Towards Sustainable Chemical Processes: Applications of Sustainability Assessment and Analysis, Design and Optimization, and Hybridization and Modularization [1 ed.] 0128183764, 9780128183762

Table of contents :
Cover
Towards Sustainable
Chemical Processes:
Applications of Sustainability
Assessment and Analysis, Design and
Optimization, and Hybridization and
Modularization
Copyright
Contributors
Part 1: Sustainability assessment and analysis
Sustainability assessment for chemical product and process design during early design stages
Introduction
Framework for the sustainability assessment of chemical products and processes at early design stages
Problem definition
Assessment of alternatives
Selection of indicators
Calculation of indicators
Integration of assessments
Calculation of weights of Indicator groups using AHP
Ranking of alternatives using PROMETHEE
Application of the methodology to the case studies
Selection of the chemical process route to produce glyceryl monostearate
Problem definition
Assessment of alternatives
Integration of assessments
Selection of the most sustainable alternative of a formulated product
Problem definition
Assessment of alternatives
Integration of assessments
Conclusions
References
Further reading
Optimization and decision-making methods in realization of tri-generation systems
Introduction
Design methods
Maximum rectangle method
Energy management sizing method
Thermodynamic sizing method
Thermo-economic sizing method
Multicriteria sizing method
Fitness function method
Evaluation criteria
Optimization methods
Mixed-integer linear programming (MILP)
Mixed-integer nonlinear programming (MINLP)
Stochastic optimization
Genetic algorithm
Particle swarm optimization (PSO)
Multiobjective optimization
Decision-making methods
Cost-benefit analysis (CBA)
Elementary methods
Multicriteria decision-making (MCDM)
Sensitivity and risk analysis
References
Further reading
Techno-economic assessment of an integrated bio-oil steam reforming and hydrodeoxygenation system for polyge
Introduction
Methodology
Process modeling
Energy integration
Economic analysis
Capital cost
Operating cost
Profitability analysis
Process modeling and integration of the BOSR-HDO system
System definition
Model component of bio-oil
Process description
Steam reforming of the aqueous fraction of bio-oil
HDO of the lignin fraction of bio-oil
Sensitivity analysis
Sensitivity analysis of steam reforming reaction
Sensitivity analysis of water-gas shift reaction
Heat integration and utility system design
Data extraction, screening, and classification
Composite curve analysis
CHP network design
Heat exchanger network design
Heat and power balance
Economic analysis
Capital cost
Operating cost
Value of products
Profitability analysis
Discussion
Conclusions
Acknowledgments
References
Risk and resilience analysis of integrated biorefineries using input-output modeling
Introduction
Problem statement
Methodology
Baseline state of the IBR
Criticality analysis
Resilience analysis
Case study: IBR
Conclusions
Acknowledgment
References
Advanced integrated systems for hydrogen production and storage from low-rank fuels
Introduction
Hydrogen: Properties and characteristics
Hydrogen production from low-rank fuels
Low-rank coal
Biomass conversion into hydrogen
Black liquor
Empty fruit bunch
Hydrogen storage
Potential hydrogen storages and their comparison
Liquid hydrogen
Toluene-MCH as LOHC
Ammonia
Cost analysis
Power-to-gas
Conclusion
References
Further reading
Part 2: Sustainability design and optimization
Energy system optimization under uncertainties: A comprehensive review
Introduction
Literature review
Energy system models
Energy system optimization models
Energy system optimization methodologies under uncertainties
Stochastic programming
Robust optimization
Hybrid model
Summary
Optimization of different energy systems under uncertainty
Distributed energy systems
Electric power systems
Conclusion
References
Sustainable utilization of low-grade heat: Modeling and case study
Introduction
Absorption refrigeration cycle
Model of the absorption refrigeration cycle
System performance under different heat source temperatures with a fixed evaporator temperature of 5C
Optimal heat source temperatures at different evaporator temperatures
Organic Rankine cycles and Kalina cycles
Simulation models of ORC and KC
Evaluation parameters for energy performance
Power recovery from three kinds of waste heat
Classification of waste heat
Power recovery for straight waste heat
Power recovery for convex waste heat
Power recovery for concave waste heat
Conclusions
References
Sustainable design of industrial complex: Industrial area-wide layout optimization
Introduction
Methodology
Artificial method
Material flow pipelines
Steam pipelines
All pipelines
Programming method
Problem statement
Assumption
Given
Determine
Mathematical formulation
Pipe cost
Pressure loss cost
Heat loss cost
Cost of explosion damage
Damage cost of toxic gas leak
Optimization algorithms
Case studies
Artificial method
Case description
Basic data acquisition
Plant Data
Material flow data
Steam flow data
Programming method
Case description
Basic data acquisition
Plant data
Material flow data
Steam flow data
Risk model data
Results and discussion
Artificial method
Programming method
Conclusion
References
Further reading
Sustainable design of cooling water system
Introduction
Problem statement
Model formulation
Series-parallel superstructure model for cooler network
Pump network formulation
Minimum pressure head requirement of coolers
Multiloop pump network
Main-auxiliary pump network
Pipe network formulation
Cooling tower formulation
Air cooler formulation
Objective function and solution technique
Case studies: Without air cooler
Single-plant scenario
Multiplants scenario
Case study: With air coolers
Cooling water system in Xian
Cooling water system in Nanchang
Conclusion
References
Further reading
Pinch analysis for sustainable process design and integration
Introduction
Pinch analysis
Example 10.1 Design of heat exchanger network
Water pinch analysis
Example 10.2 Design of water network based on water pinch analysis
Example 10.3 Design of water network based on water cascade analysis
Carbon emission pinch analysis
Example 10.4 CEPA for regional energy planning
Other applications of pinch analysis
Conclusions
References
Model-based synthesis and Monte Carlo simulation of biochar-based carbon management networks
Introduction
Methodology for assessing the robustness of BCMNs
Formal problem statement
Mathematical model formulation
Generation of near-optimal solutions via integer-cut constraints
Testing the robustness of BCMNs via MCS
Illustrative case study
Conclusion
Acknowledgment
References
Part 3: Sustainable manufacturing via hybridization and modularization
Frontiers of sustainable manufacturing: Hybridization and modularization
Introduction
Hybrid energy processes
Multifeed input structure
Multiproduct output structure
Modular chemical production processes
Conventional evaluation and optimization methods
Techno-economic evaluation
Model-based simulation and optimization
Sustainability assessment and optimization
Future challenges and opportunities
Hybrid energy processes
Modular chemical production processes
References
Hybrid processes for sustainable liquids production from lignite, natural gas, and biomass
Introduction
Lignite conversion and hybridization opportunity
Hybrid processes description
Life cycle multiindicator optimization
Primary exergy saving ratio
Primary total overnight cost saving ratio
Life cycle waste emissions avoidance ratio
Primary levelized cost saving ratio
Illustrative example
Conclusions
References
Hybrid processes for sustainable chemicals production from shale gas and ethanol
Introduction
Shale gas conversion and hybridization opportunity
Hybrid process description
Life cycle bi-objective optimization
General optimization framework
Energy integration model
LCO model
Illustrative example
Conclusions
References
Modular fuels/chemical production from shale gas
Introduction
Technologies for modular fuels/chemicals production from shale gas
Modular LNG production from shale gas
Shale gas liquefaction
Modular LNG production technologies
Modular liquids production from shale gas
Shale gas to methanol
Modular methanol production technologies
Shale gas to synthetic fuels via Fischer-Tropsch synthesis
Modular liquid fuels production technologies
Modeling, analysis, and optimization of modular fuels/chemicals production systems
Techno-economic analysis of modular fuels/chemicals production
Small-scale modular production versus large-scale production
Modular methanol production from shale gas
Modeling and optimization of modular production systems
Pros and cons of modular production
Pros of modular production
Cons of modular production
Discussion and future directions
Conclusion
Acknowledgment
References
Author Index
Subject Index
Back Cover

Citation preview

Towards Sustainable Chemical Processes

Towards Sustainable Chemical Processes

Applications of Sustainability Assessment and Analysis, Design and Optimization, and Hybridization and Modularization Edited by Jingzheng Ren The Hong Kong Polytechnic University, Department of Industrial and Systems Engineering, Center for Sustainability Science, Hong Kong, China

Yufei Wang Associate professor, State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, China

Chang He School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou, China

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-818376-2 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Anita Koch Editorial Project Manager: Emerald Li Production Project Manager: Sruthi Satheesh Cover Designer: Matthew Limbert Typeset by SPi Global, India

Contributors Muhammad W. Ajiwibowo Universitas Indonesia, Depok, Indonesia Houssein Al Moussawi Lebanese International University, LIU, Beirut, Lebanon Muhammad Aziz Institute of Industrial Science, The University of Tokyo, Tokyo, Japan Beatriz A. Belmonte Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines Michael Francis D. Benjamin Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines Mauricio Camargo Equipe de Recherche des Processus Innovatifs, ERPI-ENSGSI,  de Lorraine, Nancy, France Universite Arif Darmawan Tokyo Institute of Technology, Tokyo, Japan Farouk Fardoun Faculty of Technology, Department GIM, Lebanese University, Saida, Lebanon Xiao Feng School of Chemical Engineering & Technology, Xi’an Jiaotong University, Xi’an, People’s Republic of China Chang He School of Materials Science and Engineering, Guangdong Engineering Centre for Petrochemical Energy Conservation, Sun Yat-sen University, Guangzhou, People’s Republic of China Xiaoping Jia School of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao, China Zhiwei Li School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa Bo Liu State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, People’s Republic of China Hasna Louahlia Normandie Univ, UNICAN, LUSAC, Saint Lo, France Jiaze Ma State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, People’s Republic of China

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Contributors

Lei Ma School of Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China Elias Martinez-Hernandez Biomass Conversion Department, The Mexican Institute of Petroleum, Mexico City, Mexico sar Narva´ez Rinco´n Departamento de Ingenierı´a Quı´mica y Ambiental, Grupo Paulo Ce de Procesos Quı´micos y Bioquı´micos, Facultad de Ingenierı´a, Universidad Nacional de Colombia Sede Bogota´, Bogota´, Colombia Kok Siew Ng Department of Engineering Science, University of Oxford, Oxford, United Kingdom ´ lvaro Orjuela Departamento de Ingenierı´a Quı´mica y Ambiental, Grupo de Procesos A Quı´micos y Bioquı´micos, Facultad de Ingenierı´a, Universidad Nacional de Colombia Sede Bogota´, Bogota´, Colombia Luis F. Razon Chemical Engineering Department, De La Salle University, Manila, Philippines Jingzheng Ren Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, People’s Republic of China Juliana Serna Departamento de Ingenierı´a Quı´mica y Ambiental, Grupo de Procesos Quı´micos y Bioquı´micos, Facultad de Ingenierı´a, Universidad Nacional de Colombia Sede Bogota´, Bogota´, Colombia Raymond R. Tan Chemical Engineering Department, De La Salle University, Manila, Philippines Ruiqi Wang State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, People’s Republic of China Yufei Wang State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, People’s Republic of China Yan Wu State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, People’s Republic of China Minbo Yang School of Chemical Engineering & Technology, Xi’an Jiaotong University, Xi’an, People’s Republic of China

1 Sustainability assessment for chemical product and process design during early design stages ´ lvaro Orjuelaa,
sar Narva´ez Rinco´na, Juliana Sernaa, A Paulo Ce Mauricio Camargob DEPARTAMENTO DE INGENIERI´ A QUI´ MI CA Y A MBIE NT A L, GR UP O DE PR OCE SO S QU I´ MICOS Y BI OQ U ´I MICOS, FACULTAD DE INGENI ERI´ A, UNIVERSIDAD NACIONAL D E COLOMBIA SEDE BOGOTA´ , BO GO TA´ , CO LOMB IA b EQ U IP E D E RECHE RCHE DE S P ROC ES S U S I N N OV AT IF S ,
D E LO R R A I N E , NA N C Y , F R A NC E ERP I- E NS GS I, UNI VE RSI T E

a

1 Introduction Nowadays, the importance of reaching sustainable production and consumption is broadly and globally recognized. This is reflected in the United Nations’ statement on the Sustainable Development Goals (SDGs) for the year 2030 (2015a). In view of this, the World Business Council for Sustainable Development (WBCSD) has published a specific roadmap to help the chemical sector undertake actions to contribute to the SDG agenda (WBCSD, 2018). Many chemical industry organizations have also manifested their active support for fulfilling the SDGs. For example, the International Council of Chemical Associations (ICCA) has presented a report of efforts being made in their sector to achieve the SDGs (ICCA, 2017). Also, the European Chemical Industry Council (CEFIC) has published a sustainability report within its new framework, ChemistryCan, created to boost cooperation between CEFIC members toward sustainable development (CEFIC, 2017). Similarly, the American Chemical Society (ACS) has published a policy statement focused on sustainability, recommending government actions that can promote the SDGs (ACS, 2017). Additionally, several big chemical industries, such as Dow, BASF, and Akzo Nobel, have aligned their goals to this purpose (Axon and James, 2017). In spite of the awareness that actions are needed for sustainable development, there is still a long way to go to achieve the SDGs (United Nations, 2018). Considering the chemical sector, a major challenge to incorporating a sustainability approach into product/process design is its inherent complexity. Sustainable design involves: •

A multidimensional perspective, because by definition it incorporates at least three dimensions: economic, environmental, and social. These dimensions are usually referred to as the triple bottom line (TBL) (Hacking and Guthrie, 2008; Govindan et al.,

Towards Sustainable Chemical Processes. https://doi.org/10.1016/B978-0-12-818376-2.00001-6 © 2020 Elsevier Inc. All rights reserved.

