constant at different operating loads during the trial. The supplied condenser water temperature was at 34.5° . During low operating loads, the condenser water parameters (flow and temperature) were manageable for optimal heat rejection from the chiller. As the chiller cooling load demand increased, heat rejection by condenser water is restricted due to high inlet temperature of supplied condenser water which leads to an increase in SPC (Fig. 4). Chiller #1 retrofitted VFD is of CG make, Model: EMOTRON FDU 2.0 AC Drive. According to the TRANE RTHD Adaptive Frequency Drive, RTHD AFD

Effective Use of Existing Efficient Variable Frequency …

Unloading logic

Loading logic

50Hz

100% 40Hz

80% 60%

100%

50Hz

90%

90% 70%

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40Hz

80% 70%

30Hz

30Hz

30Hz

30Hz

30Hz

60%

50%

50%

40%

40%

30%

30%

20%

20%

10%

10%

0%

0% 12%

30%

60%

80%

100%

100%

Slide Valve position

80%

60%

30%

12%

Chiller load

Chiller load

Soft loading with VFD

Fig. 4 Loading and unloading logic for RTHD AFD controller [5]

controller logic is illustrated in Fig. 5. Blue colour bar in Fig. 5 indicates slide valve variation and Red colour indicates VFD operating frequency variation with respect to different chiller loads RTHD AFD controller controls the chiller with VFD by means of following important parameters: a. Slide Valve position controlling from 20 to 100%. b. Soft loading of compressor by controlling VFD from 30 to 50 Hz. c. Controlling the Lift which is defined as the difference between the condensing (discharge) pressure and the evaporating (suction) pressure. d. Chilled water and condenser water temperature. The chiller starts at 50 Hz and later it stabilizes at 30 Hz after reaching the maximum lift with respect to the chiller operating load conditions. Throughout the study period it was found that the chiller VFD always operated at 30 Hz to avoid tripping of compressor motor due to insufficient condenser water temperature (detailed in Sect. 3.2). During this trail operation at different RLAs such as 60, 70, 80 and 95% only power variation occurred due to slide valve adjustment for 30 Hz VFD speed (Fig. 4).

3.2 Performance Evaluation of Cooling Towers at Facility A Facility has four cooling towers each of 463.62 TR capacity. Single cooling tower is sufficient for one chiller but all four cooling towers were found to be operated for heat rejection loads of two operating chillers. Operating performance of all the

R. R. Dusa et al. 45 40 35 30 25 20 15 10 5 0

30 25 20 15 10 5 0 14:00 14:15 14:45 15:00 15:15 15:45 16:00 16:15 16:30 17:15 18:00

Effectiveness/ Cooling water T

Range/Approach

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Time Effectiveness

Range

Cooling water T in

Condenser water Tout

Appoach

Fig. 5 Performance trend of the cooling towers

cooling towers was poor as compared to its design values (Effectiveness 60.64%, Approach 4 °C, Range 7 °C) (See Fig. 5). Only about 103–105 m3 /h of condenser water flow was measured against the design 200 m3 /h. Even at low condenser water flow rates, the operating range of the cooling tower was measured to be low in the range of 3–4 °C as compared to its design 7 °C. It clearly shows the poor performance of cooling towers, affecting SPC of chillers.

3.3 Result and Recommendations for Case 1 at Facility a The energy savings by a VFD chiller over a non-VFD chiller at different loading conditions were varying between 22.3 kWh/hour and 31 kWh/hour. It was recommended to operate the chiller #2 (non-VFD) for baseload and chiller #1 (with VFD) to cater to the variable load during the peak time of the day. In order to consider the cyclical operation of chillers, the facility was recommended to install similar retrofit VFD for another chiller. Though the energy savings on installation of the VFD are evident, it is important that the facility has an idea of possible cooling load demands from the user side to plan and operate the VFD chiller. The condenser water temperatures are primarily restricting the chiller #1 VFD at 30 Hz irrespective of the cooling load. It was recommended to change the fills of cooling tower which will increase the water to air heat transfer area and change the nozzles for uniform water distribution which will improve the cooling tower operational performance.

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4 Case–2: 3125 TR Cooling Tower Optimization with VFD to Improve Chiller Performance at Facility B The performance of water-cooled chiller varies based on the entering condenser water temperature. As per our study of HVAC system at facility B, the installed chillers are designed for 32.2 °C (90 °F) entering condenser water temperature to give chiller Specific Power Consumption (SPC) of 0.589 kW/TR [3]. It is possible to reduce the cooling water temperature below 32.2 °C by utilizing all cooling towers which lead to a reduction in specific power consumption of chillers Facility has six water-cooled centrifugal type chillers of capacity 2500 TR each and six cooling towers of capacity 3125 TR each. The number of chillers in operation depends on the ambient atmospheric conditions and cooling load requirement. The condenser side heat from the operating chillers is rejected in the cooling towers. Monthwise average hourly basis cooling load and power consumption of facility B is provided in Fig. 6.

4.1 Performance Evaluation of Chillers

Cooling load, TR

Two chillers were being operated as per the cooling load requirement and setpoint to generate chilled water temperature at 6 °C. For the entering condenser water temperature at 32 °C, a summary of chillers performance is given in Table 2. 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

4000 3500 3000 2500 2000 1500 1000 500 0 Jan

Feb March April May June July

Aug Sept

Oct

Nov

Month Cooling load, TR

Chiller power, kW/hour

Fig. 6 Average hourly basis cooling load and power consumption

Dec

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Table 2 Performance evaluation details of chillers (two chiller operation) Particulars

Units

Chiller #1

Chiller #2

Compressor load

%

85%

92%

Power consumption

kW

1419.33

1536.1

IkW

1381

1495

Cooling water flow to the condensers

m3 /hr.

1800

1820

Temperature drop across the condenser

°C

Heat rejection on condenser side

TR

Condenser side 4.8 2877

4.9 2942

Evaporator side Temperature drop across the evaporator

°C

Cooling load

TR

Specific power consumption

kW/TR

7.06 2442.3

7.11 2497.8

0.565

0.598

4.2 Result and Recommendation for Case 2 at Facility B The fans’ speed of the cooling towers was operated with VFD in auto mode with approach temperature as feedback. It is possible to reduce the cooling water temperature below the design approach by increasing surface area of cooling tower. Utilizing standby cooling towers with operating all fans in auto mode could reduce water temperature below cooling tower design approach with respect to ambient conditions which will reduce the power consumption of chiller. Performance of a chiller is affected/varies based on the entering temperature of cooling water (see Fig. 6). Lower cooling water entering temperature, results in low refrigerant temperature and reduced work done by the compressor to maintain the same differential pressure leading to a reduction in motor loading and power consumption. Maintaining the temperature of condenser water below the design 32.2 °C will improve the specific power consumption of the chiller, provided the design flow rate is maintained. Trial was taken by operating all the cooling tower fans (6nos) in auto mode for better heat rejection from the cooling water. Both chiller #1 and chiller #2 parameters were monitored during the trial and performance of the chillers was analyzed before and after the trial (see Tables 3 and 4). Cooling tower inlet to condenser was 28.5 °C (Fig. 7). It was found that the specific energy consumption of the chiller and power consumption cooling tower of all fans operating at lower speed has significantly reduced. Thus, operating all cooling tower fans in auto mode for two chillers has resulted in an energy saving of around 120 kW, i.e., (4% of the total power consumption).

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Table 3 Trial for chiller #1 Description

Unit

Time Power consumption

Cooling load SPC

Day-1

Day-2

12:00

13:00

14:00

12:00

13:00

14:00

kW

1522

1419.3

1410.3

1512.3

1514.8

1499.6

IKW

1481

1381

1372

1471

1474

1459

IkW

4234

TR

2554.5

2694.9

2638.8

TR

7411

kW/TR

Difference in SPC

kW/TR

Difference in power

kW

4405 2442.3

2414.2

2610.7 7944.4

0.571

0.554

0.017 42

Table 4 Trial for chiller #2 Description

Unit

Time Power consumption

Day-1

Day-2

12:00

13:00

14:00

12:00

13:00

14:00

kW

1582.2

1536.1

1509.1

1553.7

1553.3

1569.7

IKW

1539

1495

1468

1512

1511

1527

2611.3

2554.5

4502 Cooling load

TR

2526.2

4550 2497.8

2384.2

7408.2 SPC

kW/TR

Difference in SPC

kW/TR

Difference in power

kW

2668.1 7834

0.608

0.581

0.027 66

Chiller Efficiency (kW/Ton)

0.66 0.64 0.62 0.6 0.58 0.56 0.54 0.52 0.5 55

62

69

76

83

Entering Condenser Water Temp (Deg F) Fig. 7 Effect of entering/leaving condenser water temperatures on chiller efficiency [6]

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5 Conclusion Aim of the study was to conclude the best operating philosophy of the chillers and cooling towers to be followed to minimize energy consumption by referring to two case studies at two different facilities. The results indicated reasonably lower hourly energy consumption by chiller with VFD compared to other non-VFD chillers. More importantly, the study also showcased the restriction of effective use of VFD for cooling generation resulting from high condenser water supply temperatures due to poor performance of cooling tower. Cooling tower is vital equipment of refrigeration system and the performance of water-cooled chiller varies based on the entering temperature of cooling water. It is important to note that appropriate savings obtained by the VFD retrofitted chiller significantly depends on the condenser water temperature and chiller loading conditions. Also, it is possible to reduce the cooling water temperature below the design approach by utilizing all cooling towers which lead to a reduction in specific power consumption of chillers. The study thus emphasizes detailed assessment regarding the importance of effective use of already existing efficient technologies, i.e., VFDs for the HVAC systems to reap maximum benefits.

References 1. Ministry of Power.: Notification D.O. No. 11/3/2018-EC, Energy conservation in building space cooling through optimum temperature setting guideline (2018) 2. T E R I. 2019, Project Report No. 2016IB23, The Energy and Resources Institute, Bangalore, p. 115 (2017) 3. T E R I. 2019, Project Report No. 2014IB10, The Energy and Resources Institute, Bangalore, p. 99 (2015) 4. Samah, K., Alghoul, A.: Comparative study of energy consumption for residential HVAC systems using energyplus. Am. J. Mech. Ind. Eng 2(2) (2017) 5. TRANE representative internal document 6. Kallerud, M., Nip, D., Zhang, M.: UC Davis thermal energy storage (TES) tank optimization investigation, Figure 1, 2012

Thermodynamic Analysis of a Combined Power and Cooling System Integrated with CO2 Capture Unit of a 500 MWe SupC Coal-Fired Power Plant Rajesh Kumar , Goutam Khankari, and Sujit Karmakar

Nomenclature E˙ H TES ψ η ε FP Evp FG i/o IC/AC CWP Sat TG mix.

Energy rate (kW) Specific enthalpy (kJ/kg) Thermal energy storage Exergy efficiency (%) Energy efficiency (kJ/kg) Specific exergy (kJ/kg) Feed pump Evaporator Flue gas Inlet/Outlet Intercooler/Aftercooler Condenser cooling water pump Saturation Turbo-generator Ammonia–water binary mixture

R. Kumar · S. Karmakar (B) Department of Mechanical Engineering, National Institute of Technology Durgapur, 713209 Durgapur, West Bengal, India e-mail: [email protected] R. Kumar e-mail: [email protected] G. Khankari Simulator Training Centre, MTPS, DVC, 722183 Bankura, West Bengal, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_113

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1 Introduction For a number of years in India, coal power plants have contributed about 60% of total electricity demand [1]. Coal is the primary workforce for electric power generation, and the burning of coal results in the generation of suspended particulate matter (SPM) and emission of different pollutants. World energy researchers are giving more attention for producing environmental friendly power in the most economical manner for safeguarding the environment. Energy-related CO2 emission will be increased by about more than 40% by 2030 [2]. In general, there exist three numbers of CO2 capture techniques like pre-combustion, post-combustion, and oxy-fuel combustion. Out of these, monoethanolamine (MEA)-based post-combustion CO2 capture technique is one of the best cost-effective technique due to its ‘end-of-pipe’ characteristic but rather energy-intensive [3]. Moreover, the conventional coal power plants are having problem to apply the technology for capturing CO2 as its low partial pressure and high flue gas velocity [4]. Zhou et al. showed an approach to improve the efficiency of a 1000 MWe coal-based steam power plant with a calcium looping (CaL) system by about 0.98% point by integrating cryogenic O2 storage for simultaneous flue gas decarbonization [5]. Singh et al. studied that the average cost of post-combustion CO2 capture in China was about 40–43 USD/t CO2 captured [6]. Flue gas condenser is one of the important components used in the post-combustion CO2 capture system where cooling water is used as heat-carrying media to reduce the flue gas temperature by about 100 °C. The large amount of compressor work is required to store the captured CO2 at a certain pressure (111 bar) and atmospheric temperature (30 °C). Staging of compressor with inter-cooler system is normally designed to reduce the specific work requirement for the compressor. The available waste heat sources from flue gas condenser and inter coolers (IC) can be used for combined power and cooling effect in a coal-based power plant with CO2 capture system. There are many thermodynamic power cycles in the literature that are studied by researchers by using different combinations of working fluids for waste heat recovery systems [7]. For low-temperature application, NH3 –H2 O mixture is one of the best-suited working fluids for getting maximum power and cooling effect as it has variable boiling temperature properties. The first use of NH3 –H2 O binary mixture for combined power and cooling cycle (CPC) was proposed by Goswami [8]. Xu et al. thermodynamically carried out a performance study of a CPC system and concluded that about 127 °C is the optimum temperature range for getting highest efficiency from the combined power and cooling cycle (CPC) system [9]. By considering the high cost of CO2 capture and high energy-intensive of the postcombustion CO2 capture unit (CCU), a solar-assisted combined power and cooling system (CPCS) is proposed in the present research work which is driven by the waste heat resources available in the CO2 capture system.

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2 System Description Figure 1 describes the flow path of a solar-assisted combined power and cooling system (CPCS) which is integrated with the MEA-based CO2 capture unit (CCU) of a 500 MWe supercritical (SupC) coal power plant to generate additional electric power and cooling effect from the various waste heat resources of the CCU like waste heat released from compressor’s coolers and flue gas condensation. Solar-assisted CPCS consists of various components like intercoolers (IC), aftercooler (AC), flue gas condenser (Evaporator), binary mixture turbine, condenser, expansion valve, chilling room, separator, compressor, feed pump and regenerative heater, indirect solar heater with thermal energy storage (TES) system. IC and AC of the compressors of CCU are a shell and tube heat exchangers where the ammonia–water binary mixture is heated and then sent to flue gas condenser which is act as an evaporator for changing the phase from liquid to vapour of binary mixture by utilizing the waste heat of flue gas condensation. After that, it is sent to regenerative heater where turbine exhaust is used as a heating source. From the regenerative heater, it is fed to the indirect solar heater to make it dry saturated vapour at turbine inlet. An expansion valve is placed after the condenser to get the cooling effect. After that, it is sent to the separator

Fig. 1 Schematic diagram of the solar-assisted CPCS integrated CO2 capture unit (CCU)

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where ammonia-rich binary mixture is separated and sends back to the absorber and ammonia lean binary mixture is sent to the IC and AC for exchanging heat with the stream of captured CO2 .

3 Analysis Methodology Thermodynamic analysis of the solar-assisted combined power and cooling system (CPCS) is carried out based on 4-E analysis which is integrated with the MEA-based CO2 capture unit (CCU) of a 500 MWe SupC coal thermal power plant. The proposed system is modeled in the computer modeling software ‘Cycle-Tempo’ [10]. In the thermodynamic modeling, the required parameter of the equipments and process path are defined as input at different operating conditions. Thermodynamic modeling of the system is done based on conservation of mass and energy principles.

3.1 The Performance Parameters of the Proposed System Net energy efficiency of CPCS   ˙ Cooling Effect − W ˙ FP ˙ Comp. ˙ CWP ˙ TG W CPCS + WCPCS CPCS − WCPCS − WCPCS net = ηCPCS in E˙ kcs     in IC/AC IC/AC FG ˙ Energy input E˙ CPCS = m +m ˙ FG hFG ˙ gas ofCCU hin − hout in − hout + Qsolar heater

(1)

(2)

Net exergy efficiency of CPCS

net ψCPCS =

  Cooling Effect FP Comp. CWP ˙ ˙ ˙ ˙ ˙ TG + W − W − W − W W CPCS CPCS CPCS CPCS CPCS

˙ in ˙ gas of EX CPCS = m

˙ in EX CPCS    FG  input IC/AC IC/AC FG ˙ Exergy ε +m ˙ FG εin +Q − ε − εout CCU in out solar heater

(3)

(4)

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Table 1 Coal report Coal composition (mass basis)

Unburnt carbon

C (%) H (%) O (%) N (%) S (%) Ash (%) LHV (MJ/kg) Bottom ash Fly ash (%) (%) 39.16

2.76

7.92

0.78

0.51

48.87

15.23

2.5

1.5

Exergy values for water/steam are calculated based on the referred paper [11]. Energy input Exergy input Q˙ solar heater and Q˙ solar heater are calculated as per the referred paper [12]. Combined power plant energy efficiency (Net)   TG ˙ TG ˙ FP ˙ Comp. ˙ CWP ˙ Net W Mainplant + WCPCS − WCPCS − WCPCS − WCPCS

comb. = ηplant

m ˙ coal LHV

(5)

Combined power plant exergy efficiency (Net)  comb. = ψplant

TG ˙ TG ˙ FP ˙ Comp. ˙ CWP ˙ Net W Mainplant + WCPCS − WCPCS − WCPCS − WCPCS



m ˙ coal εcoal

(6)

Coal specific exergy (εcoal ) is calculated on the basis of the given formula in referred paper [13]. The CO2 emission from the coal power plant is calculated based on used coal Table 1 by the following equation: consumption 

COemission = 2

m ˙ coal

  UCFA UCBA % C − % Ash 0.80 % 100 3.67 + 0.20 % 100 100

(7)

4 Results and Discussion 4.1 Energy and Exergy Analysis of Solar-Assisted Combined Power and Cooling System (CPCS) Thermodynamic analysis of the proposed system is made based on the energy and exergy method and Table 2 shows the operating data of the proposed solar-assisted CPCS at full load condition of a 500 MWe SupC coal power plant with MEA-based CO2 capture unit (CCU). The results in Tables 3 and 4 show that the net energy and exergy efficiencies of the plant are improved by about 4.23% and 3.90% points,

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Table 2 Operating data of solar-assisted CPCS with base NH3 mass fraction of 0.75 Pipe no. Pressure [bar] Temperature °C Mass flow [kg/s] Ammonia mass fraction [point] 1

1.06

2

1.06

147.89

598.672

–

97.52

598.672

–

4

3.392

146.09

124.656

–

5

3.392

30.6

124.656

– –

6

10.85

140.64

124.656

7

10.85

30.6

124.656

–

8

34.73

140.64

124.656

–

9

30.6

124.656

–

10

111.1

140.64

124.656

–

11

111.1

30.6

124.656

–

12

9.5

33.16

206.968

0.75

13

6

24

206.968

0.75

14

6

24

9.5

61.8

15

34.73

1 57.8 49.168 1 57.8

0.6723 0.9994

16

40

24.6

17

40

94.445

0.6723

18

40

90.64

19

40

92.52

1 57.8

0.6723

20

40

107.52

1 57.8

0.6723

21

40

1 37.39

1 57.8

0.6723

22

40

1 94.49

1 57.8

0.6723

1 57.8

0.6723

77.546

0.6723

80.25

0.6723

23

9.5

1 13.12

24

2.0 13

30.01

6473.474

–

40.01

6473.474

–

25

1.513

26

6

19.1 1

206.968

0.75

27

9.5

105.53

206.968

0.75

respectively, over the 500 MW SupC coal power plant with MEA-based CO2 capture system due to additional net electric power of about 30.68 MW from solar-assisted waste heat recovery system. In addition, about 52.57 MW of chilling effect is also observed from the proposed system. The net energy and exergy efficiencies of the solar-assisted CPCS are about 22.79% and 38.47%, respectively. The energy efficiency of the CPCS is lower than the conventional steam power cycle due to the high condenser back pressure of CPCS which is about 9.5 bar. It is concluded from Fig. 2 that about 73.96% of total energy is lost in the condenser which is maximum compared to other components and in Fig. 3, maximum exergy loss is found in the solar heater of about 69.16%.

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Table 3 Energy analysis of solar-assisted CPCS at full load Descriptions Gross TG output (kW) Energy available at IC and AC of CCS (kW) Solar energy input (kW) Energy available in flue gas stream (kW) Total energy available for CPCS (kW)

Data 36,340.300 50,899.538 282,598.760 31,855.337 365,353.635

Net energy supply to CPCS (kW)

327,397.128

Energy rejection in condenser (kW)

270,202.805

Auxiliary power consumption (kW)

5653.430

Net TG output (kW)

30,686.870

Cooling effect (kW)

52,573.230

Gross energy efficiency of CPCS (%)

24.336

Net energy efficiency of CPCS (%)

22.789

Net plant energy efficiency improvement (without cooling effect) (% point)

4.230

Table 4 Exergy analysis of solar-assisted CPCS at full load Descriptions Gross TG output (kW) Exergy available at IC & AC of CCS (kW) Solar exergy input (kW) Exergy available in Flu gas stream (kW) Total exergy available for CPCS (kW) Net exergy supply to CPCS (kW)

Data 36,340.300 7311.074 201,933.335 7190.051 216,434.460 78,888.954

Exergy rejection in condenser (kW)

1424.164

Auxiliary power consumption (kW)

5653.430

Net TG output (kW)

30,686.870

Cooling effect (kW)

52,573.230

Gross exergy efficiency of CPCS (%)

41.081

Net exergy efficiency of CPCS (%)

38.469

Net plant exergy efficiency improvement (without cooling effect) (% point)

3.900

4.2 System Performance at Different Part Load Thermodynamic performance analysis of the proposed system was carried out at different part load conditions and results are shown in Figs. 4 and 5, and Table 5. From Fig. 4, it is analysed that the energy and exergy efficiencies of the solar-assisted CPCS are increased with increase in load due to less irreversibility that occurs in the heat exchangers and less throttling effect of control valves at higher load. At

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Fig. 2 Energy balance of solar-assisted CPCS at full load

Fig. 3 Exergy balance of solar-assisted CPCS at full load Fig. 4 Effect of part load on the efficiency of CPCS

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Fig. 5 Part load vs. cooling effect and mass flow

higher load, irreversibility in the control valves is reduced which is observed in the exergetic performance analysis. At low load, additional power from the proposed system is less due to the reduction of available waste heat sources. As at part load, the availability of waste heat reduces at flue gas condenser and CO2 compression system due to reduction in amount of flue gas and captured CO2 . As a result, the amount of binary mixture mass flow rate reduces which causes less electrical output and also less cooling effect and the same is shown in Fig. 5. Net amount of additional power with main plant generation increases with increase in load which causes improvement in net plant energy efficiency of about 4.43% points and net plant exergy efficiency of about 3.90% points at full load and the same is shown in Table 5. Table 5 Effect of part load on main plant performance Main plant load

100%

80%

60%

Components

Data

Data

Data

Increase in net delivered power over main plant without solar assisted CPCS (kW)

31407.70

25255.10

19103.90

Net energy efficiency improvement over main plant without solar assisted CPCS (% point)

4.43

4.42

4.42

Net energy efficiency improvement over main plant without solar assisted CPCS (% point)

3.90

3.90

3.89

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4.3 Performance Study at Different Operating Pressure The performance of the system at various operating pressure is studied by fixing turbine exhaust pressure of about 9.5 bar and results are shown in Figs. 6, 7, and 8. It is observed in Fig. 6 that the TG output and cooling effect of solar-assisted CPCS increase with the increase in turbine inlet pressure. This improvement reduces the specific power requirement for the CO2 capture and the same is shown in Fig. 7. In Fig. 8, the requirement of the binary mixture mass flow rate is increased at higher operating pressure which causes the performance improvement of the system. At lower operating pressure, binary mixture temperature at heat exchangers inlet decreases and thereby, the heat taking capacity of the fluid increases at a fixed outlet temperature. The higher heat taking capacity reduces the demand of mass flow requirement for the system. The reduction of mass flow rate through turbine and chilling system decreases their desired performance. Figure 9 shows the variation in energy and exergy efficiencies of the solar-assisted CPCS without considering the cooling effect of the system at different turbine inlet pressure. It is found that both efficiencies are increased with increase in operating pressure due to changes in condenser loss. It is analyzed that at low operating pressure, Fig. 6 Turbine inlet pressure versus system performance

Fig. 7 Turbine inlet pressure versus specific power consumption for CO2 captured

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Fig. 8 Turbine inlet pressure versus mass flow rate

Fig. 9 Turbine inlet pressure versus performance of Solar-assisted CPCS without cooling effect

turbine exhaust temperature at fixed condenser pressure is more than the higher operating pressure and thereby, it enhances the condenser losses. Moreover, performance improvement slope reduces after the pressure of about 40 bar.

4.4 Environmental Impact of Solar-Assisted CPCS About 60% of total power generation is shared by the coal power plants in India and it emits different environmental pollutants like carbon-dioxide, sulphur-dioxide, suspended particulate matters (SPM), and NOx . Among these, CO2 is the main factor for creating global warming effect in the world. Considering the environment protection, only CO2 is considered for the present study. Combined power and cooling effect generation from the CO2 capture unit (CCU) of a 500 MWe SupC coal power plant can play a crucial role for safeguarding the environment. The following are the two major approaches which are considered for environmental analysis. Coal saving or CO2 reduction opportunity The combined electric power generation of about 500 MW can save the coal consumption of about 22 t/h by this novel approach at full load condition of a 500 MWe SupC coal power plant with CO2 capture system or the proposed system helps indirectly to reduce the CO2 emission

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Fig. 10 Part load versus coal saving and CO2 reduction

resulting in coal saving from other coal power plants without CCU by reducing its scheduled generation due to addition of excess electric power from the CPCS of the power plants with MEA-based CCU. From the present study, it is studied that about 29.29 t/h of CO2 emission can be avoided at full load. The variation of its values is found at different part loads and the same is shown in Fig. 10. Promoting the use of CCU High investment cost and high auxiliary power consumption hinder the use of MEA-based CO2 capture unit in the conventional coal power plants. The proposed research can motivate the use of said technology as the power drawn from the main plant generation for the CCU decreases due to the cogeneration of electric power from the waste heat resources. As a result, net electric power delivers to the national grid increases and auxiliary power requirements for running the CCU also decrease. For which, organization can be economically benefited. In the present study, about 30.68 MWe can be added with the main plant generation and auxiliary power requirement can be reduced by about 58.42% for capturing CO2 in a 500 MWe SupC coal power plant (auxiliary power requirements for the CCU without integration of solar-assisted CPCS is about 0.117 kWh/kg of CO2 captured).

4.5 Economic Analysis The thermo-economic viability of the solar-assisted CPCS integrated 500 MWe coal power plant with MEA-based CCU has been carried out based on LCoE analysis [13]. The cost of power generating unit with MEA-based CCU is assumed as Rs. 96, 250 per kW [14] and the capital cost of solar-assisted CPCS is taken as Rs. 3,48,402 per kW which is calculated based on weighted power law equation [15]. The discount rate of about 12% and coal cost of Rs. 0.75/kg are considered in the present work. The result shows that the LCoE and simple payback period (SPP) of the 500 MWe

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SupC steam power plant with CCU and solar-assisted CPCS are about Rs. 3.505/-per kWh and 5.424 years, respectively.

5 Conclusions Net plant energy and exergy efficiencies can be improved by about 4.23 and 3.90% points, respectively, over the 500 MW SupC coal power plant with MEA-based CO2 capture system due to additional net electric power of about 30.68 MW using solarassisted CPCS at full load. The additional cooling effect of about 52.57 MW can also be obtained along with additional electric power at full load. The net energy and exergy efficiencies of the solar-assisted CPCS are about 22.79 and 38.47%, respectively. The proposed solar-assisted CPCS can reduce coal consumption by about 22 t/h at full load of main coal power plant with CO2 capture system and about 29.29 t/h of CO2 emission for the power plant without CO2 capture system. It will also reduce the auxiliary power requirement by about 58.42% for capturing CO2 in a 500 MWe SupC coal power plant. LCoE and SPP of the proposed plant integrated with solar-assisted CPCS are about Rs. 3.50/-per kWh and 5.42 years, respectively.