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Towards Sustainable Chemical Processes

2013). Moreover, in recent approaches, additional dimensions, including political and technological, are also taken into account (Bautista et al., 2016). A multiscale view, because decisions made at the molecular, phenomenological, process, and supplier chain scales may have effects at the ecosystem and planet scales (Martinez-Hernandez, 2017; Hanes and Bakshi, 2015). A multiactor problem, because decisions must be made considering different stakeholders with diverse and even contradictory interests that may be affected or benefited by the product/process to be devised (Azapagic et al., 2016). Stakeholders to be considered include investors, organizations, governments, individuals, communities, and workers.

The complex nature of sustainability means more effort is required for its implementation. Nevertheless, it is also an opportunity for broadening the scope of chemical engineering design beyond the chemical plant. Sustainability can be evaluated using indicators related to each dimension, and the appropriate indicators must be selected according to the design stage under evaluation and the available information. A sustainable design must consider simultaneously all TBL dimensions throughout the entire design process, i.e., from the early design stages when the product is devised, components are selected and/or a chemical route is defined, all the way to the production stage when the plant is in operation and administrative and manufacturing decisions are made. Thus, the implementation of sustainability assessment can be more demanding during the early design stages when information is scarce and the impact of decisions is high and difficult to correct at later stages (Serna et al., 2016; Argoti et al., 2019). In this context, appropriate assessment methods for each dimension and decision-making tools involving multiple objectives are needed. Several sustainability assessment approaches applicable to early design stages have been proposed. Examples are the indicator-based methodology proposed by Srinivasan and Nhan (2008), the environmental hazard index (EHI) (Cave and Edwards, 1997), and the waste reduction (WAR) algorithm (Young et al., 2000), among many others. The social dimension is difficult to assess at the early design stages due to a lack of models and information (Argoti et al., 2019). Some methods apply a surrogate approach using safety and health indicators. Examples of safety assessment approaches are the inherent safety index (ISI and ISI2) (Adu et al., 2008) and the prototype inherent safety index (PIIS) (Edwards and Lawrence, 1993). Examples of occupational health indicators are the Inherent Occupational Health Index (Hassim and Edwards, 2006) and the Globally Harmonized System of Classification and Labelling of Chemicals (GHS). The latter is global standard for hazardous material categorization that has been widely implemented worldwide (United Nations, 2015b). It can also be used to assess the environmental and health hazards of substances (Suarez and Narva´ez, 2017). However, more investigation is required to generate a more comprehensive assessment of the social dimension at early and advanced stages of the product and process design.

Chapter 1 • Sustainability assessment for chemical product

5

For the specific case of sustainability assessments during product design, there are different methodologies in which sustainability indicators have been incorporated to a greater or lesser degree. For example, in addition to cost, Conte et al. (2011) and Mattei et al. (2013) considered flammability and toxicity as criteria for selecting safe and economic product components. Heintz et al. (2014) proposed a framework for substituting possible toxic ingredients with more sustainable options, considering their lethal dose (LD50) and bioconcentration factor (BCF). Similarly, a framework integrating computer-aided molecular design and a complete evaluation of occupational health and safety criteria was presented by Ten et al. (2016). In that proposal, seven indices related to the safety and health characteristics of chemical molecules were selected and implemented when generating molecular products. These indices are flammability and explosiveness for safety; and viscosity, material phase, volatility, and exposure limit for occupational health. In addition to the selection of suitable indicators, two aspects must be considered when carrying out an integrated sustainability assessment: (1) to normalize the indicators to make them comparable, (2) to present the indicators simultaneously so that decisionmakers can identify eventual compromises between alternatives (Serna et al., 2016). When incorporating multiple dimensions into the assessments, all stakeholder preferences have to be included. This can be done by implementing a multicriteria decision analysis (MCDA) (Govindan et al., 2013; Serna et al., 2016; Azapagic et al., 2016), and multiactor decision-making (MADM) methods (Ren et al., 2013). According to the problem and the availability of models and information, design alternatives can be identified by experience, literature search, or by modeling and optimization. In the latter case, when complete problem modeling is possible, optimization can be done before or after incorporating assessments with MCDA or MADM (Azapagic et al., 2016). In the first option, the Pareto front is identified by applying a multiobjective optimization method, and the assessments are integrated subsequently, and the best alternative is defined. In the second option, the criteria are first integrated into a single objective, and subsequently an easier mono-objective optimization is performed. The advantage of the first option is a clear identification of all the best alternatives (Pareto front) and possible trade-offs. Taking the above into account, this chapter presents a decision-making methodology applicable to the sustainable design of chemical products and processes at early design stages. It is based on a previously presented methodology that uses indicators to assess the process design alternatives and MCDA methods to integrate the assessment (Serna et al., 2016). The contribution of this chapter is that it extends the application of the methodology to product design and presents new indicators based on the GHS that require little information and can be easily applied at early design stages. Additionally, the herepresented methodology uses the MCDA methods such as the analytic hierarchy process (AHP) and the preference ranking organization method for enrichment of evaluations (PROMETHEE). The latter is a noncompensatory decision method that enables a clear comparison of alternatives and the identification of synergies and compromises. The

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Towards Sustainable Chemical Processes

following section explains the steps of decision methodology, which is exemplified in Section 3 through two case studies: the selection of a chemical route to produce glyceryl monostearate, and the selection of a formulation for cosmetic application.

2 Framework for the sustainability assessment of chemical products and processes at early design stages Fig. 1.1 presents the steps involved in the multicriteria analyzes-based framework proposed in this chapter. The steps are based on the previously presented methodology (Serna et al., 2016), but in this proposal a new set of indicators applicable to both products and processes is presented, and a different MCDA method is used for the integration of assessments. The framework has four steps: 1. Problem definition. This includes the substeps of the objective statement, knowledge of the product, identification of alternatives, and information gathering. 2. Assessment of alternatives. This comprises the selection of appropriate indicators applicable at the early stages of product and process design, calculation of indicators for each alternative, and normalization of indicators. 3. Integration of assessments. This step involves the calculation of weights for the indicators, calculation of a global sustainability index, and the exploration of the relationship between indicators through a sensitivity analysis. In this study, for the

Problem statement

Problem definition

1.Objective statement 2.Knowledge of the product 3.Identification of alternatives 4.Information gathering

Assessment of alternatives

1. Selection of indicators 2. Calculation of indicators 3. Normalization of indicators

Integration of assessments

1. Calculation of weights 2. Ranking of alternatives 3. Sensitivity analysis

1

2

3

Decision FIG. 1.1 Multicriteria analyses-based framework to assess product/process alternatives under sustainability criteria at early design stages. Adapted from Serna, J., Dı´az, E., Narva´ez, P., Camargo, M., Ga´lvez, D., Orjuela, A´., 2016. Multicriteria decision analysis for the selection of sustainable chemical process routes during early design stages. Chem. Eng. Res. Des. https://doi.org/10.1016/j.cherd.2016.07.001.

Chapter 1 • Sustainability assessment for chemical product

7

assessment of alternatives, indicators applicable to early design stages of product and process design are presented. For the integration of assessments, the MCDA method is used. 4. Final decision

2.1 Problem definition In this step, the scope of the design is defined and the product is thoroughly characterized (properties, specifications, prices, legal framework, etc.). If the product is not a formulation but the result of a reaction and a separation process, it is necessary to identify and study the possible chemical process routes and raw materials for its generation. If the product is a formulation, it is necessary to gather information about the possible ingredients to be used. In both cases, this information includes economic, safety, occupational health, and environmental properties of the substances, and operating conditions of the processes. Among others, sources for this information include: • • • •

• • • •

scientific papers safety data sheet of ingredients suppliers’ documentation reports from governmental and intergovernmental agencies and organizations (e.g., European Chemicals Agency (ECHA), the Organization for Economic Co-operation and Development (OECD), and the U.S. Environmental Protection Agency (EPA)) scientific databases (e.g., PubChem, eChemPortal) ECHA dossier of chemicals group contribution methods to calculate some safety and occupational health indices for molecules (e.g., Ten et al., 2016) software that incorporates property estimation tools (e.g., EPI Suite from EPA)

2.2 Assessment of alternatives During this stage, the performance of each alternative is assessed within the TBL dimensions through suitable indicators. Because the methodology is applicable to early design stages, when information on the social dimension is very scarce at this point, this dimension was indirectly assessed via health and safety indicators, as shown in Fig. 1.2.

2.2.1 Selection of indicators A set of indicators is used to calculate the sustainability performance of different chemical process routes and formulations at the early design stages. Most of them are defined based on the GHS (United Nations, 2015b). The indicators can be calculated using the properties of the substances, which are normalized using their definition according to GHS Hazard statements (H statements). An H statement is assigned to a substance to indicate a hazard class (e.g., acute toxicity, eye irritation, flammability, etc.) and a hazard category (i.e., division of a hazard class that specifies its severity) (United Nations, 2015b). Alternatively, indicators can be defined directly from the H statements, which can be found in the safety

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Towards Sustainable Chemical Processes

Assessment of the product/process alternatives Economic dimension

Environmental dimension

Economic indicator

Impact on water

Added value (VA)

Hazard to aquatic life (HtoAL)

Hazard to the ozone layer (HtoOL)

Hazard to aquatic life long term (HtoAL_L)

Global warming potential (GWP)

Impact on air

Photochemical oxidation potential (PCOP) Ozone depletion potential (ODP) Acidification potential (AP)

Social dimension

Waste

Bioconcentration factor (BCF)

Physical hazards Flammability (F) Explosiveness (E)

Renewable sources (RS)

Reactivity (R) Oxidiser (O)

Biodegradability (BD)

Self-heating (SH) Flammability by mixture with water (RFG) Corrosion to metals (CtoM) Gases under pressure (GUP) Temperature (T) Pressure (P) Heat of reaction (H) Complexity of separation (CSP)

Health hazards Acute toxicity (AT) Eye irritation (EI) Skin irritation (SI) Respiratory irritation (RI) Danger if enters airways (DA) Carcinogenicity (CAR) Mutagenicity (MUT) Damage to fertility (Dfer) Damage to organs (DtoO) Damage to organs long term (DtoO_L)

FIG. 1.2 Sustainability indicators to assess product/process alternatives at early design stages.

information on substances. This approach was used because the GHS information was constructed on current scientific principles, is globally accepted, and is available for almost any commercial substance. To complete the assessment, additional indicators outside the GHS are proposed. Most of them have been previously included in the WAR algorithm (Young and Cabezas, 1999) and the inherent safety index (ISI) approach (Heikkil, 1999). Some additional indicators are also proposed, and the complete list is presented in Table 1.1. It is not always necessary to use all the listed indicators; some of them can be disregarded or additional ones can be included. Decision-makers have to select the most suitable indicators according to product characteristics, the specific context of selection problem, and the availability of information.