References 1. Energy statistic report-2017 (2018) Ministry of statistics and programme implementation government of India. Available online: w.ww.mospi.gov.in 2. Minea, V.: Power generation with ORC machines using low-grade waste heat or renewable energy. J Appl Therm Eng 69, 143–154 (2014) 3. Liao, P., Wu, X., Wang, Y.M., Shen, J., Sun, B., Pan, L.: Flexible operation of coal-fired power plant integrated with post-combustion CO2 capture. J Energy Procedia 158, 4810–4815 (2019) 4. Merkel, T.C., Lin, H., Wei, X., Baker, R.: Power plant post-combustion carbon dioxide capture: an opportunity for membranes. J Membr Sci 359(1–2), 126–139 (2010) 5. Zhou, L., Duan, L., Anthony, E.J.: A calcium looping process for simultaneous CO2 capture and peak shaving in a coal-fired power plant. Appl. Energy 235, 480–486 (2019) 6. Singh S, Lu H, Cui Q, Li C, Zhao X, Xu W, Ku AY (2018) China baseline coal-fired power plant with post-combustion CO2 capture: 2. Techno-economics. Int J Greenh Gas Control 78: 429–436 7. Maurya, S., Patel, D.: Combined refrigeration cycle for thermal power plant using low grade waste steam. Int J Eng Res Appl 4, 60–63 (2014) 8. Goswami, D.Y.: Solar thermal power: status of technologies and opportunities for research. Heat Mass Transf 95, 57–60 (1995) 9. Xu, F., Goswami, D.Y., Bhagwat, S.S.: A combined power/cooling cycle. Energy 25(3), 233– 246 (2000) 10. Cycle-Tempo Release 5.0. (2008) Delft University of Technology. Available online: http:// www.tudelft.nl 11. Kotas, T.J.: The exergy method of thermal plant analysis. Butterworth, London (1985) 12. Khankari G, Karmakar S (2018) Power generation from fluegas waste heat in a 500 MW subcritical coal-fired thermal power plant using solar assisted KCS11, J. Appl Therm Eng, Elsevier, 138: 235–245

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13. Suresh, M.V.J.J., Reddy, K.S., Kolar, A.K.: 4-E (energy, exergy, environment, and economic) analysis of solar thermal aided coal-fired power plants. Energy Sustain Dev 14(4), 267–279 (2010) 14. Karmakar S (2016) Ph.D. thesis on 4-E (Energy, Exergy, Environment, and Economic) analysis on advanced coal technologies for power generation with CO2 capture, Dept. of Mechanical Engineering, IIT Chennai, India 15. Akbari, M., Mahmoudi, S., Yari, M., Rosen, M.: Energy and exergy analyses of a new combined cycle for producing electricity and desalinated water using geothermal energy. Sustainability 6(4), 1796–1820 (2014)

DFT Studies on Electronic and Optical Properties of Inorganic CsPbI3 Perovskite Absorber for Solar Cell Application Abhijeet Kale, Rajneesh Chaurasiya, and Ambesh Dixit

1 Introduction Solar energy holds the promise to tackle the ever-increasing energy demands across the globe if highly efficient as well as stable solar cell can be realized at a relatively reduced cost. Perovskite materials based solar cells are currently the frontrunner in photovoltaic research. In a small period of less than a decade, owing to their excellent electronic and optical properties, for example, long carrier diffusion length [1] and ambipolar carrier transport [2] characteristics, inorganic–organic perovskite solar cells (PSCs) already crossed 20% laboratory efficiency [3–5]. Moreover, the lower production cost associated with such solar cells makes PSCs a suitable alternative to the established silicon-based solar cell technology. However, PSCs are commercially least favored till today as these materials severely suffer from the degradation under ambient conditions, showing instability of organic–inorganic perovskites in air leading to fast degradation in their photovoltaic response. These devices are also prone to degradation against external perturbation such as temperature [6], electric field [7], and light soaking [7]. The obvious solutions to these problems can be the transition from organic/hybrid PSCs to all-inorganic PSCs which can offer relatively better stability. Cesium metal halide (CsMX3 ) perovskites may show an advantage over their organic–inorganic counterparts in terms of thermal stability as volatile organic compounds are replaced by stable cesium ions [8]. CsMX3 exhibits the bandgap of 1.72 eV with lead (Pb) as a metal and iodine (I) as halide, similar to other perovskite systems, and thus, may be suitable for photovoltaic applications [9]. There are already few reports on solar cell devices in an inverted configuration, which also assists in confronting the stability issues of a solar cell. Refs. [10–12] Incorporating CsPbI3 as an absorber layer in solar cells with an inverted configuration can yield better A. Kale · R. Chaurasiya · A. Dixit (B) Department of Physics, Indian Institute of Technology, Jodhpur 342037, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_114

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results. Moreover, being a relatively wide bandgap semiconductor, CsPbI3 may also be integrated with tandem configuration with the existing solar cells for enhanced efficiency and photovoltaic response [13].

2 Computational Details The linear augmented plane wave (LAPW) full potential method is carried out for the present computational studies using WIEN2K [14]. We used Perdew–Burke–Ernzerhof (PBE) with Generalized Gradient Approximation (GGA) exchange-correlation potential (ECP) [15]. Moreover, modified Becke–Johnson (mBJ) [16] exchangecorrelation potential is also used in computing the electronic and optical properties of CsPbI3 . The basis set 6s1 for cesium (Cs), 4f14 , 5d10 , 6 s2 , 6p2 for lead (Pb) and 4d10 , 5 s2 , 5p5 for iodine (I) are used in all these calculations. Around ions and interstitial regions (IR), the cubic unit cell is divided into muffin tin radius (Rmt ). The values of muffin tin radii are selected to avoid the overlapping of atomic spheres. Further, the plane wave cut-off parameters, i.e., Rmt × K max = 7, (here, Rmt is the smallest atomic sphere radius in the unit cell and K max is the plane wave cut-off), and Gmax = 12 Bohr−1 are used to optimize and then to analyze the electronic and optical properties of CsPbI3 system. To separate core states from valence states, lmax, i.e., the maximum value of l considered is 12, while the energy cut-off considered is −6 Ry in all these calculations. The atomic forces are restricted to 1mRy/au for each atom while relaxing their positions. The self-consistent calculations are carried out with 0.0001 Ry tolerance for the total energy convergence. Further, 125 K-points are considered in irreducible Brillouin zone for structural optimization, whereas 1000 Kpoints calculating properties such as electronic band structure, density of states, and optical properties.

3 Results and Discussion 3.1 Structural Properties ¯ space group [17]. The crystal structure of CsPbI3 system is a simple cubic pm3m Initially, cell volume is varied to find out the respective total energy, Fig. 1 (right panel). Further, Murnaghan’s equation of state, eq (1), is used to fit the computed energy versus volume data [18]. ⎡   ⎤ V0 B0 B0 V0 B0 V E(V ) = E 0 +  ⎣ V + 1⎦ −  B0 B0 − 1 B0 − 1

(1)

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1201

-100152.572

CsPbI3

Energy (eV)

-100152.574 -100152.576 -100152.578 -100152.580 -100152.582 -100152.584 -100152.586 -100152.588 1600

1650

1700

1750

1800

1850

1900

Volume (a.u.3)

Fig. 1 Optimized crystal structure and selected k-path R-g-X-M-g in Brillouin zone for cubic system(left panel) and variation in total energy with cubic cell volume for CsPbI3 perovskite systems (right panel)

where E 0 is the lowest energy for the equilibrium lowest volume V 0 at absolute zero temperature, B0 is the bulk modulus and B’0 is the 1st order derivative of bulk modulus with respect to pressure at the equilibrium volume. The minimum energy corresponds to the lattice parameter of 6.3772 Å. The optimized CsPbI3 crystal structure is shown in the left panel of Fig. 1 together with the respective Brillouin zone with high symmetry points. The crystal structure confirms the coordination of six iodine atoms with the lead atom, forming the octahedral crystal symmetry. The optimized PbI6 bond length and bond angles are 3.1886 Å and 90° , respectively.

3.2 Electronic Properties We computed electronic band structure and partial density of states using GGA-PBE and mBJ to understand the electronic properties of CsPbI3 system. The computed electronic band structures are plotted in Fig. 2a and b along with R-g-X-M-g high symmetry points of the Brillouin zone. The valance band (VB) maxima and conduction band (CB) minima are located at the same K-point, i.e., R in the Brillouin zone, suggesting the direct bandgap with bandgap values are ~1.42 eV and 1.72 eV for GGA-PBE and mBJ exchange-correlation potentials, respectively. The computed mBJ bandgap is close to the experimentally reported 1.74 eV. [9, 13, 19] The corresponding partial density of states (PDOS) are shown in Fig. 3, substantiating the forbidden region in band structure, Fig. 2. Further, PDOS spectra show that I-p,Pb-p and Pb-s orbitals contribute in majorly to VB, while, Pb-p, I-p and I-d orbitals are forming CB. The hybridization of Pb and I orbitals are noticed in both valence and

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conduction bands. Pb and I atoms also form PbI6 octahedral symmetry in CsPbI3 system. We also plotted the valence electron charge density, Fig. 4, along (100) and (110) planes for cubic CsPbI3 crystal. The charge density along (100) plane confirms the charge sharing between Pb and I atom, indicating the formation of

Fig. 4 Valence electron charge density contour plots for CsPbI3 along (100) (left panel) and (110) plane (right panel)

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covalent bonding as observed in the PDOS spectra, where the electronic states of Pb and I atoms are hybridized strongly. Further, charge density along (100) plane does not exhibit any significant charge sharing between Cs and I atoms, suggesting the formation of ionic bonding between these atoms.

3.3 Optical Properties The computed electronic properties reveal that CsPbI3 is a direct bandgap system. Further, computed real and imaginary parts of dielectric constants are plotted in Fig. 5. The imaginary part of dielectric function, Fig. 5 right panel, is very close to zero for initial energy values, while non-vanishing part of dielectric function is nearly close to the bandgap energy for CsPbI3 . The Kramers–Kroning relations are used to compute the real part of dielectric function using the imaginary dielectric constant and plotted in Fig. 5 (left panel). The static dielectric constant (i.e., dielectric constant near zero energy) is 5.50 and 4.46 for GGA-PBE and mBJ exchange-correlation potentials. The smaller value of static dielectric constant using mBJ as compared to GGA-PBE can be explained using the Penn model [20]. Further, at higher energies the real part GGA - PBE

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of dielectric function becomes negative near or above 8 eV energies, suggesting that the system may become metallic at higher energies [21]. The optical constants, i.e., refractive index (RI) and extinction coefficient are computed using real and imaginary parts of the dielectric function. The nature of the refractive index is the same under GGA and mBJ exchange potentials, which increases initially up to about 2 eV and decreases with any further increase in energy together with small peaks, which may correspond to the respective high energy band transitions, as noticed in the band structure. The computed extinction coefficient is similar to that of imaginary dielectric constant behavior and substantiates the onset of large extinction coefficient at the bandgap values, Fig. 6, bottom panel. We also computed reflectivity and shown in Fig. 7 for both exchange potentials. The zero crossovers, observed in the real part of the dielectric function, Fig. 5, suggest the transition from semiconductor to metallic states. This may be ascribed to screened plasma and causes higher reflectivity in that particular region, as noticed in computed reflectivity, Fig. 7. The change in the real part of dielectric function from positive to negative values indicates transition from semiconductor to the metallic state at the cost of incident energy. As it becomes metallic, the enhancement in the reflectivity is observed at around 8.3 eV in GGA-PBE case and 9.6 eV in mBJ case, where corresponding positive to negative crossvover is noticed in the real part of the dielectric spectra. GGA - PBE

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The computed extinction coefficient is further used to compute the absorption against the energy and shown in Fig. 8 for GGA-PBE as well as mBJ exchangecorrelation potentials. The absorption is of the order of 104 cm−1 or more, which suggests the large absorption for incident radiation at or above bandgap value.

4 Conclusion We investigated optoelectronic properties for CsPbI3 perovskite material utilizing density functional theory under GGA-PBE and mBJ exchange-correlation potentials. The studies showed that cubic CsPbI3 is a direct bandgap of ~1.72 eV with mBJ, which is consistent with reported experimental bandgap value. The absorption coefficient α > 104 cm−1 above bandgap energy, suggesting that this material may be a good fit for absorber in photovoltaic application.

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Acknowledgements Author Abhijeet Kale acknowledges the financial aid from the Department of Science and Technology (DST), Gov. of India via the INSPIRE Ph.D. fellowship. Ambesh Dixit acknowledges the Department of Science and Technology, Government of India through project#DST/INT/Mexico/P-02/2016 for this work.

References 1. Zhang, F., et al.: Extra long electron–hole diffusion lengths in CH 3 NH 3 PbI 3-x Cl x perovskite single crystals. J. Mater. Chem. C 5.33, 8431–8435 (2017) 2. Giorgi, G., et al.: Small photocarrier effective masses featuring ambipolar transport in methylammonium lead iodide perovskite: a density functional analysis. J. Phys. Chem. Lett. 4.24, 4213–4216 (2013) 3. Singh, T., Miyasaka, T.: Stabilizing the efficiency beyond 20% with a mixed cation perovskite solar cell fabricated in ambient air under controlled humidity. Adv. Energy Mater. 8.3, 1700677 (2018) 4. Yang, D., et al.: Achieving 20% efficiency for low-temperature-processed Inverted perovskite solar cells. Adv. Funct. Mater. 29.12, 1807556 (2019) 5. Wu, C.-G., et al.: High efficiency stable inverted perovskite solar cells without current hysteresis. Energy and Environ. Sci. 8.9, 2725–2733 (2015) 6. Ava, T., et al.: A review: thermal stability of methylammonium lead halide based perovskite solar cells. Appl. Sci. 9.1, 188 (2019) 7. Leijtens, T., et al.: Mapping electric field-induced switchable poling and structural degradation in hybrid lead halide perovskite thin films. Adv. Energ. Mater. 5.20, 1500962 (2015) 8. Tai, Q., Kai-Chi, T.A.N. G., Yan F.: Recent Progress of Inorganic Perovskite Solar Cells. Energy and Environmental Science (2019) 9. Wu, T., et al.: Efficient and stable CsPbI3 solar cells via regulating lattice distortion with surface organic terminal groups. Adv. Mater. 1900605 (2019) 10. Nouri, E., Mohammadi, M.R., Lianos, P.: Improving the stability of inverted perovskite solar cells under ambient conditions with graphene-based inorganic charge transporting layers. Carbon 126, 208–214 (2018) 11. Kim, J.M., et al.: Use of AuCl3-doped graphene as a protecting layer for enhancing the stabilities of inverted perovskite solar cells. Appl. Surf. Sci. 455, 1131–1136 (2018) 12. Zhu, Y., et al.: Enhanced efficiency and stability of inverted perovskite solar cells by carbon dots cathode interlayer via solution process. Org. Elect. 74, 228–236 (2019) 13. Ahmad, W., et al.: Inorganic CsPbI3 perovskite-based solar cells: a choice for a tandem device. Solar RRL 1.7, 1700048 (2017) 14. Blaha, P., Madsen, G.: WIEN2k. (2016) 15. Perdew, J.P., Burke, K., Ernzerhof, M.: Phys. Rev. Lett. 78, 1396 (1997) 16. Tran, F., Blaha, P.: Phys. Rev. Lett. 102, 5 (2009) 17. Materials Project website.: https://materialsproject.org/materials/mp-1069538/ 18. Murnaghan, F.D.: Proc. Natl. Acad. Sci. Unit. States Am. 30, 244 (1944) 19. Tao, S.X., Cao, X., Bobbert, P.A.: Accurate and efficient band gap predictions of metal halide perovskites using the DFT-1/2 method: GW accuracy with DFT expense. Sci. Rep. 7(1), 14386 (2017) 20. Penn, D.R.: Wave-number-dependent dielectric function of semiconductors. Phys. Rev. 128(5), 2093 (1962) 21. Chaurasiya, Rajneesh, Auluck, Sushil, Dixit, Ambesh: Cation modified A2 (Ba, Sr and Ca) ZnWO6 cubic double perovskites: A theoretical study. Comput. Condens. Matter 14, 27–35 (2018)

Biowaste Derived Highly Porous Carbon for Energy Storage Dinesh J. Ahirrao, Shreerang D. Datar, and Neetu Jha

1 Introduction Uncontrolled waste generation and its disposal are one of the serious issues around the globe. In India, the generation of waste in the urban areas is about 200 to 600 grams per person per day. This rate of waste production is expected to increase continuously with the increasing population. This is one of the serious concerns as such a high amount of waste is difficult to dispose completely. Most of these wastes contain either high amount of carbon or any other materials depending upon the source of the waste from which it has been generated. The waste generated from agriculture contains different types of elements. One of such agricultural waste is the sweet lime peels. Its annual production is nearly 15% of the total fruit market. If the waste generated from sweet lime is disposed in soil, it may give rise to hazardous phenolic compounds once decomposed [1]. This can eventually decrease the fertility of the soil. So it would be better if we could recycle this waste in some good manner which otherwise is just laying in dumping grounds. Sweet lime peels contain carbon-rich substances like cellulose, hemicellulose, and pectin [1]. This waste has little or no economic importance, so it can be used as an inexpensive source for the production of highly porous carbon material for many applications that use carbon-based materials. The energy storage devices such as supercapacitors (SC) have gained tremendous awareness because of their high power density and range of applications in electronic devices. The supercapacitors can be categorized into two types depending upon their mechanisms of charge storage. Among which, the first type is pseudocapacitors, and the other type is electrical double-layer capacitors (EDLC). In the case of pseudocapacitors, electrodes are fabricated with metal oxides or conducting polymers and store the charges by means of Faradic reactions, whereas in EDLC carbon-based electrode material is used to store the charges in the absence of redox reactions D. J. Ahirrao · S. D. Datar · N. Jha (B) Department of Physics, Institute of Chemical Technology, Matunga, Mumbai, MH, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_115

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(non-Faradically). The electrodes used in EDLC are made up of carbon-based nanomaterials such as graphene, carbon nanotubes, and activated carbon. Graphene and recent nanomaterials have been considered as a high-quality carbon which requires complex synthesis procedures and advanced instruments such as CVD, sputtering, laser ablation, and arc discharge [2]. Hence, easy and economic approach needs to be investigated to fulfill the requirement of high surface area carbon. In this present work, we demonstrate the simple production of biowaste sweet lime derived porous activated carbon (AC). Activated carbon has been used to fabricate the electrodes and its electrochemical properties have been investigated for energy storage applications in supercapacitors. KOH solution was used as an activator to initiate the activation process followed by low-temperature carbonization at 500 °C. Superior charge storage capacity has been achieved with aqueous electrolyte. The supercapacitor also showed excellent stability even after 5000 charge–discharge cycles without any fading in its initial capacitance.

2 Experimental 2.1 Materials Sweet lime (Citrus limetta) peels were obtained from the Institute of Chemical Technology canteen. Potassium hydroxide (KOH) was purchased from S.D. Fine Chemicals, India.

2.2 Method Obtained peels were washed in running tap water for several times and rinsed with the deionized water to eliminate the dirt particles. The peels were then cut into small pieces and dried in air for 24 h at 100 °C. After complete drying, the peels were grinded to fine powder (10 g) and soaked into 100 mL of 2 M KOH solution for 30 min. The mixture was then centrifuged to separate the soaked peels, and excess KOH solution was discarded. Obtained cake was again kept for drying at 100 °C for 24 h. The dried KOH-treated peels were carbonized in the furnace at 500 °C for 1 h. The obtained activated carbon was washed with copious amount of deionized water and dried in air at 60 °C for 2-3 h. It was then used for the structural, morphological, and electrochemical characterizations (see Fig. 1).

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Fig. 1 Pictorial representation for the synthesis procedure of activated carbon

2.3 Instrumentation Structural and morphological studies for as synthesized activated carbon were carried out by the following techniques. Powder XRD (Bruker D8 Advance) with copper K-alpha (Cu Kα) radiation (λ = 1.54 A˚) was used for the confirmation of the diffraction patterns. The Raman spectra were taken in the range from 1000 to 2500 cm−1 with a Horiba HR 800 model equipped with laser excitation wavelength of 514.5 nm and spot size 1 mm with incident power ~10 mW. Porous surface of activated carbon was observed in scanning electron microscope (SEM, JEOL JSM 6380) at multiple magnifications. All the electrochemical tests like cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS) were carried out at room temperature with metrohm (PGSTAT204) electrochemical workstation.

2.4 Electrode Preparation and Electrochemical Measurements To investigate the electrochemical properties for activated carbon for supercapacitor, initially electrodes need to be made, by forming suspension of electro-active material. In our case, the suspension was made by mixing biowaste sweet lime peels derived AC with ethanol in bath sonicator. The AC suspension was then loaded on the graphitic carbon paper by drop cast method and allowed it to dry for 2-3 h in air at 60 °C. The total loading on the graphitic carbon paper was calculated by weighing carbon paper after loading. Swagelok cell was used for the fabrication of symmetric two-electrode supercapacitor cell. Electrochemical characterizations, such as galvanostatic chargedischarge (GCD), were performed at various current densities (from 1 A/g to 3.5 A/g), and cyclic voltammetry (CV) was carried out at various scan rates (from 50 mV/s to 350 mV/s). Electrochemical impedance spectroscopy was performed in the frequency

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range from 100 kHz to 0.01 Hz. All the electrochemical data was obtained in the voltage window of 0.0 to 0.9 V in 1 M H2 SO4 solution as an electrolyte.

3 Results and Discussion An XRD spectrum of the activated carbon shows two broad peaks at 25.71˚ which corresponds to the reflection from plane (002) and at 43.46˚ which corresponds to the reflection from plane (100) (see Fig. 2a). This confirms the graphitic nature of activated carbon [3]. The precise nature of the AC was studied using Raman spectroscopy. The Raman spectra show the peak at 1335 cm−1 attributes to D band and the peak at 1580 cm−1 corresponds to G band of carbon (see Fig. 2b). The D band represents the defects, whereas G band implies to the vibration of sp2 bonded atoms in a 2D hexagonal lattice. The intensity ratio I D /I G was found to be 1.18, which is significantly higher. This shows the existence of disorderness in the carbon matrix [4]. The morphology and the surface structure of the activated carbon were investigated by scanning electron microscope (see Fig. 3a, b). The SEM image shows highly porous and spongy surface of activated carbon that can clearly be seen at several magnifications. Both the SEM images show the material with the desired morphological properties having the pores with micrometer size. From porosity, we can conclude that the chemical activation with KOH worked well even at the low temperature and can be applicable for large-scale production of carbon materials with high surface area. To investigate the electrochemical performance of a sample, the electrochemical investigations play a significant role, and it also gives the exact value for charge holding capacity for any electro-active material. Equation (1) was used to evaluate the energy storage capacity from discharge curves [5].

Fig. 2 a X-ray diffraction pattern (XRD) and, b Raman spectra for sweet lime peels derived AC

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Fig. 3 SEM images for sweet lime peels derived activated carbon

CSC = (4 × I × t)/(m × v).

(1)

where CSC represents the specific capacitance (F/g), I denotes the discharge current (A), m is the total mass loaded on both the electrodes (mg), and t and V are the discharge time (s) and working potential window (V ), respectively. Electrochemical response of AC electrodes was recorded using CV curves at range of scan rates (see Fig. 4a). The charge storage capability of a supercapacitor highly depends on the applied scan rates [6]. The ideal electric double-layer (EDLC) behavior of activated carbon electrodes can be interpreted from the perfectly rectangular form of all the CV curves. Even at the higher scan rates, it has maintained its rectangular shape, which is due to the superior electrochemical response, the ability of activated carbon to work even at higher inputs, and it indicates good rate capability with increasing scan rates [7]. This clearly confirms that the mechanism of charge storage of KOH activated carbon is mainly caused by the formation of electric double layer. This means no oxidation and reduction reaction are occurring at the interface of the electrode and the electrolyte. Further to obtain the values of specific capacitance for activated carbon material, electrodes were analyzed for GCD at various current densities (see Fig. 4b). The curves from GCD represent the typical symmetric triangular form which is the representation of non-Faradic mechanism of charge storage. The calculated specific capacitance values were 243.63, 238.62, 232.13, 221.64, 219.20, and 216.66 F/g at current densities of 1, 1.5, 2, 2.5, 3, and 3.5 A/g, respectively. These high specific capacitance values were obtained due to the high surface area, optimal porosity, and excellent electrical conductivity of the as synthesized material [8]. The graph of specific capacitance versus current density (see Fig. 4c) shows that the specific capacitance values decrease as the current density increases. This is the conventional behavior of supercapacitors which occurs owing to the less diffusion of electrolyte ions into the porous structure of the activated carbon at elevated current densities. Apart from the charge storage capacity, another important parameter for the supercapacitor is its cyclic stability, which gives the idea about supercapacitors

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ability to sustain the maximum number of continuous charging–discharging cycles and its consequence on the electrode material. Results from cyclic stability interpret the durability as well as the efficiency of the materials life as the electrode. Here, we have carried out cyclic stability test up to 5000 charge–discharge cycles at current density of 5 A/g (see Fig. 5a), and no decrement in the specific capacitance was noticed as compared to its initial capacitance instead capacitance value has increased by 10.12%. This may be because of the electro-activation of the AC electrode. The electro-activation is a very interesting concept in which charge storage capacity of an electrode enhances as the number of charging–discharging increases. In electro-activation, as charge–discharge cycles increase, the layers of the materials reallocate to house the maximum electrolyte ions. This allows ions to acquire the maximum surface area of the electrodes. Therefore, specific capacitance increases with increasing charge–discharge cycles [9]. Electrochemical impedance spectroscopy is a better tool to investigate the internal resistance of the material as well as the resistance between the electrodes and the electrolytes (see Fig. 5b). Electrochemical measurements through EIS were carried out in the frequency range from 100 kHz to 0.01 Hz. Nyquist plot consists of a semicircle followed by Warburg impedance, which is the high-frequency region

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Fig. 5 a Cyclic stability test (up to 5000 cycles at 5 A/g), b Nyquist plot with inset representing the magnified higher-frequency region

and the low-frequency region, respectively. Resistance value obtained for activated carbon is very low (0.48 ) due to the higher conductivity and porous structure which allows faster diffusion of the electrolyte ions on the porous surface. This leads to better electrochemical performance and improved charge storing capacity.

4 Conclusion Here, we report a simple and low-temperature scheme to produce extremely porous activated carbon from the biowaste sweet lime peels. Low temperature was used for the carbonization purpose, and KOH solution was used as a chemical activator for activation. Obtained porous carbon was further used to make the electrodes for supercapacitor to investigate its charge storage capacity. Activated carbon showed ideal electric double-layer supercapacitive behavior with excellent charge storage capabilities. The specific capacitance of about 243 F/g was obtained at 1 A/g in 1 M H2 SO4 electrolyte. The high value of specific capacitance may be due to the increased surface area and high conductivity of the as prepared material. The activated carbon electrode also showed outstanding cyclic stability even after 5000 repetitive charging–discharging cycles with 10.12% increment in its initial capacitance value. This is attributed to the electro-activation of the electrode surface. This reveals the applicability of the as prepared activated carbon derived from biowaste sweet lime peels as a reliable electrode material for energy storage application. Acknowledgements The authors would like to acknowledge the economic support from the department of science and technology (No. SR/NM/NS-1110/2012), Government of India.

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References 1. Kim, N.R., Yun, Y.S., Song, M.Y., Hong, S.J., Kang, M., Leal, C., Park, Y.W., Jin, H.J.: Citruspeel-derived, nanoporous carbon nanosheets containing redox-active heteroatoms for sodiumion storage. ACS Appl. Mater. Interfaces. 8(5), 3175–3181 (2016) 2. Biswal, M., Banerjee, A., Deoab, M., Ogale, S.: From dead leaves to high energy density supercapacitors. Energy Environ. Sci. 6, 1249–1259 (2013) 3. Xie, Z., Guan, W., Ji, F., Song, Z., Zhao, Y.: Production of biologically activated carbon from orange peel and landfill leachate subsequent treatment technology. J. Chem. 491912 (2014) 4. Shimodaira, N., Masui, A.: Raman spectroscopic investigations of activated carbon materials. Appl. Phys. 92, 902 (2002) 5. Stollera, M.D., Ruoff, R.S.: Best practice methods for determining an electrodematerial’s performance for ultracapacitors. Energy Environ. Sci. 3, 1294–1301 (2010) 6. Kang, Y., Xiaorui, S., Huachao, Y., Jianhua, Y., Kefa, C.: Electrochemical performance of activated graphene powder supercapacitors using a room temperature ionic liquid electrolyte. Acta Phys. Chim. Sin. 35(7), 755–765 (2019) 7. Mo, R.J., Zhao, Y., Wu, M., Xiao, H.M., Kuga, S., Huang, Y., Li, J.P., Fu, S.Y.: Activated carbon from nitrogen rich watermelon rind for high-performance supercapacitor. RSC Adv. 6, 59333–59342 (2016) 8. Ahirrao, D.J., Jha, N.: Polyaniline-manganese dioxide nanorods nanocomposite as an electrode material for supercapacitors. DAE Solid State Physics Symposium 2016. AIP Conference Proceedings, vo.l 1832, pp. 1–3. (2017) 9. Cheng, Q., Tang, J., Ma, J., Zhang, H., Shinya, N., Qin, L.C.: Graphene and nanostructured MnO2 composite electrodes for supercapacitors. Carbon 49(9), 2917–2925 (2011)

Bio-Ethanol Production from Carbohydrate-Rich Microalgal Biomass: Scenedesmus Obliquus Maskura Hasin, Minakshi Gohain, and Dhanapati Deka

1 Introduction In the eighteenth century, the Industrial Revolution occurred, and as an important energy resource, fossil fuels came into the picture. But problems like global warming, greenhouse effect occurred due to the increasing use of fossil fuels. In search of solution to the different problems, the renewable source of energy which has been developed as an alternative fuel source gathered a wide range of attention over some decades [1, 2]. Presently in the world, bio-energy is the fourth largest primary energy source. In biomass, its carbon content is derived from the atmosphere when it grows and when the biomass is burnt, the carbon is liberated back into the atmosphere. So, the biofuel which is obtained from the biomass is regarded as the carbon-neutral fuel [2–4]. The bio-ethanol has the capacity of minimizing the particulate emission in the CI engines as it is biodegradable, non-toxic, and also a renewable source of energy. In, the CI engines, ethanol can be used in both the hydrous and anhydrous form. As an alternate fuel, bio-ethanol–diesel blends and bio-ethanol have been used in the countries like France, Germany, Brazil in the late nineteenth century and early twentieth century [2]. Starch, sugar, and cellulose are the polysaccharides found in algae and the fermentation of these algal polysaccharides leads to the production of ethanol. Under some particular conditions, the carbohydrate content mainly the starch in the microalgae can reach up to as high as 70% [5–7]. The cell wall of microalgae is classified into inner and outer layer. Polysaccharides as agar, pectin, and alginate are present in outer cell wall and mostly cellulose, hemicellulose is presented in inner cell wall layer of the microalgae. These polysaccharides and starch present in the cell wall are fermented for bio-ethanol production [7, 8]. There are several advantages of algae as M. Hasin (B) · M. Gohain · D. Deka Department of Energy, Biomass Conversion Laboratory, Tezpur University, Tezpur, Assam 784028, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_116

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compared to the other biomass for using as a renewable source of energy. The growth of the algal biomass is very fast. Efficient removal of carbon dioxide and synthesize of polysaccharides and oil for the production of biofuel is one of the main advantages of algal biomass. So, these microalgae are a good source of clean biofuels [2, 9, 10]. Locally available microalgae species have been less exploited for bio-ethanol production. Hence, herein we report the use of Scenedesmus obliquus for the bioethanol production.