2.2.2 Calculation of indicators Table 1.1 presents a list of indicators applicable to sustainability assessment at early design stages. To calculate an indicator for a chemical route, this approach uses the average value of all substances involved in the reaction and gives the same relative importance to all of them (Serna et al., 2016). This is done to avoid the underestimation of an alternative when it has a critical component in small quantity that may disappear as the reaction advances (Serna et al., 2016).

Table 1.1 Economic dimension

Indicators for sustainability assessment of product/process alternatives at early design stages. Indicator

Explanation

Normalized indicator

Sources

Added value (VAT )

The added value VA corresponds to the difference between product value and raw material costs. It can be normalized by dividing this difference by the sale price of products, obtaining the dimensionless normalized added value (VA). The total normalized added value (VAT ) corresponds to 1 minus the normalized added value (VA). This is calculated to make the indicator comparable with other indicators of the assessment. A high VA and the corresponding lower value of VAT means a better economic performance

• VAp for formulations   NI P VAp ¼ mp PP p  xi  Pci

The indicator was used in Serna et al. (2016). Equations were taken from Carvalho et al. (2008) and modified to suit the approach proposed in this study

(1.1)

1

• VAp for products from a chemical process route   NI  P jνi jMi Pci VAp ¼ mp PP p  CF p (1.2) νp jMp j 1 jvp jMp CF p ¼ P NP jvp jMp

(1.3)

1

• Normalization VA ¼

NP P

VAp

(1.4)

1

VA ¼ P NP

VA

(1.5)

mp PP p

9 > If VA < 1 > = the alternative is not economically viable > > > > ; : else VAT ¼ 1  VA 8 > >
> < 0:75 if H ¼ 401toxic to aquatic life (1.8) I i, HtoAL or Ip, HtoAL ¼ 0:05 if H ¼ 402harmful to aquatic life > > : 0 if H ¼ no statement

H statements information from United Nations (2015b)

• The normalized indicator value of HtoAL for a chemical route (Ir , HtoAL ) is calculated as the average of the indicators of the components, as follows: NI P

Ir, α ¼ • H400—hazard category 1, if 96 h lethal concentration (LC50) (fish) and/or 48 h half maximal effective concentration (EC50) (crustacean) and/or 72 or 96 h ErC50 (algae or aquatic plant) are 1 mg/L • H401—hazard category 2, if any of the previously considered concentrations are >1 and  10 mg/L • H402—hazard category 3, if any of the previously considered concentrations are >10 and 100 mg/L • H statement is not assigned, if any of the considered properties are >100 mg/L In the case of a mixture, it is possible to find the H statement as follows: • H400—hazard category 1, if 25% of the components are classified as H400 • H401—hazard category 2, if the sum of the concentrations of its components in category H401 plus 10 times the concentration of its components in category H400 is 25%. • H402—hazard category 3, if the sum of the concentrations of its components in category H402 plus 10 times the concentration of its components in category H401 plus 100 times the concentration of its components in category H400 is 25%

Ii , α

i¼1

NI

(1.9)

Here, NI is the total number of substances in the chemical route, Ii, α is the indicator of substance i associated with the indicator category α, I r , α is the normalized indicator of the chemical route r associated with the indicator category α. This normalization method is valid for the calculation of chemical route indicators defined according to United Nations (2015b). • Normalized indicator value of HtoAL for a formulation is defined in two steps: (1) mixture rules are used to classify the formulation in a category; (2) the normalized indicator value for the formulation is assigned following Eq. (1.8) For other indicators, if mixture rules or better property mixture prediction models are not available, the indicator is calculated as an approximation at early design stages with a linear relation as follows: I p, α ¼

NI P i¼1

x i  Ii , α

(1.10)

where NI is the total number of substances of a product, Ii, α is the indicator of substance i associated with the category α indicator, and Ip, α is the normalized impact of product p associated with impact category α

Continued

Table 1.1

Indicators for sustainability assessment of product/process alternatives at early design stages—cont’d Indicator

Explanation

Normalized indicator

Sources

• The normalized indicator value of HtoAL_L for pure substances (I i, HtoAL_L ) is defined based on H statements 410–413, as follows: 8 1 if H ¼ 410very toxic to aquatic life  long lasting effects > > > > < 0:75 if H ¼ 411toxic to aquatic life  long lasting effects (1.12) Ii , HtoAL_L or Ip, HtoAL_L ¼ 0:5 if H ¼ 412harmful to aquatic life  long lasting effects > > 0:25 if H ¼ 413may cause long  lasting harmful effects > > : 0 if H ¼ No statement

H statements information from United Nations (2015b)

When toxicity information for some of the components of a mixture is unknown, and the H statements of the rest are known, it is possible to approximate HtoAL of the mixture assigning a hazard category following these steps: (1) to find the toxicity of the portion of ingredients with toxicity information using Eq. (1.7); (2) to classify that portion of the mixture in an H category using the rules for substances given at the beginning of this section; (3) to find the H category of the entire mixture using the previously presented rules for Pmixture classification. P xi xi (1.7) n LðEÞC50 i LðEÞC50 m ¼

Hazard to aquatic life—long-lasting effect (HtoAL_L)

Here xi is the concentration of component i in the mixture, L(E)C50i is the lethal concentration or half maximal effective concentration of the component, n is the number of components, and L(E)C50m is the lethal concentration or half maximal effective concentration of the mixture This hazard indicates that the substance can cause damage to aquatic organisms in a long-term exposure, and it is represented with H statements 410–413. H statements related to HtoAL_L can be found directly in the MSDS of a substance. Alternatively, if toxicity data on aquatic life for the long term are available, it is also possible to identify the H statement of a substance as follows: • H410—hazard category 1, if it is nonrapidly biodegradable and the NOEC (no observed effect concentration) or ECx (concentration that causes a response that is x% of the maximum) (fish) and/or NOEC or ECx (crustacean) and/or NOEC or ECx (algae or aquatic plant) is 0.1 mg/L • H411—hazard category 2, if it is non-rapidly biodegradable and the NOEC or ECx (fish) and/or NOEC or ECx (crustacean) and/or NOEC or ECx (algae or aquatic plant) is >0.1 and 1 mg/L • H410—hazard category 1, if it is rapidly biodegradable and the NOEC or ECx (fish) and/or NOEC or ECx (crustacean) and/or NOEC or ECx (algae or aquatic plant) is 0.01 mg/L • H411—hazard category 2, if it is rapidly biodegradable and the NOEC or ECx (fish) and/or NOEC or ECx (crustacean) and/or NOEC or ECx (algae or aquatic plant) is >0.01 and 0.1 mg/L • H412—hazard category 3, if it is rapidly biodegradable and the NOEC or ECx (fish) and/or NOEC or ECx (crustacean) and/or NOEC or ECx (algae or aquatic plant) is >0.1 and 1 mg/L When information about chronic toxicity such as NOEC or ECx is not available, it is possible to classify a substance following the rules: • H410—hazard category 1, if its 96 h LC50 (fish) and/or 48 h EC50 (crustacean) and/or 72 and/or 96 h ErC50 (concentration at which a 50% inhibition of growth rate is observed) (algae or aquatic plant) is 1 mg/L, and it is not rapidly biodegradable and/or BCF is >500 and/or KO/W > 4 • H411—hazard category 2, if its 96 h LC50 (fish) and/or 48 h EC50 (crustacean) and/or 72 and/or 96 h ErC50 (algae or aquatic plant) is >1 and 10 mg/L, and the substance is not rapidly biodegradable and/or BCF is >500 and/or KO/W > 4 • H412—hazard category 3, if the 96 h LC50 (fish) and/or 48 h EC50 (crustacean) and/or 72 and/or 96 h ErC50 (algae or aquatic plant) is >10 and 100 mg/L, and the substance is not rapidly biodegradable and/or BCF is >500 and/or KO/W > 4 • H413—hazard category 4, if the substance is poorly soluble and if no acute toxicity is reported, it is not rapidly biodegradable and KO/W > 4 H statement of a mixture is assigned as follows: • H410—category 1, if it contains 25% of components classified as H410 • H411—category 2, if the sum of the concentrations of its components in category H411, plus 10 times the concentration of its components in category H410 is 25%

• Normalized indicator value of HtoAL_L for a chemical route (Ir, HtoAL_L ), is calculated with Eq. (1.9) • Normalized indicator value of HtoAL_L for a formulated product (Ip, HtoAL_L ) is defined in two steps: (1) mixture rules are used to classify the formulation in an H category; (2) the normalized indicator value for the formulation is assigned following Eq. (1.12). For HtoAL_L, mixture rules are presented in the explanation column

• H412—category 3, if the sum of the concentrations of its components in category H412, plus 10 times the concentration of its components in category H411, plus 100 times the concentration of its components in category H410 is 25% • H413—category 4, if the sum of the concentrations of its components in categories H413 plus H412 plus 411 plus 410 is 25% When chronic toxicity information for some of the components of a mixture is known and the H statements of the rest are known, it is possible to approximate HtoAL_L of a mixture, assigning it a hazard category following the next steps: (1) to find the chronic toxicity of the portion of ingredients with toxicity information using Eq. (1.11); P P P xi P xi + xj xj (1.11) n NOIC i + n 0:1∗NOIC j EQNOEC m ¼ where xi is the concentration of component i in the rapidly degradable ingredients, xj is the concentration of component j in the non-rapidly degradable ingredients, NOECi is the chronic toxicity of component i in the rapidly degradable ingredients, NOECj is the chronic toxicity of component j in the non-rapidly degradable ingredients, n is the number of components, EQNOECm is the chronic toxicity of the mixture; (2) to classify the portion of the mixture in H categories according to chronic toxicity using the rules for substance classification given at the beginning of this section; (3) to find the H category of the entire mixture using the rules of mixture classification previously presented Hazard to the ozone layer (HtoOL)

ODP is defined only for those substances that stay in the atmosphere long enough to reach the stratosphere and contain a chlorine or bromine atom (Young and Cabezas, 1999). Any substance listed in the Montreal Protocol, which means it has an ODP, or any mixture containing >0.1% of a substance in the Montreal Protocol list, has an H statement 420

• The normalized indicator value of HtoOL for pure substances (I i, HtoOL ) or product formulations (Ip, HtoOL ) is defined based on Eq. (1.13): 8 < 1 if H ¼ H420harms public health and the environment by destroying ozone in the upper atmosphere (1.13) I i, HtoOL or Ip, HtoOL ¼ : 0 if H ¼ No statement

H statements information from United Nations (2015b)

• Normalized indicator value of HtoOL for a chemical route (Ir , HtoOL ), is calculated with Eq. (1.9) Global warming potential (GWP)

By definition, this impact is determined by comparing “the extent to which a unit mass of a chemical absorbs infrared radiation over its atmospheric lifetime to the extent that CO2 absorbs infrared radiation over its respective lifetimes” (Young and Cabezas, 1999)

• Normalized indicator value of GWP for a chemical route (Ir , GWP ) is calculated with Eq. (1.14) and for a formulated product (Ip, GWP ) with Eq. (1.15): NI P Ir, α ¼

Potential i , α

i¼1

(1.14)

ðhPotentialr iα + 2σα Þ∗NI NI P

I p, α ¼  NI P

xi Potential i , α

i¼1



(1.15)

xi Potential i, α + 2σ α, wt:

Young and Cabezas (1999); normalization method from Srinivasan and Nhan (2008); the indicator was also used in Serna et al. (2016)

i¼1

Here, NI is the total number of substances, Potentiali,α is the potential environmental impact of component i associated with impact category α, hPotentialriα is the average of impacts of components i in chemical process route r associated with impact category α, σ α is the standard deviation of potential environmental impact of substances associated with impact category α, xi is the composition of component i, σ α,wt. is the weighted standard deviation of potential environmental impact, Ir , α is the normalized potential impact of chemical process route r associated with impact category α, and I p, α is the normalized impact of the formulation Photochemical oxidation potential (PCOP)

“This impact category is determined by comparing the rate at which a unit mass of chemical reacts with a hydroxyl radical (OH) to the rate at which a unit mass of ethylene reacts with OH” (Young and Cabezas, 1999)

• Normalized indicator values of PCOP for a chemical route (Ir , PCOP ) and for a formulated product (I p, PCOP ) are calculated with Eqs. (1.14), (1.15), respectively

Young and Cabezas (1999); normalization method from Srinivasan and Nhan (2008); the indicator was also used in Serna et al. (2016)

Continued

Table 1.1

Indicators for sustainability assessment of product/process alternatives at early design stages—cont’d Indicator

Explanation

Normalized indicator

Sources

ODP

“This impact category is determined by comparing the rate at which a unit mass of chemical reacts with ozone to form molecular oxygen to the rate at which a unit mass of CFC-11 (trichlorofluoromethane) reacts with ozone to form molecular oxygen. For a chemical to have ODP it must exist in the atmosphere long enough to reach the stratosphere, it also must contain a chlorine or bromine atom” (Young and Cabezas, 1999)

• Normalized indicator values of ODP for a chemical route (I r , ODP ) and for a formulated product (Ip, ODP ) are calculated with Eqs. (1.14), (1.15), respectively

Acidification potential (AP)

“This impact category is determined by comparing the rate of release of H+ in the atmosphere as promoted by a chemical to the rate of release of H + in the atmosphere as promoted by SO2” (Young and Cabezas, 1999)

• Normalized indicator values of AP for a chemical route (I r , AP ) and for a formulated product (Ip, AP ) are calculated with Eqs. (1.14), (1.15), respectively

Bioconcentration factor (BCF)

“Bio-concentration means net result of uptake, transformation and elimination of a substance in an organism due to waterborne exposure. The potential for bioaccumulation would normally be determined by using the octanol/water partition coefficient, usually reported as a log Kow determined by OECD Test Guideline 107 or 117” (United Nations, 2015b) Raw materials are classified according to their source into the categories: (1) natural resources only physically modified, (2) enzymatically modified, (3) chemically modified, (4) from both natural and synthetic sources, or (5) completely synthetic origin (COSMOS-standard, 2018)

• Normalized indicator values of BCF for a chemical route (Ir , BCF ) and for a formulated product (Ip, BCF ) are calculated with Eqs. (1.14), (1.15), respectively

Young and Cabezas, (1999); normalization method from Srinivasan and Nhan (2008); the indicator was also used in Serna et al. (2016) Young and Cabezas (1999); normalization method from Srinivasan and Nhan (2008); the indicator was also used in Serna et al. (2016) Young and Cabezas (1999); normalization method from Srinivasan and Nhan (2008)

Renewable source (RS)

Biodegradability (BD)

Social dimension— physical hazard

Flammability (F)

“Degradation means the decomposition of organic molecules into smaller molecules and eventually to carbon dioxide, water and salts. Ready biodegradation can most easily be defined using the biodegradability tests (A–F) of OECD Test Guideline 301” (United Nations, 2015b). According to ASTM 5864 applicable to oils and lubricants, an oil is readily biodegradable if it degrades 60% within 28 days. It is inherently biodegradable if it degrades from 30% to 60% in 28 days, and it is nonbiodegradable if it does not degrade >30% in 28 days (Sharma and Biresaw, 2017) A liquid is flammable if it has a flash point 93°C. It is represented with H statements H224–227. A liquid is classified in the hazard categories as follows: • H224—flammability hazard category 1, if it has a flash point 60°C and 93°C For mixtures, the flash point can be calculated as presented in Gmehling and Rasmussen (1982), where the UNIFAC group contribution method is used. A solid is flammable if it is readily combustible or causes or contributes to fire. It is represented with H statement H228. It is possible to directly find an H statement related to this hazard in the MSDS of a substance. If this information is not available, a solid substance can be classified as follows:

• The normalized indicator value of RS for pure substances (Ii, RS ) or product formulations (Ip, RS ) is defined based on Eq. (1.16): 8 1 if Source ¼ synthetic origin > > < 0:66 if Source ¼ both natural and synthetic sources (1.16) I i, RS or I p, RS ¼ 0:33 if Source ¼ natural sources chemically modified > > : 0 if Source ¼ natural resources physically=enzimatic modified

Based on classification from COSMOS-standard (2018); the indicator was also used in Serna et al. (2016)

• Normalized indicator value of RS for a chemical route (Ir , RS ) is calculated with Eq. (1.9) • The normalized indicator value of BD for pure substances (Ii, BD ) or formulated products (Ip, BD ) is defined based on Eq. (1.18): 8 > > < 1 if it is non  biodegradable (1.18) I i, BD or I p, BD ¼ 0:5 if Biodegradability is inherent > > : 0 if Biodegradability is readily

Young and Cabezas, (1999); normalization from biodegradability definition from ASTM 5864.

The normalized indicator value of BD for a chemical route (Ir , BD ) is calculated with Eq. (1.9)

• The normalized indicator value of F for pure substances (Ii, F ) or mixtures (Ip, F ) is defined based on Eq. (1.19): 8 1 if H ¼ 220,222, 224,229extremely flammable > > < 0:75 if H ¼ 225very flammable (1.19) I i, F or Ip, F ¼ 0:5 if H ¼ 221, 223,226, 228flammable > > : 0 if No statement • The normalized indicator value of F for a chemical route (Ir , F ) is calculated with Eq. (1.9)

H statements information from United Nations (2015b)

• H228—flammability hazard category 1, if it is a solid or mixture other than metal powders that has a burning time 2.2 mm/s, and the fire cause cannot be stopped by wetted zones, or it is metal powder with a burning time 5 min • H228—flammability hazard category 2, if it is a solid or mixture other than metal powders that has a burning time 2.2 mm/s, and the fire cause is stopped by wetted zones in at least 4 min, or it is metal powder with a burning time >5 and 10 min A gas is flammable if it has a flammable concentration range with air at 20°C and 1 atm. It is represented with H statements H220 and 221. It is possible to directly find an H statement related to this hazard in substance safety documentation. If this information is not available, it is possible to classify it as follows: • H220—flammability hazard category 1, if at 20°C and 1 atm it has a flammable range with air 12% points, independent of the lower flammable limit, or it ignites in a mixture with air at a concentration 13% • H221—flammability hazard category 2, if it is a different gas from those classified in category 1, and has a flammability range with air at 20°C and 1 atm For gaseous mixtures, the criterion to define if it is flammable is shown in Eq. (1.17): n P Vi % (1.17) Tci > 1 i

Explosiveness (E)

where Vi% is the equivalent flammable gas content, Tci is the maximum concentration of a flammable gas in nitrogen at which the mixture is still not flammable with air. In mixtures with inert gas other than nitrogen, their concentration must be corrected to be included in the formula with a factor Ki. An aerosol is flammable if it contains any solid, liquid, or flammable gas. It is represented with H statements H222 and 223 Explosiveness can be assessed based on the upper explosive limit (UEL) and lower explosive limit (LEL) of each substance, as suggested in the Inherent Safety Index approach (Heikkil, 1999), also implemented in Srinivasan and Nhan (2008) and Ten et al. (2016). The calculation and classification criteria are shown in Eq. (1.20): Ii,E ¼ (UEL  LEL) vol% (1.20) where explosiveness is scored with • • • • •

0—nonexplosive 1 for Ii,E >0 and 20 2 for Ii,E >20 and 45 3 for Ii,E >45 and 70 4 for Ii,E >70 and 100

• The normalized indicator value of E for pure substances (I i, E ) or mixtures (I p, E ) is defined based on Eq. (1.23): 8 1 if Ii or p, E > 70 and  100 > > > > < 0:75 if Ii or p, E > 45 and  70 (1.23) I i, E or Ip, E ¼ 0:5 if Ii or p, E > 20 and  45 > > > > 0:25 if Ii or p, E > 0 and  20 : 0 if non  explosive

Heikkil (1999)

• The normalized indicator value of E for a chemical route (Ir , E ) is calculated with Eq. (1.9)

For mixtures, explosive limits can be approximated with Le Chatelier’s rule (Hristova and Tchaoushev, 2006), as shown in Eqs. (1.21), (1.22) LELmix ¼ P100xi

(1.21)

LELi UELmix ¼ P100xi

(1.22)

UELi Reactivity (R)

A self-reactive substance is a liquid or solid that is unstable and reacts strongly and exothermically even without the presence of oxygen. It is represented with H statements H200–205. The classification categories for this hazard are: • H240—type A substance, if it is a self-reactive substance or mixture that can detonate • H241—type B substance, if it is a self-reactive substance that is liable to undergo a thermal explosion within the package

• The normalized indicator value of R for pure substances (Ii, R ) or mixtures (I p, R ) is defined based on Eq. (1.24): 8 1 if H ¼ 240heating may cause an explosion > > < 0:66 if H ¼ 241heating may cause a fire or explosion (1.24) I i, R or Ip, R ¼ 0:33 if H ¼ 242heating may cause a fire > > : 0 if H ¼ No statement

H statements information from United Nations (2015b)

• The normalized indicator value of R for a chemical route (Ir , R ) is calculated with Eq. (1.9)

Continued

Table 1.1

Indicators for sustainability assessment of product/process alternatives at early design stages—cont’d Indicator

Explanation

Normalized indicator

Sources

• The normalized indicator value of O for pure substances (I i, O ) or mixtures (I p, O ) is defined based on Eq. (1.26): 8 1 if H ¼ 270may cause or intensify fire; oxidizer > > < 1 if H ¼ 271may cause fire or explosion; strong oxidizer (1.26) I i, O or I p, O ¼ 0:5 if H ¼ 272may intensify fire > > : 0 if H ¼ No statement

H statements information from United Nations (2015b)

• H242—type C substance, if it is a self-reactive substance that possesses explosive properties but cannot detonate or deflagrate as packaged • H242—type D substance, if it is a self-reactive substance that in laboratory testing detonates partially, does not deflagrate rapidly, and shows no violent effect when heated under confinement, or in laboratory testing does not detonate, deflagrates slowly, and shows no violent effect when heated under confinement, or in laboratory testing does not detonate or deflagrate and shows a medium effect when heated under confinement • H242—type E substance, if it is a self-reactive substance that in laboratory testing does not detonate or deflagrate and shows a low or no effect when heated under confinement • H242—type F substance, if it is a self-reactive substance that in laboratory testing does not detonate or deflagrate and shows a low or no effect when heated under confinement as well as low or no explosive power Oxidizer (O)

An oxidizer is a substance that contributes to or causes the combustion of other substance or material. It is represented with H statements H270–272. The classification categories for this hazard are: For gaseous substances and mixtures: • H270—category 1 is given to gases that have an oxidation power (OP) greater than that of air, i.e., substances with OP 23.5%. OG P xi Ci

OP ¼ P OG i

i

P

(1.25)