2 Experiment The overall experimental approach has been depicted in Fig. 1. Raw material (Scenedesmus obliquus)

Analysis of the Scenedesmus obliquus

Pre-treatment of the raw sample

Liquid Hot water Treatment

Acid pre-treatment

Fermentation

Distillation

Bio-ethanol

Fig. 1 Flowchart for bio-ethanol production

Alkaline pre-treatment

Bio-Ethanol Production from Carbohydrate-Rich …

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2.1 Materials The freshwater microalgae species Scenedesmus obliquus (SO) used for the experiment was obtained from Gauhati University, Department of Biotechnology. All the chemicals used in this study were obtained from Sigma Aldrich and were used as procured. Saccharomyces cerevisiae was obtained from local bakery shop.

2.2 Pre-treatment of SO Pre-treatment step was performed in order to remove the lignin and hemicelluloses and reduce the crystalline structure of cellulose. Different pre-treatment methods were followed:

2.2.1

Liquid Hot Water Pre-treatment (LHWT)

In a typical process, 10% (w/v) SO was taken in a 100 ml autoclave, placed in a muffle furnace, and pre-treated at 190 °C for 30 min. After pre-treatment for quenching the reaction, immediately after taking out the reactor from the furnace, it was cooled in an ice-bath. Then filtration is done to separate the insoluble solid from the liquid and then washing is done with distilled water after which it was dried at 40 °C for further processing [11].

2.2.2

Acid Pre-treatment

In this pre-treatment method, the sample was soaked at solid loading 10% (w/v) in a sulfuric acid solution of 2% v/v overnight at room temperature. For the solution, here de-ionized water was used. The mixture was then pre-treated at 121° for 60 min in a muffle furnace. After the pre-treatment, filtration was done and then washed to neutralize the pH after which it was dried at 60 °C [12].

2.2.3

Alkaline Pre-treatment

Herein, the sample was subjected to dilute alkaline pre-treatment using KOH (2%). The alkaline sample mixture is autoclaved at 121 °C for 30 min. The ratio of 1:10 of the solid phase to the liquid phase was maintained. After cooling, the treated biomass sample is then filtered, washed to neutralize, and then dried at 60 °C to attain a weight which is constant [13].

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2.3 Total Sugar Estimation The estimation of total sugar concentration in the supernatant after pre-treatment was done by DNSA (3, 5-Dinitrosalicylic acid) method in UV visible spectrophotometer at 540 nm wavelength to determine the effect of pre-treatment. Herein, addition of 1 mL of sample was done to 2 mL of DNSA reagent in a test tube, and after that for 10 min, it was kept at boiling hot water bath. After that, the test tube was cooled and then addition of 7 mL was done. Finally, absorbance at 540 nm was taken. The existence of so-called reducing sugar, i.e., the free carbonyl group(C = O), in the sample was detected by this method. Here the aldehyde functional group that is present is oxidized, like in fructose the glucose and the ketone functional group. At the same time, under the alkaline conditions, 3,5-dinitrosalicyclic acid(DNSA) is reduced to 3-amino,5-nitrosalicyclic acid.

2.4 Fermentation For the fermentation process, the yeast Saccharomyces cerevisiae was used. The fermentation reaction is depicted below C6 H12 O6 → 2C2 H5 OH + 2CO2

(1)

The yeast inoculum was added to the hydrolysate, and at the temperature of 32 °C and pH 5 for 7 days along with 150 rpm agitation, the fermentation was carried out.

2.5 Distillation For the separation and purification of ethanol from the mixture after the fermentation process, distillation process is necessary. Firstly, the fermentation broth is filtered in vacuum pump filter for the removal of solid and the filtrate is then distilled in the distillation unit. The water–ethanol mixture is separated based on the boiling point difference. Since the boiling point of water is 100 °C which is higher than that of the boiling point of ethanol which is 78 °C, the ethanol gets vaporized before water. The product was dried using molecular sieve having a pore size diameter of 3A exclusively suitable for drying the water content of the bio-ethanol mixture.

Bio-Ethanol Production from Carbohydrate-Rich … Table 1 Proximate analysis of SO

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Composition

Percentage by weight

Method

Moisture

10.91

ASTM D 3173

Ash

17.99

ASTM D 3174

Volatile matter

69.06

ASTM D 3175

Fixed carbon

2.04

2.6 Characterization Proximate analysis was done to determine the moisture content, ash content, volatile matter, and the fixed carbon of the sample and obtained according to the ASTM standard methods. FTIR spectra of the raw and the pre-treated samples were recorded on Shimadzu Fourier transform infrared spectrophotometer (IR Affinity 1, Shimadzu, Japan) in the range of 500-5000 cm. For spectral analysis, the introduction of the sample was done by preparing a thin film of KBr pellet. Recording of the spectra was done in transmittance mode. Bio-ethanol was characterized and confirmed by gas chromatography. Gas chromatography of bio-ethanol was carried out in Agilent GC/MS 7890A, 240 Ion trap with auto headspace sampler equipped with flame ionization detector (FID), and the sample was run for 20 min. The column used is the Agilent HP-1MS UI 30 m × 250 μm, 0.50 μm injection mode headspace analysis FID detector at 250 °C. Split/Splitless Inlet, Split: 500:1, 0.5 mL injection volume, purge flow 50 mL/min at 0.75 min. The injection temperature was 230 °C with column temperature of 80 °C. As the carrier gas, helium was used and a constant flow mode of 0.5 mL/min was maintained.

3 Results 3.1 Characterization of the Raw Sample 3.1.1

Proximate Analysis

The physical properties of SO are tabulated (Table 1).

3.1.2

Gravimetric Analysis

For the gravimetric analysis, the NREL protocol for determination of structural carbohydrates and lignin in biomass was followed which can be found in Fig. 2. The cellulose content was the highest, which proves SO to be suitable for bio-ethanol production.

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Fig. 2 Structural carbohydrates and lignin in SO

Extractives , 3.2 %

Cellulose, 53.08 % Acid soluble lignin,0.30 5%

3.1.3

Hemicellulose, 38.4 %

Acid insoluble lignin , 5.03 %

FTIR Analysis of Pre-treated Sample

The FTIR spectra of the pre-treated SO samples are shown in Fig. 3 indicating the shape and transmittance change of the spectral bands. Absorption band at 875 cm−1 in Fig. 3a indicates C-O-C stretching present in β-(1-4)-glycosidic linkage in cellulose and hemicellulose. A decrease in xylose content in SO was observed due to solubilzation of hemicellulose and the confirmation of which was done by the appearance of peak at 1035 cm−1 (Fig. 4b–d). This peak can be attributed to vibrational modes of CH2 OH groups which are normally coupled with C-O bending of the COH functional groups of carbohydrates. The peak at 1500 cm−1 corresponds to aromatic ring vibration of lignin (Fig. 3a), but a decrease in the peak intensity was observed for the pre-treated samples denoting the lignin depolymerization. The absorption peak at 2925 cm−1 can be attributed to C-H stretching of lignin which is increased after pre-treatment steps. Broadening of bands at around 3450 cm−1 (Fig. 3b–d) can be attributed to the stretching of H-bonded hydroxyl functional groups of lignin [14]. Fig. 3 FTIR spectra of a Raw sample, b Acid treated, c KOH treated, and d LHWT

Bio-Ethanol Production from Carbohydrate-Rich …

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Fig. 4 GC graph of a standard ethanol, b KOH treated, c LHWT, and d Acid treated ethanol

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Fig. 4 (continued)

Table 2 TRS content before and after fermentation and the percentage sugar conversion Samples

TRS content before fermentation(mg/ml)

TRS content after fermentation (mg/ml)

Percentage sugar conversion (%)

Liquid hot water pre-treated

0.98

0.53

45.92

KOH pre-treated

1.31

0.62

52.67

Acid pre-treated

1.09

0.39

64.22

3.2 Total Reducing Sugar(TRS) Content and the Percentage Conversion of Sugar to Ethanol In the experiment, the ethanol production has been measured in terms of the percentage conversion of the TRS content of the substrate before and after fermentation. The results have been tabulated below in Table 2. From the data, it has been seen that for the acid pre-treated sample, the sugar conversion is highest, and thus the ethanol production.

3.3 Bio-Ethanol Analysis 3.3.1

Gas Chromatography

The GC of the bio-ethanol (retention time) was compared with the GC of a standard ethanol sample. The graph signifies that the bio-ethanol spectrum lies between the retention times from 2.855 min to 2.930 min; 2.909 min to 3.3 min; and 2.993 min to

Bio-Ethanol Production from Carbohydrate-Rich …

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3.298 min for KOH, LHWT, and acid-treated biomass, respectively, which matches with the retention time of the standard ethanol sample, i.e., 2.8 min to 3.3 min. The GC plot confirms the bio-ethanol formation as the injected standard of the ethanol and the bio-ethanol sample elutes at the same time as the peak shown in test chromatogram (Fig. 4).

4 Conclusions Microalgae are one of the prominent feedstock to produce green fuels. Different pre-treatment techniques have been employed to enhance the conversion of sugar to bio-ethanol. The various analyses done for the raw sample as well as for the pretreated sample proves abundance of cellulose which can be successfully converted to bio-ethanol. The comparisons of the effect of different pre-treatment techniques of the raw sample are shown in the FTIR plot. Bio-ethanol production was confirmed by GC analysis. Hence, it can be concluded that this algal sample (SO) is suitable for the bio-ethanol production which is a clean and green fuel that can contribute to combating the environmental pollution.

References 1. Chen, W., Lin, B., Huang, M., Chang, J.: Thermochemical conversion of microalgal biomass into bio-fuels: a review. Bioresour. Technol. 184, 314–327 (2015) 2. Haq, F., Ali, H., Shuaib, M., Badshah, M., Hassan, S.W., Munis, M.F.H., Chaudhary, J.: Recent progress in bioethanol production from lignoceellulosic materials: a review. Int. J. Green Energy 13(14), 1413–1441 (2016) 3. Farrell, A.E., Gopal, A.: Bioenergy research needs for heat, electricity, and liquid fuels. MRS Bull. 33(4), 373–380 (2008) 4. Ullah, K., Ahmad, M., Sharma, V.K., Lu, P., Harvey, A., Zafar, M., Sultana, S.: Assessing the potential of algal biomass opportunities for bioenergy industry: a review. Fuel 143, 414–423 (2015) 5. Hall, J., Payne, G.: Factors controlling the growth of field population of hydrodictyon reticulatum in New Zealand. J. Appl. Phycol. 9, 229–236 (1997) 6. Zhao, G., Chen, X., Wang, L., Zhou, S., Feng, H., Chen, W.N., Lau, R.: Ultrasound assisted extraction of carbohydrates from microalgae as feedstock for yeast fermentation. Bioresour. Technol. 128, 337–344 (2013) 7. Daroch, M., Geng, S., Wang, G.: Recent advances in liquid biofuel production from algal feedstocks. Appl. Energy 102, 1371–1381 (2013) 8. Nigam, P.S., Singh, A.: Production of liquid biofuels from renewable resources. Prog. Energy Combust. Sci. 37(1), 52–68 (2011) 9. Vassilev, S.V., Vassileva, C.G.: Composition, properties and challenges of algae biomass for biofuel application: an overview. Fuel 181, 1–33 (2016) 10. Yen, H.W., Hu, I.C., Chen, C.Y., Nagarajan, D., Chang, J.S.: Design of photobioreactors for algal cultivation. In: Biofuels from algae. pp. 225–256 (2019) 11. Michelin, M., Teixeira, J.A.: Liquid hot water pretreatment of multi feedstocks and enzymatic hydrolysis of solids obtained thereof. Bioresour. Technol. 216, 862–869 (2016)

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12. Sritrakul, N., Nitisinprasert, S., Keawsompong, S.: Evaluation of dilute acid pretreatment for bioethanol fermentation from sugarcane bagasse pith. Agric. Nat. Resour. 51(6), 512–519 (2017) 13. Saratale, G.D., Oh, M.-K.: Improving alkaline pretreatment method for preparation of whole rice waste biomass feedstock and bioethanol production. RSC Adv. 5(118), 97171–97179 (2015) 14. Das, K., Ghosh, P., Dey, A., Ganguly, A., Das, S., Chatterjee, P.K.: Studies on the optimization of phenolics during production of xylitol from water hyacinth. Eur. J. Biotechnol. Biosci. 4(3), 23–33 (2015)

Safety Analysis of Loss of NPP Off-Site Power with Failure of Reactor SCRAM (ATWS) for VVER-1000 Manish Mehta, Sanuj Chaudhary, Anirban Biswangri, P. Krishna Kumar, Y. K. Pandey, and Gautam Biswas

1 Introduction Kudankulam Nuclear Power Plant (KKNPP 1&2) are 1000 MWe VVER-1000 Reactors. In order to investigate the variations in different parameters of the power plant under the condition of ATWS, Loss of Nuclear Power Plant off-site power with failure of reactor SCRAM was analyzed. It is a Design Extension Condition (DEC-A) that can demonstrate the capability of safety systems to cope with such accidents. The RELAP-5 code has been developed for transient simulations of Light Water Reactor (LWR) coolant systems during postulated accidents. It has been extensively used in the analysis of thermal hydraulic behavior of Light Water Reactors (LWR) during loss-of-coolant accidents and operational transients such as anticipated transient without scram (ATWS), loss of off-site power, loss of feedwater, and loss of flow [1]. Qi and Yang [2] have analyzed, loss of main feedwater with ATWS using RELAP5 code to evaluate the intrinsic safety mainly impacted by fuel and moderator. This paper focuses on one of the operating NPPs in China, the TianWan NPP Unit 1&2, which is VVER-1000 type. Calculations and analyses were made to give thermal hydraulics support to Level-1 PSA of this VVER type PWR on the anticipated transient without scram (ATWS) accidents. Kyrki [3] has carried out ATWS analyses with HEXTRAN code for Loviisa and Hungarian Paks NPP’s. In the present paper the described event has been analyzed using system thermal hydraulics code RELAP5/MOD3.2 with KKNPP 1&2 nodalization. The variation in safety parameters during the event is not readily available in the literature. Selected enveloped cases [4–7] were analyzed with the developed RELAP model and the result was validated with the results of Final Safety Analysis Report for KKNPP-1.

M. Mehta · S. Chaudhary (B) · A. Biswangri · P. Krishna Kumar · Y. K. Pandey · G. Biswas Nuclear Power Corporation of India Ltd., Mumbai 400094, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_117

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2 KKNPP-1&2 Reactors KKNPP-1&2 use enriched uranium in oxide form as fuel, light water as moderator and coolant. KKNPP is a two-circuit system namely primary and secondary circuits. Primary circuit contains radioactive coolant. It consists of a heterogeneous thermal neutron-reactor, four main circulation loops, and a pressurizer connected to one of the four loops. Each circulation loop consists of a steam generator and the main circulation pump which are connected to the reactor by cold and hot legs of the main circulation pipeline. The turbine-driven feedwater pumps deliver the feed water to steam generators. Simple schematic of KKNPP-1&2 Reactor primary circuit is shown in Fig. 1. The secondary circuit is a non-radioactive circuit. It consists of steam generators, main steam lines, turbine, auxiliary equipment, and associated systems of de-aeration, reheating, and feed water supply to the steam generators. The main steam at 6.27 Mpa through governor valves flows to high-pressure turbine and is allowed to expand, which drive the generator to produce electricity. The reactor has been provided with two diverse, fast-acting, and independent shutdown systems. Secondary system consists of overpressure protection valves. There are four SGPSD on each steam generator line among which two opens at 8.23 Mpa and the other two at 8.43 Mpa. It also consists of an atmospheric steam discharge valve (BRU-A) and condenser steam discharge valve (CSDV) mounted on the main steam header.

Fig. 1 Simple schematic of KKNPP-1&2 Reactor primary circuit

Safety Analysis of Loss of NPP Off-Site Power …

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3 RELAP-5 Model of KKNPP-1&2 To analyze system thermal hydraulic behavior under ATWS scenarios a detailed nodalization was developed for the core, primary system, secondary system, feed water train, and safety systems. ATWS mitigation systems, Quick Boron Injection System (QBIS), and Emergency Boron Injection System were also modeled. The nodalization used is shown in Figs. 2 and 3. Nodalization of primary circuits involves reactor, circulation loops, and pressurizer. To describe the reactor, four elements are considered: reactor core, downcomer, lower plenum, and upper plenum. The reactor core is simulated by maximum power channel, average power channel, and bypass path. The primary coolant system is represented by four circulation loops and linked with each other by multiple junctions. Each loop is divided into the hot leg, Steam generator (SG) hot collector, SG tubing, SG cold collector, main circulation coolant pipeline, Reactor Coolant Pump (RCP), and cold leg. The RCPs have been modeled by pump characteristics, rated flow, head, and speed (Fig. 4). Nodalization of secondary circuit involves four steam generators, streamlines, and main steam isolation valves (MSIV’s). All four steam lines from each SG join to main steam line header followed by the turbine stop valve, governor valve and SG Tubes

RCP

Reactor Core Fig. 2 KKNPP-1&2 Reactor primary circuit nodalization

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Fig. 3 KKNPP-1&2 Reactor primary circuit nodalization Fig. 4 Reactor power

1 Relative Core power Relative neutronic Power

Power (MW)

0.8

0.6

0.4

0.2

0 0

20

40

60

Time (Sec)

80

100

Safety Analysis of Loss of NPP Off-Site Power …

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finally connecting to the turbine. The relief devices like atmospheric steam relief valves (BRU-A), Condenser dump valves (BRU-K), and Steam generator relief valves (SGPSD) are also modeled thereafter. The steam generator blowdown and emergency cooldown system (SGECD) was also modeled in the pressure maintenance mode for all the four steam generators which are independent of Nuclear Power Plant AC power as they work on Diesel Generator sets. Detailed trip and actuation logics are also modeled. Reactivity feedbacks due to fuel temperature, coolant temperature, and boron concentration change in the core are simulated using control parameters coupled with the neutronic point kinetics model. Decay heat data is used from the 1979 standard data (American National Standard for Decay Heat Power in Light Water Reactors) for U-235 in Light Water Reactors [8].

4 Assumptions In this analysis, the following assumptions have been considered: • Reactor thermal power is considered as 3120 MW (104% of rated power). • Primary coolant pressurizing system which includes injection and pressurizer heaters along with Chemical and volume control system of the primary circuit is not taken into account. • As a single failure, the failure of one Diesel Generator (DG) is assumed in the calculation, which results in the failure of one channel of active safety systems. Besides, in the calculation the account is taken for bringing one DG into repair as a result of which another channel of the active safety system is out of operation. • As a single failure, the failure of one train of passive safety system is assumed in the calculation. • The scram signal is generated and arrives in the control safety systems of the reactor plant, but motion of control rods downwards does not occur. All data, i.e., geometric, physical, control, and instrumentation parameters are taken from KKNPP-1&2 Final Safety Analysis Report (FSAR) [9]. The initial conditions of the plant are mentioned in Table 1.

5 Results and Discussion The analysis aims to find the thermal hydraulic and neutronic response of all systems during the event progression. Following Loss of Power Supply, trip of Reactor Coolant pump, closure of turbine stops valves, and closure of feedwater valves occurs along with the generation of SCRAM signal. With the loss of forced circulation in primary circuit and removal of heat from secondary circuit, the primary and secondary pressure and temperature start increasing (Figs. 5 and 7). In the present

1230 Table 1 Plant initial conditions

M. Mehta et al. Parameter

Value

Reactor thermal power (MW)

3120 (max.)

Temperature at the reactor inlet (°C)

293.0 (max.)

Primary pressure at the core outlet (MPa)

15.4 (min.)

Coolant flowrate through the reactor (min.) (m3 /h)

82,200 (min)

Initial concentration of boric acid in coolant (g/Kg)

9.2

Steam pressure in the steam generator steam header (MPa)

6.37 (max.)

Steam flowrate to turbine (Kg/s)

1704.0

Feedwater temperature in SG (°C)

225 (max.)

Maximum linear heat flux (W/cm)

448

Fig. 5 Primary pressure

19 Core Inlet Pressure Core Outlet Pressure Pressurizer Pressure

18

Pressure (MPa)

17

16

15

14

13 0

40

80

120

160

200

Time (Sec)

analysis failure of SCRAM is postulated which makes the situation worse and leads to further increase of primary and secondary parameters. Due to an increase in primary pressure, the pulse safety device located above pressurizer gets actuated at pressure of 18.11 MPa to release primary pressure.

Safety Analysis of Loss of NPP Off-Site Power … Fig. 6 DNBR

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3 Minimum DNBR

Minimum DNBR

2.5

2

1.5

1

0.5 0

3

6

9

12

15

18

21

24

27

Time (Sec)

Due to deterioration of core heat removal, Departure from Nucleate Boiling (DNB) [10] occurs at 12.0 s (Fig. 6). As a result of it, the temperature of fuel rod claddings increases and reaches maximum value of 367.0 °C at 15.0 s (Fig. 10). On ATWS signal (Fig. 4), safety systems designed for its mitigation gets actuated. Quick Boron Injection System (QBIS) and Emergency Boron Injection System (EBIS) are passive and active safety systems designed for ATWS mitigation respectively. QBIS passively injects borated water in primary coolant to bring down the reactor in sub-critical state. It injects borated water due to pressure differential of RCP suction and discharge. In case of trip of RCP, injection takes place due to the coast down of RCP. EBIS actively injects borated water into primary coolant through reciprocating pump these safety systems bring the reactor to nearly zero fission power (Figs. 8 and 9). The decay heat remained in the system is removed by Steam Generator Emergency Cool Down (SGECD) system. SGECD takes steam from SG and condenses it to subcooled water before putting it back to the respective SG. The stabilization of reactor plant parameters is verified in this analysis, thus ensuring a safe state of the reactor plant. Table 2 presents the chronological sequence of events and operation of systems during the transient.

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Fig. 7 SG pressure

8.5 SG-1 Pressure SG-4 Pressure SG-2 Pressure SG-3 Pressure

Pressure (MPa)

8

7.5

7

6.5

6 0

500

1000

1500

2000

Time (Sec)

6 Conclusion The maximum pressure of the primary and secondary circuits reached is 18.46 and 8.28 MPa, respectively, during the whole transient (Figs. 5 and 7) which is within acceptable limits based on failure resistance of the reactor vessel. The primary reactor coolant system was maintained in cooling downstate during short-term and long-term periods of time by settling a stable coolant flow rate in the primary circuit due to natural circulation. Decay heat removal from the reactor core is provided due to the operation of safety devices of a steam generator and SG ECD. Maximum fuel rod claddings temperature reached during the event is 367.0 °C (Fig. 10) which indicate absence of any fuel clad damage. Temperature of the fuel did not exceed its initial value during the transient thereby avoiding any local fuel fragmentation and fuel damage. The Quick boron injection system (QBIS) and Emergency Boron Injection System (EBIS) are capable enough to bring the reactor to a prolonged sub-critical stage in an event of failure of control rods to drop in the core on reactor trip signal. Analysis of the conditions was performed with disregard for the operator’s actions and showed that passive and active safety systems designed for ATWS mitigation with taking the principle of single failure into consideration are capable to overcome the accident consequences.

Safety Analysis of Loss of NPP Off-Site Power … Fig. 8 Boron concentration in core

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11.6

Boron Concentration (g/Kg)

11.2

10.8 Boron Concentration at reactor core inlet

10.4

10

9.6

9.2

8.8 0

400

800

1200

Time (Sec)

1600

2000

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M. Mehta et al.

Fig. 9 Reactivity (%)

3 Reactivity Due to Boron Concentration Reactivity due to fuel temperature Feedback Reactivity due to coolant density feedback Total reactivity

2

Reactivity (%)

1

0

-1

-2

-3 0

400

1200

800

1600

Time (Sec) 400 Max. Fuel Cladding Temperature

390

Temperature ( 0 C)

380 370 360 350 340 330 320 310 300 -10

0

10

20

30

40

50

Time (Sec)

Fig. 10 Clad temperature

60

70

80

90

100

2000

Safety Analysis of Loss of NPP Off-Site Power …

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Table 2 Sequence of events 0.0

Trip of all RCP sets, closing of TG stop valves and loss of the main feedwater to SG’s

As a result of the initiating event

1.9

Arrival of signal for Reactor SCRAM. The signal has arrived but the rods have remained immovable

Trip of all RCP sets

3.5–10.5

Opening of BRU-A

The secondary pressure reaches set-point

6.2

Beginning of signal generation for connection of SG ECD system

Pressure in all SGs reaches ECD set-point

6.2

Signal generation for start-up QBIS and EBIS safety system

ATWS signal

11.5–19.0

Period of operation of control Pressurizer safety valve

Pressure above the core increases to the set-point

19.0–7.0

Period of operation of control SG’s safety valve

SG pressure reaches the set-point

126.2

Bringing of the SG ECD of loops 2 and 3 The time delay of 120 s

1440.0

Beginning of MSIV closing in loop 1

SG1 low level

1528.5

Beginning of MSIV closing in loop 4

SG-4 low level

1738.0

Beginning of repeated periodic operation Pressure above the core increases to the of control Pressurizer safety valve set-point

2000.0

End of calculation

References 1. Ransom, V.H., et al.: RELAP5/MOD2 Code Manual, Vol. 1: Code Structure, System Models, and Solution Methods, NUREG/CR-4312, EGG-2396 (1985) 2. Qi, T., Yang, C.: Safety analysis on mitigation overpressure ATWS in VVER-1000 NPP. In: 21st International Conference on Nuclear Engineering, vol. 3, Chengdu, China, July 29–August 2 (2013) 3. Kyrki, R.: Three Dimensional Reactor Dynamics Code for VVER Type Nuclear Reactor, vol. 28. VTT Publications(1995) 4. Mehta, M., Krishna Kumar, P., Pandey, Y.K., Biswas, G.: Transient thermal hydraulic studies of turbine trip for VVER-1000 reactor. In: Proceedings of the 7th International and 45th National Conference on Fluid Mechanics and Fluid Power (FMFP). IIT Bombay, Mumbai, India (2018) 5. Chaudhary, S., Krishna Kumar, P., Pandey, Y.K., Biswas, G.: Study and thermal hydraulic analysis for steam generator feed water pipe break For KKNPP using computer code Relap5/Mod 3. In: Fifth International Conference on Computational Methods for Thermal Problems. Indian Institute of Science, Bangalore, India (2018) 6. Chaudhary, S., Krishna Kumar, P., Pandey, Y.K., Biswas, G.: Thermal-Hydraulic analysis of partial loss of forced reactor coolant flow with non-uniform and asymmetric loop flow mixing in VVER-100. In: Proceedings of the 7th International and 45th National Conference on Fluid Mechanics and Fluid Power (FMFP). IIT Bombay, Mumbai, India (2018) 7. Biswangri, A., Krishna Kumar, P., Pandey, Y.K., Biswas, G.: Thermal-hydraulic analysis of VVER-1000 in case of reactor coolant pump shaft seizure. In: Proceedings of the 7th International and 45th National Conference on Fluid Mechanics and Fluid Power (FMFP), IIT Bombay, Mumbai, India, (2018)

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8. American National Standard for Decay Heat Power in Light Water Reactors, “ANSI/ANS-5.1” (1979) 9. KKNPP-1&2 Final Safety Analysis Report (FSAR): Accident Analysis, Section 15.8.1, Rev. 0 (2016) 10. Mehta, M., Chaudhary, S., Biswangri, A., Krishna Kumar, P., Pandey, Y.K., Biswas, G.: Approaches adopted for CHF evaluation during transients using system analysis code RELAP5 for KKNP. In: National Conference on Critical Heat Flux and Multiphase Flow, IIT-BHU (2018)

P-type Crystalline Silicon Surface Passivation Using Silicon Oxynitride/SiN Stack for PERC Solar Cell Application Irfan M. Khorakiwala, Vikas Nandal, Pradeep Nair, and Aldrin Antony

1 Introduction Conventional p-type crystalline silicon-based solar cell technology uses aluminum back surface field for passivating the backside. The surface recombination velocity is very high compared to that of thermally grown silicon oxide passivation. Aluminum oxide grown by plasma-assisted atomic layer deposition (ALD) gives the best surface passivation [1] on p-type silicon, but employing these ALD processes on large scale is very challenging, and also, the deposition rate is very low. So in light of this, we try a relatively non-ideal passivation mechanism on the backside of p-type-based silicon wafers by hydrogenated silicon oxynitride (SiOx Ny :H, from now on will be referred to as simply SiON) layer with silicon nitride (Six Ny , from now on will be referred to as simply SiN) capping layers. These layers can easily be deposited on large scale using plasma-enhanced chemical vapor deposition (PECVD) at low temperature with high deposition rates. Additionally, the SiON layer has low refractive index (less than 2) and their refractive index can be easily tuned [2] by changing the N2 O precursor gas flow rates during deposition. These low refractive index values help SiON to become good IR reflectors at the rear side of the cell, which can boost the short-circuit current in solar cells. In light of this, we have tried to study how the passivation mechanism of SiON/SiN stack can be improved by extracting important information like fixed charge, mobile charge, and interface charge density using lowand high-frequency capacitance–voltage (C-V) measurements. We have also looked into the carrier lifetimes associated with such samples and tried to correlate the effect of these charges on the resulting carrier lifetime. SiN capping layers were deposited on the SiON layer, and the influence of this capping layer on the different types of charges in the dielectric is studied. I. M. Khorakiwala (B) · V. Nandal · P. Nair · A. Antony Department of Electrical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra 400076, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_118