IG

xi +

Kk Bk

k

where xi is molar fraction of oxidizer gas i, Ci is coefficient of oxygen equivalency of the i-oxidizing gas in the mixture, Kk is coefficient of equivalency of the inner gas k compared to nitrogen, Bk is molar fraction of the k inert gas in the mixture, OG is total number of oxidizing gases, IG total number of inert gases For liquid substances or mixtures, the classification is based on test O.2 Part III subsection 34.4.2, as follows: • H271—category 1, if it ignites spontaneously in the test conditions (1:1 mixture by mass of the tested substance:cellulose), or it presents a mean pressure rise time lower than a reference comparison (1:1 mixture by mass of perchloric acid:cellulose) • H272—category 2, if in test conditions (1:1 mixture by mass of the tested substance:cellulose) it presents a mean pressure rise time less than or equal to a reference (40% aqueous sodium chlorate solution and cellulose) and has not been classified in category 1 • H272—category 3, if in test conditions (1:1 mixture by mass of the tested substance:cellulose) it presents a mean pressure rise time less than or equal to a reference (65% aqueous nitric acid and cellulose) and has not been classified in category 1 or 2 For solid substances or mixtures: • H271—category 1, if in test conditions (4:1 or 1:1 mixture by mass of the sample:cellulose) it presents a mean burning time less than a reference (3:2 by mass potassium bromate to cellulose) • H272—category 2, if in test conditions (4:1 or 1:1 mixture by mass of the sample:cellulose) it presents a mean burning time equal to or less than a reference (2:3 by mass potassium bromate to cellulose), and has not been classified in category 1

• The normalized indicator value of O for a chemical route (Ir , O ) is calculated with Eq. (1.9)

• H272—category 2, if in test conditions (4:1 or 1:1 mixture by mass of the sample:cellulose) it presents a mean burning time equal to or less than a reference (3:7 by mass potassium bromate to cellulose), and has not been classified in category 1 or 2 Self-heating (SH)

Release flammable gases when mixed with water (RFG)

Corrosive to metals (CtoM)

Pyrophoric substances or mixtures are liquids or solids that can ignite in 5 min in contact with air. They are identified with H statement 250—category 1. Self-heating substances or mixtures are liquids or solids that differ from pyrophoric substances as they are self-heating when in contact with air. They can also ignite if they are in large amounts and after long periods. They are classified into two categories: H251 for self-heating substances and H252 for self-heating substances when they are in large quantities Substances (mixtures, liquids, or solids) that release flammable gases or become flammable in contact with water. They are represented with H statements H260–261. The classification categories for this hazard are: • H260—category 1, substances which react strongly with water at ambient temperature and the gas produced ignites spontaneously or reacts readily with water so that the rate of production of flammable gas is 10 L/kg substance min • H261—category 2, substances which react readily with water so that the rate of production of flammable gas is 20 L/kg substance hour • H261—category 3, substances which react slowly with water so that the rate of production of flammable gas is 1 L/kg substance hour A substance is corrosive to metals when it can damage or destroy metals by a chemical reaction. It is represented with H statement 290. The classification for this hazard is: • H290—category 1, corrosion rate on either steel or aluminum is 6.25 mm/year at 55 °C

• The normalized indicator value of SH for pure substances (Ii, SH ) or mixtures (Ip, SH ) is defined based on Eq. (1.27): 8 1 if H ¼ 250catches fire spontaneously if exposed to air > > < 0:66 if H ¼ 251self  heating; may catch fire (1.27) I i, SH or Ip, SH ¼ 0:33 if H ¼ 252self  heating in large quantities; may catch fire > > : 0 if H ¼ No statement

H statements information from United Nations (2015b)

The normalized indicator value of SH for a chemical route (I r , SH ) is calculated with Eq. (1.9) • The normalized indicator value of RFG for pure substances (Ii, RFG ) or mixtures (Ip, RFG ) is defined based on Eq. (1.28): 8 1 if H ¼ 260in contact with water releases flammable gases > > < which may ignitespontaneously (1.28) Ii, RFG or Ip, RFG ¼ 0:5 if H ¼ 261in contact with water releases flammable gases > > : 0 if H ¼ No statement

H statements information from United Nations (2015b)

• The normalized indicator value of E for a chemical route (Ir , E ) is calculated with Eq. (1.9)

• The normalized indicator value of CtoM for pure substances (Ii, CtoM ) or mixtures (I p, CtoM ) is defined based on Eq. (1.29):  1 if H ¼ 290may be corrosive to metals (1.29) I i, CtoM or Ip, CtoM ¼ 0 if H ¼ No statement

H statements information from United Nations (2015b)

• The normalized indicator value of CtoM for a chemical route (Ir , R ) is calculated with Eq. (1.9) Gases under pressure (GUP)

Gases under 200 kPa at 20°C or liquefied gases. They are represented with H statements 280, 281. Categories for this hazard are: • H280—compressed gas is a gas under pressure that is completely gaseous at 50°C, including gases with a critical temperature 5°C • H280—liquefied gas is a gas which is partially liquid at temperature >50°C • H280—dissolved gas is a gas that is packed under pressure and dissolved in a liquid solvent • H281—refrigerated liquefied gas is a gas that is partially liquefied due to the low temperature when packaged

Process temperature (T)

Process temperature is classified according to the Inherent Safety Index (Heikkil, 1999), where the ranges are selected according to the danger to humans as follows: • 1, for process temperature 0°C (low temperatures may constitute a hazard because an effort is required to maintain the process in that condition; if there is a failure, substances may begin to vaporize; another hazard is the presence of solids due to the low temperature that may cause a blockage; construction materials and isolation strategies must be carefully considered) • 0, for process temperature >0°C and 70°C • 1, for process temperature >70°C and 150°C (hazard for thermal stress, mild temperature process) • 2, for process temperature >150°C and 300°C (high temperature process, beyond this temperature strength of carbon still decreases considerably) • 3, for process temperature >300°C and 600°C (very high temperature, at this temperature range, the strength of carbon still decreases considerably, so special materials are required) • 4, for process temperature >600°C (extremely high temperature)

• The normalized indicator value of GUP for pure substances (Ii, GUP ) or mixtures (Ip, GUP ) is defined based on Eq. (1.30): 8 1 if H ¼ 280contains gas under pressure, may explode if heated > > < 1 if H ¼ 281contains refrigerated gas, maycause cryogenic burn (1.30) I i, GUP or Ip, GUP ¼ or injury > > : 0 if H ¼ No statement

H statements information from United Nations (2015b)

The normalized indicator value of GUP for a chemical route (Ir , GUP ) is calculated with Eq. (1.9)

• The normalized indicator value of T for the assessment of chemical routes or product production process (IT ) is defined based on Eq. (1.31): 8 1 if Process Temperature > 600°C > > > > 0:75 if Process Temperature > 300°C and  600°C > > < 0:5 if Process Temperature > 150°C and  300°C (1.31) IT ¼ 0:25 if Process Temperature > 70°C and  150°C > > > > > 0:25 if Process Temperature  0°C > : 0 if Process Temperature > 0°C and  70°C

From the Inherent Safety Index (Heikkil, 1999)

Continued

Table 1.1

Indicators for sustainability assessment of product/process alternatives at early design stages—cont’d Indicator

Explanation

Normalized indicator

Sources

Process pressure (P)

Process pressure is classified according to the inherent safety index (Heikkil, 1999), where the ranges are selected according to the danger to humans as follows:

• The normalized indicator value of P for the assessment of chemical routes or product processes (IP ) is defined based on Eq. (1.32): 8 1 if Process Pressure > 200 bar > > > > 0:75 if Process Pressure > 50 and  200 bar > > < 0:5 if Process Pressure > 25 and  50 bar (1.32) IP ¼ 0:25 if Process Pressure > 5 and  25 bar > > > > 0:25 if Process Pressure  0:5 bar > > : 0 if Process Pressure > 0:5 and  5 bar

From the Inherent Safety Index (Heikkil, 1999)

• The normalized indicator value of H for chemical routes (I r , H ) is defined based on Eq. (1.33): 8 1 if |Heat of reaction|  3000 J=g > > > > < 0:75 if |Heat of reaction|  1200 < 3000 J=g I r , H ¼ 0:5 if |Heat of reaction|  600 and < 1200 J=g > > 0:25 if |Heat of reaction|  200 and < 600 J=g > > : 0 if |Heat of reaction| < 200 J=g

From the Inherent Safety Index (Heikkil, 1999)

• 1, for process pressure between 0 and 0.5 bar (low pressure) • 0, for process pressure between 0.5 and 5 bar • 1, for process pressure between 5 and 25 bar (mild pressured process) • 2, for process pressure between 25 and 50 bar (high pressure) • 3, for process pressure between 50 and 200 bar (very high pressure) • 4, for process pressure >200 bar (extremely high pressure) Heat of reaction (H) (when applicable)

Complexity in separation process (CSP)

Social dimension— health hazard

Acute toxicity (AT)

Heat of reaction is classified according to the Inherent Safety Index (Heikkil, 1999) as follows: • • • • •

0—thermally neutral, when heat of reaction is 200 J/g 1—mildly exothermic >200 and 600 and 1200 and 5 and 50 mg/kg body weight or dermal LD50 > 50 and 200 mg/kg body weight or inhalation gas 100 and 500, then acute toxicity corresponds to the H statement of fatal, hazard category 2 • If oral LD50 > 50 and 100 mg/kg body weight or dermal LD50 > 50 and 200 mg/kg body weight or inhalation gas 500 and 2500, then acute toxicity corresponds to the H statement of toxic, hazard category 3 • If oral LD50 > 300 and 2000 mg/kg body weight or dermal LD50 > 1000 and 2000 mg/kg body weight or inhalation gas 2500 and 20,000, then acute toxicity corresponds to the H statement of harmful, hazard category 4

I r , CSP ¼ fcð1  Yr Þ + ð1  fcÞð1  Xr Þ

From this study

(1.34)

where I r , CSP is the normalized indicator of CSP, Yr is the yield of chemical process route r, Xr is the conversion of chemical process route r, f is a factor that takes values from 0 to 1 to identify which of the following separation process may be more complex. It takes values >0.5 if the separation of the main products from other products is very difficult. It takes values > > > 0:75 if H ¼ 301,311, 331toxic if swallowed, skin, inhaled < (1.36) I i, AT or Ip, AT ¼ 0:5 if H ¼ 302, 312,332harmful if swallowed, skin, inhaled > > > 0:25 if H ¼ 303, 313,333may be harmful if swallowed, skin, inhaled > : 0 if H ¼ No statement • The normalized indicator value of AT for a chemical route (Ir , AT ) is calculated with Eq. (1.9)

H statements information from United Nations (2015b)

• If oral LD50 > 2000 and 5000 mg/Kg bodyweight or dermal LD50 > 2000 and 5000 mg/kg body weight, then acute toxicity corresponds to the H statement of may be harmful, hazard category 5 In the case of a mixture, it is possible to find the H statement based on information from ingredients as follows: (1) calculate acute toxicity estimate (ATE) from ingredients, (2) use Eq. (1.35) to calculate ATE of the mixture, (3) find the H statements based on the rules previously given for substances: P xi 100 (1.35) n ATEi ATEm ¼ where ATEm is the ATE of the mixture, xi is the composition in percentage of the compound I, and ATEi is the ATE of component i. ATE for ingredients is equivalent to LD50, LC50 when available, or it can be approximated when the H statement of the ingredient is known as follows: • When the ingredient is hazard category 1, ATE is 0.5 oral acute toxicity mg/kg bodyweight or 5 dermal acute toxicity mg/kg bodyweight or 10 ppmV acute toxicity inhalation • When the ingredient is hazard category 2, ATE is 5 oral acute toxicity mg/kg bodyweight or 50 dermal acute toxicity mg/kg bodyweight or 100 ppmV acute toxicity inhalation • When the ingredient is hazard category 3, ATE is 100 oral acute toxicity mg/kg bodyweight or 300 dermal acute toxicity mg/kg bodyweight or 700 ppmV acute toxicity inhalation • When the ingredient is hazard category 4, ATE is 500 oral acute toxicity mg/kg bodyweight or 1100 dermal acute toxicity mg/kg bodyweight or 4500 ppmV acute toxicity inhalation • When the ingredient is hazard category 5, ATE is 2500 oral acute toxicity mg/kg bodyweight or 2500 dermal acute toxicity mg/kg bodyweight Eye irritation (EI)

Serious eye damage is produced if after the application of a substance the effect is not fully reversible within 21 days. Eye irritation is a change in the eye after the application of a substance is fully reversible within 21 days. It is represented with H statements 318–320. If the H statement of a substance is unknown, the classification in these categories is based on existing data of effects on humans or animals, structure–activity relationship, in vitro or in vivo test. In the case of a mixture, it is possible to find the H statement based on information from ingredients as follows:

• The normalized indicator value of EI for pure substances (I i, EI ) or mixtures (Ip, EI ) is defined based on Eq. (1.37):

I i,

EI

or I p, EI ¼

8 > >
> : 0 if H ¼ No statement

H statements information from United Nations (2015b)

(1.37)

The normalized indicator value of E for a chemical route (Ir , EI ), is calculated with Eq. (1.9)

• H318—category 1 of serious eye damage, if it contains >3% of components in skin category 1 (H314) and/or eye category 1 (H318) • H319 or 320—category 2 of serious eye irritant or eye irritant, if it contains 1 but > > > 0:75 if H ¼ 315causes skin irritation < (1.38) I i, SI or Ip, SI ¼ 0:5 if H ¼ 316causes mild skin irritation > > 0:25 if H ¼ 317may cause an allergic skin reaction > > : 0 if H ¼ No statement

H statements information from United Nations (2015b)

The normalized indicator value of SI for a chemical route (Ir , SI ) is calculated with Eq. (1.9)

Continued

Table 1.1

Indicators for sustainability assessment of product/process alternatives at early design stages—cont’d Indicator

Explanation

Normalized indicator

Sources

• The normalized indicator value of RI for pure substances (Ii, RI ) or mixtures (Ip, RI ) is defined based on Eq. (1.39): 8 1 if H ¼ 334may cause allergy or asthma symptoms or > > > > breathing difficulties if inhaled < (1.39) 1 if H ¼ 335may cause respiratory irritation I i, RI or Ip, RI ¼ > > 1 if H ¼ 336may cause drowsiness or dizziness > > : 0 if H ¼ No statement

H statements information from United Nations (2015b)

• H314—categorized as corrosive, if it contains 5% of a corrosive component • H315—categorized as irritant, if it contains between 1% and 5% of corrosive ingredients, >10% of an irritant ingredient, or the sum of 10 times corrosive ingredients plus irritant ingredients is 10% • H316—categorized as mild irritant, if it contains between 1% to 10% of irritant ingredients or it has >10% of mild irritant ingredients, or the sum of 10 times corrosive ingredients plus irritant ingredients is 1 to > < > > :

1 if H ¼ 360may damage fertility or the unborn child 0:5 if H ¼ 361suspected of damaging fertility or the unborn child 0:5 if H ¼ 362may cause harm to breast  fed children 0 if H ¼ No statement

H statements information from United Nations (2015b)

(1.43)

The normalized indicator value of DFer for a chemical route (Ir , DFer ) is calculated with Eq. (1.9)

For mixtures: • H360—category 1, if it contains 0.1% of ingredients classified in the same category • H361—category 2, if it contains 0.1% of ingredients classified in the same category • H362—may cause harm to breast-fed children, if it contains 0.1% of ingredients classified in the same category

Continued

Table 1.1

Indicators for sustainability assessment of product/process alternatives at early design stages—cont’d Indicator

Explanation

Normalized indicator

Sources

Damage to organs (DtoO)

This hazard refers to substances that cause no lethal damage but can cause damage to organs after a single exposure. It is represented with H statements 370 or 371. It is possible to directly find H statements related to this hazard in substance safety documentation. The classification is done as follows:

• The normalized indicator value of DtoO for pure substances (Ii, DtoO ) or mixtures (Ip, DtoO ) is defined based on Eq. (1.44)

H statements information from United Nations (2015b)

• H370—category 1, if there is evidence from human data or significant toxic effects in animals with additional evidence of acute toxicity data such as: oral (rat) 300 mg/kg body weight, dermal rat or rabbit 1000 mg/kg body weight, inhalation (rat) gas 2500 ppm/4 h • H371—category 2, if there is evidence from animals of toxic effects that may be relevant to human health, such as oral (rat) LD50 > 300 and 2000 mg/kg body weight, dermal rat or rabbit LD50 > 1000 and 2000 mg/kg body weight, inhalation (rat) gas >2500 and 20,000 ppm/4 h

I i, DtoO or Ip, DtoO ¼

8 < :

1 if H ¼ 370causes damage to organs 0:5 if H ¼ 371may cause damage to organs 0 if H ¼ No statement

(1.44)

The normalized indicator value of DtoO for a chemical route (Ir , DtoO ) is calculated with Eq. (1.9)

For mixtures: • H370—category 1, if it contains 10% of ingredients classified in the same category • H371—category 2, if it contains 10% of ingredients classified in the same category • H371—category 2, if it contains 1% and < 10% of ingredients classified in category 1 Damage to organs prolonged exposure (DtoO_L)

This hazard refers to substances that cause no lethal damage but can cause damage to organs after repeated exposure. It is represented with H statements 372 or 373. It is possible to directly find H statements related to this hazard in substance safety documentation. The classification is done as follows: • H372—category 1, if there is evidence from human data or significant toxic effects in animals that are considered to be toxic for humans after repeated exposure. In the latter case, additional evidence is required, such as toxicity data after 90 days repeated dose study: oral (rat) 10 mg/Kg bodyweight/day, dermal rat or rabbit 20 mg/kg body weight/day, inhalation (rat) gas 50 ppm/6 h day • H373—category 2, if there is evidence from animals of toxic effects that may be harmful to human health after repeated exposure. The classification in this category uses toxicity data after a 90-day repeated dose study for guidance: oral (rat) 100 and >10 mg/kg body weight/day, dermal rat or rabbit 200 and >20 mg/kg body weight/day, inhalation (rat) gas 250 and >50 ppm/6 h day For mixtures: • H372—category 1, if it contains 10% of ingredients classified in the same category • H373—category 2, if it contains 10% of ingredients classified in the same category • H373—category 2, if it contains 1% and < 10% of ingredients classified in category 1

• The normalized indicator value of DtoO_L for pure substances (Ii,DtoO_L) or mixtures (Ip,DtoO_L) is defined based on Eq. (1.45) 8 1 if H ¼ 372causes damage to organs through prolonged > > > > or repeated exposure < (1.45) Ii, DtoO_L or Ip, DtoO_L ¼ 0:5 if H ¼ 373may cause damage to organs through prolonged > > or repeated exposure > > : 0 if H ¼ No statement • The normalized indicator value of DtoO_L for a chemical route (Ir , DtoO_L ) is calculated with Eq. (1.9)

H statements information from United Nations (2015b)

Chapter 1 • Sustainability assessment for chemical product

21

To calculate the indicators for a formulation, it is suggested that specific mixing rules are used for the indicator or mixture property models. If this is not possible, the indicator value for the mixture is estimated through the weighted average of properties of the components using their corresponding compositions. In this case, the weighted average is applied because components and composition in a formulation are not supposed to change over time. (i) Economic dimension The present approach uses added value as an economic indicator. Its value is calculated from the ratio of product value minus raw material costs to product value. This value gives an initial estimate of the possible profits of the product. The larger this value, the greater the gain per product. If the value of the products is less than the costs of raw materials, the alternative is screened out immediately. In advance design stages, capital and operating costs must be included. It is suggested that energy consumption indicators are used in later design stages, once the separation operations are defined. (ii) Environmental dimension This dimension is represented through three groups of indicators: impacts on water, impacts on air, and waste. They measure the potential negative impacts of the evaluated alternatives on the environment. (a) Impacts on water This group of indicators is represented by hazard to aquatic life (HtoAL) and hazard to aquatic life long term (HtoAL_L); both of them are defined according to the GHS. (b) Impacts on air This group is composed of five indicators: Global warming potential (GWP), photochemical oxidation potential (PCOP), ozone depletion potential (ODP), acidification potential (AP) from the WAR algorithm (Young and Cabezas, 1999; Srinivasan and Nhan, 2008), and hazard to the ozone layer (HtoOL) defined from the GHS. (c) Waste This group of indicators is represented by the bioconcentration factor (BCF), renewable sources (RS), and biodegradability (BD). BCF is defined from GHS, and the renewable source indicator is obtained by classifying substances into different categories, namely: (1) natural resources only physically modified, (2) enzymatically modified, (3) chemically modified, (4) from both natural and synthetic sources, or (5) of completely synthetic origin. This is a similar approach to that proposed by Cosmos-Ecocert standards (COSMOS-standard, 2018). BD is assessed by classifying substances as readily, inherently, and nonbiodegradable, according to ASTM 5864 (Sharma and Biresaw, 2017). (iii) Social dimension This dimension is represented through two groups of indicators: physical hazards and health hazards.

22

Towards Sustainable Chemical Processes

(a) Physical hazards Physical hazards of alternatives are measured using 12 indicators. From these, the following were defined based on GHS: flammability (F), reactivity (R), oxidizer (O), self-heating substances (SH), substances that release flammable gases when mixed with water (RFG), substances that cause corrosion to metals (CtoM), and gases under pressure (GUP). Despite existing in the GHS, explosiveness (E) is defined according to the ISI approach (Heikkil, 1999) because it enables an easier hazard categorization of both pure substances and mixtures. Process indicators were also considered: process temperature (T), process pressure (P), heat of reaction (H)—all three as defined by the ISI (Heikkil, 1999). Additionally, an indicator of complexity of the separation process (CSP) is involved, defined based on the conversion (X) and yield (Y) of the reaction. These last two indicators are only applicable when the goal is the selection of a chemical route. (b) Health hazards Health hazards are measured through 10 indicators, all of them calculated based on GHS H statements. The indicators are: acute toxicity when swallowed, by skin contact, or inhalation (AT), eye irritation (EI), skin irritation (SI), respiratory irritation (RI), danger when entering airways (DA), carcinogenicity (CAR), mutagenicity (MUT), damage to fertility (DFer), damage to organs single exposure (DtoO), and prolonged exposure (DtoO_L).

2.3 Integration of assessments MCDA methods are used to incorporate the assessments and select the best alternative considering all indicators simultaneously. In this case, PROMETHEE and AHP are suggested to solve the selection problem. AHP is used to calculate the weights of different groups of indicators, while PROMETHEE is applied to compare and rank alternatives implementing AHP weights. AHP structures the problem by breaking it into subproblems and organizing them in a hierarchical architecture (Saaty, 1980). In this method, criteria are pair-wise compared to calculate their weights (Ishizaka and Nemery, 2013). On the other hand, PROMETHEE is a noncompensatory method with which alternatives can be ranked based on a preference degree (Ishizaka and Nemery, 2013). The advantage of this method is that it is easy for decision-makers to apply and understand because it coincides closely with the human perspectives (Vinodh and Jeya Girubha, 2011). The combination of these methods has already been implemented in other selection problems (Turcksin et al., 2011).

2.3.1 Calculation of weights of Indicator groups using AHP As previously explained in Serna et al. (2016) and presented again here for explanatory purposes, AHP comprises four main steps: hierarchical structuring of the problem, assignment of weights, consistency test, and sensibility analysis (Ishizaka and Nemery, 2013):

Chapter 1 • Sustainability assessment for chemical product

•

•

•

•

23

“Hierarchical structuring of the problem: The method breaks down the problem and organizes its parts hierarchically. The top level is the goal of the decision, the second level comprises the criteria, and the lowest level contains the alternatives. In this case, the goal is to select the most sustainable product/process alternative at early design stages. The criteria are the six indicator groups shown in Fig. 1.2, i.e., economic, impacts on water, impacts on air, waste, health hazards, and physical hazards. The alternatives are the chemical process routes or the formulated products. Assignment of weights: Weights are calculated for each group of indicators using the pairwise comparison given by decision-makers and the eigenvalue method. The pairwise comparison is generally made on a ratio scale from 1 to 9, where 1 means equal importance between two indicator groups, and 9 means that one indicator group is much more important than the other. Consistency test: To know if the weights are meaningful, that is, there are no contradictions between the answers during pairwise comparison, a consistency test related to the eigenvalue method is performed. A value for the consistency index 90% can be obtained by vacuum distillation (EFEMA, 2019). The most widely used commercial product has at least 40% of α-monoglycerides.