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2 Fabrication Details The SiON and SiN layers were deposited using plasma-enhanced chemical vapor deposition (PECVD) using Plasmalab 100 Oxford. We made three sets of samples with different compositions of SiON layers with different oxygen to nitrogen ratios by varying the N2 O gas flow rates during deposition. We also made another sample with SiON/SiN stack as the dielectric passivation layer to demonstrate the importance of the SiN capping layer. All the films were deposited at a temperature of 380 °C at an rf power density of 61.7 mW/cm2 . All the SiON dielectric films have a thickness of 100 nm, and in the case of double stack the SiON layer is 30 nm thick and the capping SiN layer is 70 nm thick. The precursor gases used for SiON deposition are silane (SiH4 ), hydrogen (H2 ), and nitrous oxide (N2 O). Different compositions of oxygen and nitrogen were incorporated by fixing the silane flow rate to 15 sccm, hydrogen flow rate to 20 sccm, and varying the nitrous oxide gas flow rate. Three different N2 O flow rates used are 45, 60 and 135 sccm. The gas mixture ratio (R) of the SiON deposition is given by RSiON =

∅N2 O ∅N2 O + ∅SiH4

(1)

where ∅SiH4 and ∅N2 O are the gas flows of SiH4 and N2 O, respectively, in sccm. The sample names of the silicon oxynitride films can be represented as SiOxNy_RSiON where RSiON has the values 0.75, 0.8, and 0.9. The SiON layers are deposited at a pressure of 700 mTorr. The silicon nitride layer was deposited using 25 sccm of silane, 980 sccm of nitrogen, and 20 sccm of NH3 . The SiN layer was deposited at a pressure of 650 mTorr. All these depositions were done over p-type (100) crystalline silicon wafer after the wafers were RCA (standard cleaning process RCA 1 and RCA 2) cleaned. Before the deposition, the wafers were dipped in 2% HF solution to remove the native silicon oxide from the surface. We made “MOS capacitor”-type structures by depositing SiON single layer and SiON/SiN stack on the p-type substrate followed by front (using shadow mask) and backside (blanket layer) metal deposition. Four different “MOS capacitor”-like structures named as samples A, B, C, and D were fabricated. Samples A, B, C, and D have different dielectric insulating layers, namely SiON 0.75, SiON 0.8, SiON 0.9, and SiON 0.9/SiN stack. On these test structures, we performed high- and low-frequency C-V measurements using Proxima (Fast IV measurement/B1500) tool. The obtained C-V data were used to quantify the amount of fixed (Df ), mobile (Dm ), and interface trap (Dit ) charge density in these dielectric layers when contacted with the p-type silicon substrate. For measuring the carrier lifetime, we deposited SiON single layers and SiON/SiN stack layer on both sides of the silicon substrate and carried out the carrier lifetime measurement using quasi-steady-state photoconductance (QSSPC) method using a Sinton WCT 120 tool.

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3 Results and Discussions In order to understand the passivation mechanism of the dielectric layer(s), capacitance–voltage (C-V) measurements were carried out at room temperature at four different frequencies (1 kHz, 10 kHz, 100 kHz, and 500 kHz) to see the effects of fixed charge (Df , in the dielectric layer close to the c-Si interface), mobile charge (Dm , in the dielectric layer), and interface charge (Dit , at the dielectric/c-Si interface) on the passivation quality of the different SiON layers and particularly on one of the SiON_0.9/SiN stack. The fixed and interface charges originate because of dangling bonds or defects but the nature of these defects can be different. The fixed charge in SiON is predominantly known to be positive [3] as is the case with SiN. Fixed charge in the oxide shifts [4] the flat-band voltage of the C-V curve in one direction depending on whether the fixed charge is positive or negative. The amount of shift depends on the position of the fixed charge from the dielectric/c-Si interface. The amount of fixed charge introduced in the dielectric also depends on the type of deposition used. The flat-band voltage of the C-V curve also shifts by the presence of mobile charges in the dielectric. These mobile charges are basically ions moving through interstitial sites under the influence of the applied electric field. The interface charges at the dielectric/c-Si interface also shifts the flat-band voltage of the C-V curves and also stretches the C-V curve depending on the charge occupancy of the interface defect states, which is dictated by the position of the Fermi energy level as the voltage is applied. Now, we discuss the C-V data obtained for our samples (Samples A, B, C, and D). The capacitance–voltage measurements in the forward and reverse sweep directions for four different frequencies are shown in Fig. 1 and Fig. 2, respectively. The x-axis of these plots is labeled as V top which means that the voltage was applied to the gate electrode with the back electrode at ground potential. From Figs. 1, 2, we see that the flat-band voltage of each of these samples is highly negative. Since the flat-band voltage has shifted in the negative direction, the polarity of fixed charge in these layers close to the c-Si interface is positive. Samples A, B, and C have higher fixed charge density (Df ) compared to sample D. To calculate the fixed charge density in these samples, we measured the shift in flat-band voltage (V FB ) compared to the ideal case when there are no charges (no mobile, fixed, and interface charges) at all in the dielectric layers. When there are no charges in the dielectric layer, the flat-band voltage in that case is simply the difference in work function (MS ) between the gate metal and the p-type silicon substrate. For our case, the gate metal used was aluminum with a work function of 4.1 eV and the substrate doping used was 1016 cm−3 ; in this case, MS turns out to be −0.83 V. The shift from this value is what determines the amount of fixed charges in these dielectric layers. The C-V plots in the forward (Fig. 1) and reverse (Fig. 2) sweep directions show hysteresis clearly indicating the presence of mobile charges in the dielectric layer. The amount of mobile charge density was calculated based on the difference in flatband voltage (V FB ) in the forward and reverse voltage sweep directions at a given frequency; here, we have calculated the mobile charge density at 1 kHz. The formula

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Fig. 1 Comparing C-V plots of four samples at four different frequencies during forward voltage sweep Note that the voltage range for sample D is from −10 to +10 V

(shown in blue) used to calculate the mobile charge density (Dm ) can be seen in Fig. 4. The presence of mobile charge can be seen clearly in Fig. 3 where we have shown the hysteresis at a frequency of 1 kHz. The interface trap density (Dit ) was calculated by using the information obtained from both low-frequency (1 kHz) and high-frequency (in our case, 10 kHz) C-V data. The obtained values for four different samples are shown in Fig. 4. The flat-band capacitance and interface trap charge density were calculated using the following set of formulas:  εs Vt (2) LD = q NA CFB =

Dit =

1 1 Cox

+

Cox =

εox tox

Cox C L F Cox −C L F

− qA

LD εS

(3) (4)

Cox C H F Cox −C H F

(5)

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Fig. 2 Comparing C-V plots of four samples at four different frequencies during reverse voltage sweep Note that the voltage range for sample D is from −10 to +10 V

where L D is the Debye length of holes, q is the charge, V t is the thermal voltage, N A is the acceptor (boron) concentration, C ox is the “oxide” capacitance which in our case is the dielectric layer capacitance, εS is the electric permittivity of the c-Si base wafer, εox is the electric permittivity of the dielectric layer, C LF and C HF are the capacitances at low (1 kHz) and high (10 kHz) frequency, respectively, and A is the area of the “MOS” capacitor. All capacitances are expressed in terms of capacitance per unit area. Now to summarize the three different types of charges explored we refer you to Fig. 4. It can be seen that the fixed charge density peaks at sample C, i.e., SiON_0.8 and from then on falls off. In the case of sample D, the fixed charge density is the least; note that the fixed charge density for sample D cannot be seen in Fig. 4 as the density is too low for it to be shown in this figure. This reduction in fixed charge can be attributed to the SiN capping layer. The reason for this improvement in passivation is that during the plasma deposition of silicon nitride a large amount of hydrogen is released which helps in passivating many surface and bulk defects. This reduction in fixed charge is a boost to the majority holes in the p-type substrate near the SiON/c-Si interface which were earlier facing an opposing electric force due to the positive fixed charges which explains why the flat-band voltage of the dielectric stack capacitor is much lower compared to the single SiON layer capacitors. The mobile charge

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Fig. 3 Hysteresis obtained at 1 kHz between the forward and reverse voltage sweep direction for four different samples Fig. 4 Extracting the non-ideal charges in the four different dielectric layers Note Dt corresponds to the total positive charge

P-type Crystalline Silicon Surface Passivation … Table 1 Lifetime and implied V oc of p-type c-Si wafer with dielectric passivation layers deposited on both sides of the silicon wafer

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Sample name

Lifetime (µs)

Implied V oc

SiON_0.75

52.1

621

SiON_0.8

48.3

620

SiON_0.9

30.6

608

SiON_0.9/SiN

64.7

627

density is highest for sample D and lowest for sample C. The higher mobile charge density of sample D could be because the SiN layer is denser than the SiON layer, and hence, the mobile carriers are mostly centered in the bulk of the SiON layer. The interface trap charge density is highest for sample B and least for sample D. The reduction of Dit for sample D is again because of the hydrogen passivation which reduces the interface dangling bonds and this hydrogen comes from the SiN capping layers during their plasma deposition. The carrier lifetime and implied V oc of c-Si passivated on both sides by four different dielectric layers are shown in Table 1. For single SiON layers as the N2 O flow rate is increased, the passivation property becomes more and more inferior as can be seen from Table 1. In the literature, Zhuo et al. [5] have reported that incorporating more nitrogen into the SiON layers degrades the passivation quality of these layers. The passivation quality obtained when a dielectric stack of SiON_0.9/SiN is used instead of single SiON_0.9 layer is significant as can be seen from Table 1. The reason for this improvement in passivation was discussed earlier. The Dit calculations and the above-listed lifetime are in close agreement to one another, and hence, the impact of interface state trap density is most significant compared to the other kinds of charges discussed in this work.

4 Conclusion In this paper, we explored the possibilities of SiON/SiN as backside passivating dielectrics for p-type crystalline silicon substrates (SiON/SiN act also as good antireflective coatings for infrared photons) as these layers can be deposited easily on large scale using PECVD at low temperatures and at high deposition rates. We have quantified three different types of charges predominant in these dielectric insulators, namely fixed charge, mobile charge, and interface trap charge. These charges were calculated from the low- and high-frequency capacitance–voltage measurements of the “MOS” like capacitor structures incorporating four different dielectric insulators (SiON_0.75, SiON_0.8, SiON_0.9, and stack of SiON_0.9/SiN). It is found that incorporating more nitrogen during the PECVD deposition is detrimental to the interface passivation between crystalline silicon and the dielectric layer(s) as is evident from the carrier lifetime measurements (Table 1) and the calculated interface charge density (Fig. 4). We demonstrated the importance of the SiN capping layer which helps in reducing the interface charges and also the positive fixed charges in

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the SiON_0.9 layer after it is capped. The benefits of SiN capping layer are that during its plasma deposition on SiON a large amount of hydrogen is released which passivates the interface and bulk defects of SiON. Finally, because of the very close correlation between the calculated interface charge density (Dit ) and the measured carrier lifetime, it can be said that the interface charges have the biggest impact on the passivation quality of the dielectric layers, and hence in order to use these layers for backside passivation of p-type crystalline silicon, we have to choose that combination of SiON and SiN which gives us the least interface charge density.

References 1. Hoex, B.: Excellent passivation of highly doped p-type Si surfaces by the negative-chargedielectric Al2 O3 . Appl. Phys. Lett. 91(11), 112107 (2007) 2. Soman, A.: Broad range refractive index engineering of Six Ny and SiOx Ny thin films and exploring their potential applications in crystalline silicon solar cells. Mater. Chem. Phys. 197, 181–191 (2017) 3. Bonilla, R.S.: Dielectric surface passivation for silicon solar cells: a review. Phys. Status Solidi (A) 214.7, 1700293 (2017) 4. Van Zeghbroeck, B.: Principles of semiconductor devices. Colarado Univ. 34 (2004) 5. Zhuo, Z.: Interface properties of SiOx Ny layer on Si prepared by atmospheric-pressure plasma oxidation-nitridation. Nanoscale Res. Lett. 8(1), 201 (2013)

Pressure Propagation and Flow Restart in the Subsea Pipeline Network Lomesh Tikariha and Lalit Kumar

1 Introduction The percentage increase in waxy crude oil resources and its economical transportation through the pipeline in a cold climate like subsea or alpine region play a pivotal role in the overall flow assurance study. The ubiquitous high-temperature gradient across the pipeline material together with a significant percentage of wax present in crude oil results in wax precipitation and deposition on the pipeline wall. Waxy crude oil is a complex mixture of low-to-high molecular weight hydrocarbon, which also contains paraffin wax. These waxes are a significant source of the problem, which start precipitating at low temperature and pressure. The temperature below which these waxes start precipitating is known as wax appearance temperature (WAT). Below WAT, wax starts precipitating first near to wall and deposits on inner tubing, decreases the flow area, reduces overall transportation efficiency [1]. At times of emergency and maintenance shutdown operation of the prolonged period, a substantial increase in heat loss to ambient takes place. This leads to formation of waxy crystal interlocking network which entrapped the remaining liquid, making the mixture semi-solid like gel structure. To restart the steady-state operation, the pressure higher than the usual operating range is required to breakdown the gel network. A simplified prediction of pressure requirement is estimated by balancing applied pressure force to initial shear resistance. PR =

4τ y L D

(1)

L. Tikariha (B) · L. Kumar Department of Energy Science and Engineering, Indian Institute of Technology, Bombay 400076, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_119

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where PR is the pressure required, τ y is the static yield stress of waxy gel, L is the critical pipe length, and D is the pipe diameter. In this estimation, the yield stress is obtained through the rheometric experiment. However, this assessment always overestimates the actual pressure requirement. The major discrepancies are due to: (i) Variation in yield strength across and along the pipe length, (ii) Low-temperature gel shrinkage induced flow and appearance of gas voids reduces the gel strength, and (iii) Difference in thermal and shear history experienced during gelation. In order to reduce predictive error involved in the measurement and to incorporate complex variation in the gel strength, research workers numerically simulate the restart process. Chang et al. [2] demonstrated the three stages of gel deformation, namely elastic response at initial stage, creep deformation below static yield stress, and large deformation with gel structure failure above yield stress. Based on the gel behaviour as mentioned above, three yield stress model has been utilized to predict the restart operation of gelled pipeline. The time-dependent Bingham model (i.e. decrease in strength with constant load) allowed to restart steady-state operation below the pressure required to overcome static yield stress. However, steady-state momentum balance equation and incompressibility do not allow to capture transient changes in the restart process. Later, Davidson et al. [3] modified three yield stress model to account the compressibility of gas voids in their pipeline simulation predicts early flow restart. In another work, Wachs et al. [4] consider the radial and axial changes in axial velocity, with negligible radial velocity, named as 1.5-dimensional numerical simulations of flow restart. In their analysis, the time-dependent Houska model is utilized to describe thixotropic behaviour of gel medium. The combined effect of gel compressibility and time-dependent gel strength allowed to restart operation below pressure requirement estimated by Eq. (1). However, their model is unable to capture creep deformation below yield stress and corresponding gel degradation. To account creeping flow during applied pressure propagation, Kumar et al. [5] consider strain-dependent shear thinning model for pipe length larger than critical length L used in Eq. (1). The combined effect of gel compressibility and slow gel degradation allowed to restore steady-state operation. In order to examine the effect of time-dependent gel strength on pressure wave propagation, El-Gendy et al. [6] conducted flow loop experiment on model oil gel. The initial and time-delayed jump in outlet pressure asserted that pressure propagation is two-step mechanism. The time-delayed second jump in pressure signal was due to gel deformation. Unlike others, Philips et al. [7] presented in their simulation the effect of low-temperature gel shrinkage following shut-in operation. The slow shrinkage flow prior to gelation modified the gel strength. The insight obtained through the above-mentioned works forms the basis of current work. The present work considers the low-temperature shrinkage flow in subsea pipeline (i.e. following earth terrain) and consequent accumulation of free gases at low pressure points of pipeline. The presence of gases at high points (i.e. low pressure points) of pipeline leads to formation of multi-pluggelled pipeline. In this context, pressure wave propagation and its attenuation to restart operation in the multi-plug-gelled pipeline are investigated.

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2 Gel Rheology Modelling the restart process in gelled pipeline requires applied pressure by secondary fluid must be sufficient to breakdown the gel structure and restore steadystate operation. A model which describes the gel breakdown process (often in terms of shear stress, shear rate and structural parameter) serves as the primary input to restart simulators. Typically, the waxy oil exhibits time, temperature, and shear rate dependent gel behaviour below WAT. Therefore, it is necessary to develop a model which accounts shear history throughout the breakdown process. Accounting the effect of absolute strain on gel breakdown process, Kumar et al. [5] described the strain-dependent viscosity for constant temperature and wax concentration. μgel

   1 − e−co γ = μo 1 + m γ

(2)

where co is rate constant, m is constant, γ is absolute strain subjected to gel, and μ0 is the slurry state viscosity of crude oil. The small deformation (i.e. strain) associated with creep flow is presented by considering the high value of gel viscosity [5]. Therefore, the model used in present work is capable of demonstrating creep deformation as well as gel structure breakdown without the use of true yield stress value. Equation. (2) is utilized to describe instantaneous gel viscosity in order to solve the restart process of the multi-plug-gelled pipe.

3 Mathematical Formulation 3.1 Governing Equations In order to numerically simulate the flow restart in multi-plug-gelled pipeline, a mathematical model is developed. The equation governing the mass and momentum balance equation written in cylindrical coordinate are described below. Continuity equation:   ∂ρ + ∇. ρ V¯ = 0 ∂t

(3)

Momentum equation: r - direction : ρ

Dv ∂ p 1 ∂(r τrr ) ∂τzr τθθ =− + + − + Fr Dt ∂r r ∂r ∂z r

(4)

∂ p 1 ∂τr z ∂τzz Du =− + + + Fz Dt ∂z r ∂r ∂z

(5)

z - direction : ρ

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where   ∂v 2 + ξ − μ ∇.V¯ ∂r 3   2 ∂u + ξ − μ ∇.V¯ τzz = 2μ ∂z 3   ∂v ∂u τr z = μ + ∂r ∂z   v 2 τθθ = 2μ + ξ − μ ∇.V¯ r 3 τrr = 2μ

(6) (7) (8) (9)

In the above equation, v and u are radial and axial components of velocity in cylindrical coordinates, respectively. We assume that the fluid considered is a Stokes fluid, i.e. ξ = 0. In order to use rheological model described in Eq. (2), we need to find the value of absolute strain at all the grid points.   ∂γ ∂γ ∂γ + v +u = ||d|| ∂t ∂r ∂z where d is strain tensor given by d =

1 2

2 + d = dzr



(10)

 T ∇ V¯ + ∇ V¯ and

 1 2 2 2 d + dθθ + dzz 2 rr

(11)

The surface tension coefficient σ is assumed to be constant. So the continuum surface force formulation gives, F = Fr rˆ + Fz zˆ = σ knδT

(12)

where δT is a delta function used to mollify the volume fraction function, k is interface curvature, and n is normal to the interface. The curvature k is defined as k = ∇.n n=

∇ψ |∇ψ|

(13) (14)

To evaluate force interaction between different phases, we need to track the interface between the two phases. To analyse two-phase flow, the volume of fluid (VOF) method is used [8]. In a VOF method for a two-phase system volume fraction ψ is defined as

Pressure Propagation and Flow Restart in the Subsea …

ψ=

1249

⎧ ⎨

1, lies in fluid 1 0 < ψ < 1, interface passing through cell ⎩ 0, lies in fluid 2

The motion of the interface is captured by the advection equation of the volume fraction given by, ∂ψ + V¯ ∇.ψ = 0 ∂ tˆ

(15)

With the use of isothermal compressibility, gel density ρgel can be written as: ∂ρgel = Xθ ∂ p ρgel

(16)

Therefore, the density of the fluid in the cell (i, j) can be obtained as: ρ = ψρgel + (1 − ψ)ρgas

(17)

Similarly, the viscosity of the fluid in the cell (i, j) can be obtained as: μ = ψμgel + (1 − ψ)μgas

(18)

Equations (2)–(18) complete the set of equation to be solved by employing the finite volume method.

3.2 Flow Geometry and Boundary Conditions A 2D axisymmetric pipeline described by cylindrical coordinates (r, θ, z) is shown in Fig. 1. To simplify our analysis, a horizontal pipeline is considered. The boundary and initial conditions for the flow domain are explained below: At the inlet boundary: v =τzz = 0

Wall Gel

Gas

Gel

Wall

Fig. 1 Schematic diagram of the multi-plug pipeline and coordinate system

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p = pinlet γ =γinlet At the outlet boundary: v =τzz = 0 p = poutlet =

∂γ =0 ∂z

At the pipe wall: u=v=0 Along the axis of symmetry: v = τr z =

∂γ =0 ∂r

In addition to the above initial and boundary conditions, the waxy gel is considered to be stationary at surrounding ambient condition. The secondary fluid is injected from inlet to apply constant pumping pressure.

3.3 Non-dimensionalization and Scaling Analysis In this case, a low aspect ratio ( = R/L) long pipeline is considered. In which radial Rτ velocity v and axial velocity u are defined in terms of Ws = 2μoy as: v = vW ˆ s and u = uW ˆ s . The radial and axial coordinates are scaled as r = rˆ R and z = zˆ L. The pR . The pressure is scaled by critical pipe length (L) is defined using Eq. (1), L = 2τ y p the applied pressure difference expressed as: pˆ = P . To capture the fast-moving pressure signal in gelled medium, time is scaled with the compressibility of gel t . Where compressibility number is defined as: δ = Xθ P and defined as: tˆ = L√δ/W s Xθ is compressibility of waxy gel. Deformation of the gel plug is defined in terms of strain scaled as WLs . The other non-dimensional numbers are defined as: o L Ws Local Reynolds number, Re = 2ρ√ δμ o

μˆ =

P μ  and μo = μo μo τy

where ρo and μo are density and viscosity of crude oil at slurry state.

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3.4 Discretization Scheme For discretization of governing equation, finite volume method over a staggered grid is used. In this scheme, pressure p and volume fractions ψ are located at the centre of the computation cell (i, j), whereas velocity components u and v are located at the positive vertical and horizontal faces of pressure cell p(i, j) denoted by u(i + 0.5, j) and v(i, j + 0.5), respectively. For transient, convective, and viscous terms forward, central, and upwind differencing methods are employed. In order to calculate interface curvature k, adjacent eight computational cells volume fraction ψ is used.

3.5 Numerical Algorithm A mathematical model to flow restart in multi-plug pipeline requires the equations of continuity, momentum, and advection equation of volume fraction. These equations may then be solved numerically for the pressure, velocity, and volume fraction function of computational cell. The numerical algorithm is summarized below. Step (1) Define boundary and initial condition of u, ˆ v, ˆ γˆ , pˆ and ψ. Step (2) Initialize the time loop T = t t, t ≥ 1, • Initialize uˆ t+1 = uˆ t , vˆ t+1 = vˆ t , pˆ t+1 = pˆ t , ψ t+1 = ψ t , and γˆ t+1 = γˆ t • Solve the advection equation of volume fraction ψ for t ≥ 2 using variables from the previous step. • Solve the momentum equation for uˆ and v. ˆ • Solve the continuity equation to find the new pressure field. • Solve the strain evolution equation to find the new value of absolute strain. • Check convergence, if solution converges go to step 2 with t = t + 1, otherwise with t = t.   • Obtained data u, ˆ v, ˆ γˆ , pˆ and ψ .

4 Results Based on the mathematical formulation of multi-plug-gelled pipeline, the numerical simulation is carried out. The pressure applied at inlet equals to pressure requirement estimated from Eq. (1). In order to understand pressure propagation in weakly compressible gel medium, a homogeneous initial gel condition (t = 0) is assumed for pipeline longer than a critical length; although, one can easily incorporate variation in initial gel state. On application of pressure, the weakly compressible gel start deforming as the compressional front passes through the gel medium. The straindependent shear thinning model to described gel state alters the homogeneous initial gel condition. As the pressure wave passes through the gel plug, a large axial pressure

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Fig. 2 Time evolution of pressure profile for gas separated gelled plug. For compressibility number δ = 4 × 10−4 , void volume 5% and μr = 200

gradient is observed across the compressional front followed by small viscous resistance due to gel displacement shown in Fig. 2. If the compressive deformation of gel material is insufficient to breakdown the gel structure, strain continues to weaken the gel structure (i.e. thixotropic material) for an extended period resulting in gel failure. The continuous shearing and initial gel breakdown redistribute the stored pressure energy among the remaining intact gel structure. To verify our results, pressure profile obtained for the multi-plug-gelled pipeline has been compared with the results of pipeline filled with homogenous gel obtained in [5]. The pressure profile obtained at similar time instant exactly matches until it approaches gel and gas interface. Thereafter due to high compressibility of gas region, pressure propagates downstream with lower speed. As the pressure profile approaches outlet, part of the pressure releases to the atmosphere; therefore, reduction in pressure gradient is obtained near the outlet. The reduction in pressure gradient at the outlet is referred to as inertial puncture in [5]. After initial oscillation, the pressure profile attains the uniform pressure gradient at tˆ = 1958. The presence of gas pocket in between the gel plugs allowed individual gel plug to shear deform and breakdown quickly. Figure 3 represents the time evolution of the outlet axial velocity for multi-plugged-gelled pipeline. A positive  increase  in outlet flow rate is observed once the pressure signal approaches outlet tˆ = 320 . The existence of gas pocket in between gel plugs results in early flow commencement.

5 Conclusion The flow restart of multi-plug-gelled pipeline is analysed after long period of shutdown. To restore pipe flow, the applied pressure has to overcome the energy required to breakdown gel structure on its propagation for a long time. Besides, the combined effects of gel compressibility and thixotropic behaviour of gelled plug, the effect of

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Fig. 3 Time evolution of outlet axial velocity for multi-plug-gelled pipeline. For compressibility number δ = 4 × 10−4 , void volume 5% and μr = 200

gas pocket on the restart mechanism is analysed. For the same applied pressure and amount of gelled oil, multi-plug pipeline predicts early flow restart and gel breakdown. Therefore, the present model is an attempt to approach more feasible flow restart operation and to predict plausible source of discrepancy.

References 1. Venkatesan, R., Nagarajan, N.R., Paso, K., Yi, Y., Sastry, A.M., Fogler, H.S.: The strength of paraffin gels formed under static and flow conditions. Chem. Eng. Sci. 60, 3587–3598 (2005) 2. Chang, C., Nguyen, Q.D., Rønningsen, H.P.: Isothermal start-up of pipeline transporting waxy crude oil. J. Nonnewton. Fluid Mech. 87(2), 127–154 (1999) 3. Davidson, M.R., Nguyen, Q.D., Rønningsen, H.P.: Restart model for a multi-plug gelled waxy oil pipeline. J. Pet. Sci. Eng. 59(2), 1–16 (2007) 4. Wachs, A., Vinay, G., Frigaard, I.: A 1.5D numerical model for the start up of weakly compressible flow of a viscoplastic and thixotropic fluid in pipelines. J. Nonnewton. Fluid Mech. 159(3), 81–94 (2009) 5. Kumar, L., Zhao, Y., Paso, K., Grimes, B., Sjöblom, J.: Numerical study of pipeline restart of weakly compressible irreversibly thixotropic waxy crude oils. AlChE J. 61(8), 2657–2671 (2015) 6. Phillips, D.A., Forsdyke, I.N., McCracken, I.R., Ravenscroft, P.D.: Novel approaches to waxy crude restart: Part 1: thermal shrinkage of waxy crude oil and the impact for pipeline restart. J. Pet. Sci. Eng. 77, 237–253 (2011) 7. El-Gendy, H., Alcoutlabi, M., Jemmett, M., Deo, M., Magda, J., Venkatesan, R., Montesi, A.: The propagation of pressure in a gelled waxy oil pipeline as studied by particle imaging velocimetry. AlChE J. 58(1), 302–311 (2012) 8. Hirt, C.W., Nichols, B.D.: Volume of fluid (VOF) method for the dynamics of free boundaries. J. Comput. Phys. 39(1), 201–225 (1981)

Electrodeposition of Cu2 O: Determination of Limiting Potential Towards Solar Water Splitting Iqra Reyaz Hamdani and Ashok N. Bhaskarwar

1 Introduction Cuprous oxide (Cu2 O) is a low cost, and a nontoxic semiconductor, having intrinsically p-type electronic conductivity [1]. With the bandgap ranging from 2.0 to 2.2 eV, it has been found to be an excellent photon absorber towards solar-driven water splitting [2], with good theoretical solar to hydrogen conversion efficiency (~18%). Till now, various deposition techniques have been explored for Cu2 O thin film deposition, such as spray pyrolysis, sputtering, electrodeposition, chemical vapor deposition, thermal and chemical oxidation, and so on. However, electrodeposition presents the most convenient method among all, because this process is economical [3, 4] and provides easy control on the physical properties of film, such as surface morphology, thickness, crystallinity, and roughness by changing the deposition parameters, such as electrolyte pH, applied potential, electrolyte temperature, and concentration of electrolyte [5]. Thus, Cu2 O prepared by electrodeposition method is an inexpensive, sustainable, and efficient material. In this work, Cu2 O thin films were electrodeposited on FTO substrates at different biasing potentials. A comprehensive study was done to investigate the effect of applied potential on the morphological and optical properties of Cu2 O. The performance of Cu2 O as a photocathode in a photoelectrochemical device, and hence its application in photoelectrolysis of water towards hydrogen evolution, was determined and analyzed with respect to the changing applied potentials.

I. R. Hamdani · A. N. Bhaskarwar (B) Indian Institute of Technology Delhi, New Delhi 110016, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_120

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2 Experimental Details Electrodeposition of Cu2 O was done in a conventional single compartment threeelectrode electrochemical cell, fitted with a flat quartz window, with FTO substrate (Pilkington, sheet resistance