Chapter 1 • Sustainability assessment for chemical product

25

Table 1.2 Selected physical, safety, and environmental properties of glyceryl monostearatea. Property

Value

Source

Appearance @25°C Actives (%) Saponification index (mgKOH/g) HLB Melting point (°C) Boiling point (°C) Flash point (°C) APHA color Density (g/mL, melted) Iodine value (cgI2/100 g) Reactivity, NFPA 704 Flammability, NFPA 704 Health hazard, NFPA 704 GWP KOW LD50 oral rats (mg/kg) LC50 fish 48 h (mg/kg) Bioconcentration factor Degradability (%)

White solid 99 172–176 3.8 58 150 210 200 0.973 Max. 0.5 0 1 1 0 6.62 >5000 0.183 784 78

Stepan Company (n.d.)

a

Sciencelab (2012)

Renewable source REACH-ECHA (2012)

Chemspider (2012)

There is no evidence that the product has negative effects on human health or the environment. There is no evidence that it is explosive.

Table 1.2 presents some properties of GM. (iii) Identification of alternatives This study evaluates the following chemical process routes to produce GM (Fig. 1.3): A. reaction between tristearin (T) and glycerol (G) B. reaction between stearic acid (SA) and glycerol (G), and C. reaction between methyl stearate (MS) and glycerol (G) (iv) Information gathering Table 1.3 summarizes most of the properties for the different substances involved in the chemical process routes described in Fig. 1.3. Table 1.4 presents the operating conditions of the different chemical routes. These values were obtained from technical reports of industrial processes and from the thermodynamic analysis of each reaction.

3.1.2 Assessment of alternatives Indicators described in Table 1.1 were calculated based on the information from Tables 1.3 and 1.4, and the corresponding results are summarized in Table 1.5. According to results in Table 1.5, there is no entirely good chemical route to produce GM. For example, reaction A has the best performance in economic, physical hazards, and health hazards groups of indicators, but it has a poor performance in the environmental dimension (waste and impacts on air). Thus, to select the most sustainable alternative, a compromise between groups of indicators has to be reached.

26

Towards Sustainable Chemical Processes

FIG. 1.3 Considered alternatives of chemical process routes to produce GM. (A) Glycerolysis of a triglyceride, (B) esterification between a fatty acid and glycerol, and (C) glycerolysis of a methyl ester.

3.1.3 Integration of assessments (i) Calculation of weights AHP is used to calculate the weights of different groups of indicators. For this case study, and just as a matter of example, weighting factors were defined according to a survey1 applied to a group of 14 graduate students from the chemical engineering program. All students had taken an environmental design course or had a background in the subject of sustainability. The applied survey had three parts. The first part explains the objectives of the study, the two design problems under analysis (selection of a chemical route for the production of GM, selection of a formulation for a cosmetic cream), and the AHP assessment scale. The second part asked the survey group to perform pairwise comparison between the six indicator groups for the case of selection of a chemical process route. The third part is similar to the second, but AHP is applied to select a formulation for a cosmetic cream. The relative weights of groups of indicators were calculated using the answers from the survey group. Data consistency was checked and only those with a consistency index 5000

[9]

[2]

No H statement

[7]

Readily biodegradable

[7]

>2000

[7]

No H statement

[8]

Readily biodegradable

[8]

>2600

[8]

[2]

No H statement

[8]

Readily biodegradable

[8]

14,400

[7]

[3]

No H statement

[8]

Readily biodegradable

[8]

>5000

[9]

No H statement

[7]

Readily biodegradable

[8]

>5000

[8]

H315, H319

[6]

Readily biodegradable

[8]

>5000

[8]

H315, H319, H412

[6]

Readily biodegradable

[8]

>60,000

[8]

H412

[6]

Readily biodegradable

[8]

>2000

[8]

H315, H319, H335

[6]

NA

>25,000

[16]

No H statement

[9]

Readily biodegradable

[14]

>2000

[8]

H315, H318

[6]

Readily biodegradable

[15]

>2000

[8]

H315, H319, H335

[6]

Readily biodegradable

[15]

>5000

[8]

H290, H314, H315, H318, H319

[6]

–

325

[8]

[4]

15

Benzyl Alcohol

9.5

[18]

9.5

[18]

16

Dehydroacetic Acid Propyl paraben

7

[18]

Synthetic—ecocert accepted Synthetic—ecocert accepted Synthetic

7

[18]

Synthetic

17

Methyl paraben Glycerin

10

[1]

Natural raw material chemically processed

18 19

Water Xanthan gum

1.1 USD/m3 35

[20] [1]

20 21

21 51

[1] [1]

22

Carbomer Acetate tocopherol Calendula oil

8.3 USD/mL

[18]

Natural raw material only physically transformed

23

Lemongrass oil

1.2 USD/mL

[18]

Natural raw material only physically transformed

Natural raw material chemically processed Synthetic Synthetic

[4]

H302, H319, H332

[8]

Readily biodegradable

[8]

1230

[13]

[4]

H315, H318, H372

[8]

Readily biodegradable

[8]

2565

[8]

H315, H319, H335

[6]

6000

[6]

H315, H319, H335, H412 No H statement

[8]

Readily biodegradable (similar to methylparaben) Readily biodegradable

[8]

2100

[8]

[8]

Readily biodegradable

[8]

27,200

[8]

No H statement

– [10]

– Readily biodegradable

[10]

– >5000

[10]

No H statement H413

[11] [6]

Readily biodegradable Readily biodegradable

[11] [12]

>5000 >10,000

[11] [12]

H315, H319, H335 (similar to lemongrass oil) H315, H319, H335

–

Readily biodegradable

[8]

>4640

[17]

[12]

Expected to be biodegradable

>5000

[12]

– [3]

References: [1] Making Cosmetics (2018), [2] BASF (2016), [3] COSMOS-standard (2018) [4] Ecocert Standard (2012), [5] BASF safety data sheet, [6] PubChem: Kim et al. (2016), [7] Croda safety data sheet, [8] European Chemicals Agency—ECHA (2018), [9] Evonik safety data sheet, [10] Solvay safety data sheet, [11] Lubrizol safety data sheet, [12] Sigma—Aldrich safety data sheet, [13] Merck safety data sheet, [14] Baker et al. (2000), [15] Madsen et al. (2001), [16] Liebert (1983), [17] Andersen et al. (2010), [18] Personal communication with suppliers of raw materials for cosmetics, [20] Local water costs 2018, Aqueduct of Bogota´.

Table 1.9

Normalized sustainability indicators of product alternatives for the cosmetic cream.

Chapter 1 • Sustainability assessment for chemical product

37

Health hazard

Relat ive weight (%)

50

Impacts on water

40

35 Physical hazard

30

Impacts 24 on air 20

Economic 10

13

12

10

8

Waste 0

Group of indicators

FIG. 1.6 Boxplot of relative weights calculated using AHP to select a formulation for a cosmetic cream.

In both cases, impacts on water and health hazards are the most important groups of indicators. The economic dimension remains the one with the lowest weight. The picture might not be the same if the surveys were to include investors, shareholders, or commercial managers. A global vision of the decision could be obtained taking into account all the stakeholders involved. (ii) Ranking alternatives with PROMETHEE To rank the alternatives, the PROMETHEE method was applied using the weights of groups of indicators obtained with AHP. Preference functions were similar to those in the first case study. Results are shown in Table 1.10. It was found that alternatives 1, 3, and 5 have the same position in ranking. This means that under the weights considered, any of these alternatives is a good option in terms of sustainability. The worst alternatives are formulations 4 and 6, which have the lowest performance on almost all criteria. As observed in Table 1.8, both contain sodium hydroxide, which increases their physical and health hazards and makes them less attractive compared Table 1.10 Global sustainability index of the product alternatives analyzed for the cosmetic cream. Rank

Alternatives

Phi

Phi+

Phi–

1 1 1 4 5 5

Alternative 1 Alternative 3 Alternative 5 Alternative 2 Alternative 4 Alternative 6

0.0973 0.0973 0.0973 0.0139 0.1528 0.1528

0.0973 0.0973 0.0973 0.0556 0.0000 0.0000

0.0000 0.0000 0.0000 0.0417 0.1528 0.1528

38

Towards Sustainable Chemical Processes

to other options. In this case, the final selection among the best ranked alternatives can be done regarding the availability of raw materials, economic factors, or any other differentiating factor.

4 Conclusions Nowadays, engineers face new customer demands and stronger sustainability requirements when designing products and processes. To do this, they analyze multiple alternatives, handling multiple information sources, criteria, and different degrees of uncertainty. Under this scenario, designers require a methodology that enables them to assemble information flows and helps them to make the best possible design decisions in a methodical form. This study proposed a systematic methodology that provides specific procedures and tools to assess and compare product/process alternatives, considering multiple criteria to select the best of them. The methodology uses sustainability indicators to assess product alternatives and multicriteria analysis methods to incorporate them. Most of the indicators were calculated based on the H statements of the GHS, because their meaning is globally accepted, and H statements are widely available. A list of indicators was suggested in Table 1.1, although decision-makers can add new indicators to the list, or consider only those that best fit their specific problem and information. The AHP multicriteria analysis method was used to integrate the assessments because of its high flexibility when analyzing very complex problems. The use of different MCAM methods is possible, provided that the method meets the decision problem requirements, and that the decision-maker can interpret the results correctly. While descriptions of the methodology can be cumbersome, taking into account all the details of the GHS, once the H statements are available, the proposed methodology is easy to apply and the indicators are readily computed. Thus, the proposed methodology can become a rapid conceptual design tool for systematic decision-making at the early stages of product/process synthesis. This was demonstrated with two examples: the definition of most sustainable route for the production of GM, and the identification of most sustainable cosmetic emulsion among a set of potential formulations. For future approaches, it is important to incorporate new tools to quantify the uncertainty of indicators into the assessment (Argoti et al., 2019).

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Hacking, T., Guthrie, P., 2008. A framework for clarifying the meaning of triple bottom-line, integrated, and sustainability assessment. Environ. Impact Assess. Rev. 28, 73–89. https://doi.org/10.1016/ j.eiar.2007.03.002. Hanes, R.J., Bakshi, B.R., 2015. Process to planet: a multiscale modeling framework toward sustainable engineering. AICHE J. 61. https://doi.org/10.1002/aic.14919. Hartman, L., 1966. Esterification rates of some saturated and unsaturated fatty acids with glycerol. J. Am. Oil Chem. Soc. 43, 536–538. Hassim, M.H., Edwards, D.W., 2006. Development of a methodology for assessing inherent occupational health hazards. Process. Saf. Environ. Prot. 84, 378–390. Heikkil, A., 1999. Inherent Safety in Process Plant Design. VTT Publications, Finland. Heintz, J., Belaud, J.-P., Pandya, N., Teles Dos Santos, M., Gerbaud, V., 2014. Computer aided product design tool for sustainable product development. Comput. Chem. Eng. 71, 362–376. https://doi. org/10.1016/j.compchemeng.2014.09.009. Hristova, M., Tchaoushev, S., 2006. Calculation of flash points and flammability limits of substances and mixtures. J. Univ. Chem. Technol. Metall. 41, 291–296. ICCA, 2017. Global Chemical Industry Contributions to the SUSTAINABLE DEVELOPMENT GOALS. ICIS, 2008. Indicative Chemical Prices. http://www.icis.com/chemicals/channel-info-chemicals-a-z/. (Accessed 14 May 2012). Ishizaka, A., Nemery, P., 2013. Multi-Criteria Decision Analysis, first ed. John Wiley & Sons, Inc., Chichester. Kim, S., Thiessen, P., Bolton, E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B., Wang, J., Yu, B., Zhang, J., Bryant, S., 2016. PubChem substance and compound databases. Nucleic Acids Res. 44 (D1), D12. https://doi.org/10.1093/nar/gkv951. Kimmel, T., 2004. Kinetic Investigation of the Base-Catalyzed Glycerolysis of Fatty Acid Methyl Esters. €t Berlin, Berlin. Technische Univerita Liebert, M.A., 1983. Final report on the safety assessment of PEG-2, 6, 12, 20, 32, 40, 50, 100, and 150 Stearate. Int. J. Toxicol. 2, 17–34. Madsen, T., Boyd, H., Nyl
en, D., 2001. Environmental and Health Assessment of Substances in Household Detergents and Cosmetic Detergent Products. 240 pp. Making Cosmetics, 2018. Cosmetic Ingredients. https://www.makingcosmetics.com. (Accessed 22 September 2018). Martinez-Hernandez, E., 2017. Trends in sustainable process design—from molecular to global scales. Curr. Opin. Chem. Eng. 17, 35–41. Mattei, M., Hill, M., Kontogeorgis, G.M., Gani, R., 2013. Design of an emulsion-based personal detergent through a model-based chemical product design methodology. In: Kraslawski, A., Turunen, I. (Eds.), Computer Aided Chemical Engineering. In: ESCAPE, vol. 23. Elsevier B.V, Lappeenranta, pp. 817–822. Morales, R., 2012. Propuesta y disen˜o de un proceso continuo para la produccio´n de monogliceridos a
steres metı´licos de a´cidos grasos. Universidad Nacional de Colombia. partir de e NCBI, 2018. PubChem. https://pubchem.ncbi.nlm.nih.gov/. (Accessed 15 September 2018). NIST, 2012. Chemical Webbook. https://webbook.nist.gov/chemistry/name-ser/. (Accessed 21 May 2012). OCDE, 2018. Echemportal. https://www.echemportal.org. REACH-ECHA, 2012. ECHA—Chemical Substance Database. https://echa.europa.eu/registrationdossier/-/registered-dossier/21230. (Accessed 11 August 2015). Ren, J., Fedele, A., Mason, M., Manzardo, A., Scipioni, A., 2013. Fuzzy multi-actor multi-criteria decision making for sustainability assessment of biomass-based technologies for hydrogen production. Int. J. Hydrogen Energy 38, 9111–9120. https://doi.org/10.1016/j.ijhydene.2013.05.074.

Chapter 1 • Sustainability assessment for chemical product

41

Saaty, T., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. Sciencelab, 2012. Science Lab MSDS. http://www.sciencelab.com/msds.php?msdsId¼9924173. (Accessed 7 May 2012). ´ ., 2016. Multi-criteria decision analysis for Serna, J., Dı´az, E., Narva´ez, P., Camargo, M., Ga´lvez, D., Orjuela, A the selection of sustainable chemical process routes during early design stages. Chem. Eng. Res. Des. https://doi.org/10.1016/j.cherd.2016.07.001. Sharma, B.K., Biresaw, G., 2017. Enviromentaly Friendly and Biobased Lubricants. CRC Press. https://doi. org/10.15713/ins.mmj.3. Spectrum Lab, 2019. Spectrum Lab Products MSDS. Srinivasan, R., Nhan, N.T., 2008. A statistical approach for evaluating inherent benign-ness of chemical process routes in early design stages. Process. Saf. Environ. Prot. 86, 163–174. Stepan Company, n.d. Stepan GMS pure product boletin. Stepan Company, Northfield, IL. Suarez, O., Narva´ez, P.C., 2017. Perfil Nacional de Sustancias Quı´micas en Colombia. Bogota. Ten, J.Y., Hassim, M.H.H., Ng, D.K.S., Chemmangattuvalappil, N.G., 2016. The incorporation of safety and health aspects as design criteria in a novel chemical product design framework. In: Martı´n, M., Eden, M.R., Chemmangattuvalappil, N.G. (Eds.), Tools for Chemical Product Design. Elsevier B.V, Oxford. Turcksin, L., Bernardini, A., Macharis, C., 2011. A combined AHP-PROMETHEE approach for selecting the most appropriate policy scenario to stimulate a clean vehicle fleet. Procedia Soc. Behav. Sci. 20, 954–965. https://doi.org/10.1016/j.sbspro.2011.08.104. United Nations 2015a. Sustainable Development Goals. https://www.un.org/sustainabledevelopment/. (Accessed 6 July 2019). United Nations (Ed.), 2015b. Globally Harmonized System of Classification and Labelling of Chemicals, fourth rev. ed. United Nations, New York. United Nations, 2018. The Sustainable Development Goals Report 2018. United Nations, New York. US-EPA, 2012. Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11. Vinodh, S., Jeya Girubha, R., 2011. PROMETHEE based sustainable concept selection. Appl. Math. Model. 36, 5301–5308. https://doi.org/10.1016/j.apm.2011.12.030. WBCSD, 2018. Chemical Sector SDG Roadmap. WBCSD, Geneva. Young, D.M., Cabezas, H., 1999. Designing sustainable processes with simulation: the waste reduction (WAR) algorithm. Comput. Chem. Eng. 23, 1477–1491. Young, D., Scharp, R., Cabezas, H., 2000. The waste reduction (WAR) algorithm: environmental impacts, energy consumption, and engineering economics. Waste Manag. 20, 605–615.

Further reading Jato-Espino, D., Castillo-Lopez, E., Rodriguez-Hernandez, J., Canteras-Jordana, J.C., 2014. A review of application of multi-criteria decision making methods in construction. Autom. Constr. 45, 151–162. Statnikov, R.B., Bordetsky, A., Statnikov, A., 2005. Multicriteria analysis of real-life engineering optimization problems: statement and solution. Nonlinear Anal. Theory Methods Appl. 63, e685–e696. https://doi. org/10.1016/j.na.2005.01.028.

2 Optimization and decision-making methods in realization of tri-generation systems Houssein Al Moussawia, Farouk Fardounb, Hasna Louahliac a

LEBANESE INTERNAT IONAL UNIVE RSIT Y, LI U, BE IRUT, LE BANON b FACULTY OF T ECHNOLOGY, DE PARTMENT GIM, LEBANESE UNIV E R S I T Y , S AI D A , L E B A N ON c NORMANDIE UNIV, UNICAN, L US AC , SAI NT LO , F RA NC E

1 Introduction Multigeneration systems are becoming a necessity in the recent decades due to the increasing global awareness, especially where renewable energy systems are absent. Environment wise, global issues such as ozone depletion, global warming, pollution, and their dangerous aftereffects on human life are raising a red line, which demands immediate responses from governments across the continents. Energy and economic wise, the need for efficient energy systems has increased due to the deterioration of nonrenewable energy sources and instability of prices. It’s a win-win situation, since the more efficient the energy production system is, the faster it will pay back, the lesser it will require to operate, and the fewer its consequences would be on environment. While the best option would be the use of renewables, the fact is that such systems are not always available or feasible. In this case, it is essential for the installed energy-producing systems to meet very high energy standards, thus justifying the employment of multigeneration systems that can deliver multiple forms of energy, such as heating, cooling, or electricity as required. However, due to the wide versatility of these systems in terms of the prime movers driving them, the components that run them, the operating conditions and fuel types to be employed, building locations and weather conditions, all require an adequate system control to meet the mentioned standards or criteria. Consequently, the realization of such systems must pass through a series of steps beginning with the design, evaluation, optimization, and ending with decision-making. This chapter is dedicated to go through these steps and their corresponding methods being used in the energy sector, along with some important results acquired from literature.

Towards Sustainable Chemical Processes. https://doi.org/10.1016/B978-0-12-818376-2.00002-8 © 2020 Elsevier Inc. All rights reserved.

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Towards Sustainable Chemical Processes

2 Design methods Upon designing a combined cooling, heating, and power (CCHP) system, or simply a trigeneration system, the designer should generally follow certain steps beginning with the selection of components, their types, and sufficient sizes. The efficiency and performance of each component must be defined. Then the operating strategy at which the system will function should be chosen. The building demand is also an important parameter that can influence such choices. In fact, a good design is one that balances between cost savings, energy savings, energy consumption, and environmental emissions. Thus, many methods can be employed in the design of CCHP systems. Those include maximum rectangle (MRM), energy management, thermodynamic, thermo-economic, and multicriteria sizing methods.

2.1 Maximum rectangle method This method originates from the mathematical rectangle method that is used to compute an approximation of definite integrals, by calculating the area of successive rectangles whose heights are defined by the values of the studied function. In CCHP system design, such methods can be used by plotting the curves of load requirements versus time and calculating the maximum area covering the surface under the curve, thus estimating the needed design parameters such as prime mover’s capacity and operation time. For instance, Sanaye and Khakpaay used the MRM to estimate an approximate value of total required nominal power of gas engines in a CCHP system operating under diverse scenarios (Sanaye and Khakpaay, 2014). Gu et al. also analyzed a CCHP system for residential applications by employing the MRM to determine the capacity of the system for different operating modes (Gu et al., 2012). Ebrahimi and Keshavarz were able to evaluate the influence of climatic conditions on the prime mover’s capacity by applying the MRM (Ebrahimi and Keshavarz, 2014).

2.2 Energy management sizing method Three basic strategies are usually used in energy management designing. The first is following electrical load (FEL) strategy, also known as electrical demand management (EDM), where the system is designed to completely satisfy the electrical demand. The waste heat can be then recovered to cover all or portion of the thermal demand. In case thermal recovery was not sufficient, auxiliary boilers can be used. In contrast, if it exceeds the demand, the excess can be either stored or discarded. The second is following thermal load (FTL) strategy, also known as thermal demand management (TDM), where the system is designed to completely satisfy the thermal demand and some or all of the electrical demand. Deficient electricity can be bought from grid if needed, while excess electricity can be sold back if authorized. The third strategy is the base load operation strategy where only constant part of electricity is covered and the remaining electrical and thermal demands are satisfied from the grid and boilers, respectively (Cho et al.,

Chapter 2 • Optimization and decision-making methods

45

2014). For example, Jiang-Jiang et al. presented a mathematical analysis of a CCHP system under five different climates and two operation modes: TDM and EDM. It was found that in cold climates, the CCHP system in TDM mode is superior, whereas EDM mode better suits mild climate zones ( Jiang-Jiang et al., 2010). Other operational strategies can be also found, such as the hybrid electric-thermal load strategy (HETS) adopted by Majo and Chamra who concluded optimal reduction results in primary energy cost and total cost of 7.5% and 4.4%, respectively, for a CCHP system following thermal load strategy, and 14.8% optimum carbon dioxide emission reduction (CDER) for a CCHP operated following the electrical load (Mago and Chamra, 2009). Also, the island mode operational strategy was presented by Gu et al. (2012).

2.3 Thermodynamic sizing method Energy is the most common measure upon which systems are usually designed. Energy efficiency defines the performance of different components, and thus the whole system, and allows the comparison with similar or alternative systems. Another parameter being increasingly used is exergy, which rather measures the quality, not the amount, of energy being provided or consumed, and the destroyed availability within the system. Consequently, the system weaknesses that can be ameliorated can be pinpointed. A survey of the main evaluation parameters concerning energy and exergy performance is presented in Tables 2.1 and 2.2, respectively.

2.4 Thermo-economic sizing method Although obtaining an efficient system is the main purpose of CCHP system design, it is equally or more important to obtain an enhanced economic system. The least cost (initial, operational, maintenance) of different components that can ensure CCHP operation is usually preferable. Thus, economic performance is a priority for most project managers, and the different criteria parameters used in economic evaluation can be found in Table 2.3. One example is the study performed by Abdollahi and Sayyaadi who used the total revenue requirement (TRR) method in their thermo-economic sizing of a small residential CCHP system (Abdollahi and Sayyaadi, 2013).

2.5 Multicriteria sizing method Modern CCHP designs are oriented to multicriteria sizing where more than one objective is considered. Energy, exergy, economy, and environment are usually used, in addition to different operating strategies. In such methods, getting “the” proper design is not easy and trade-offs are often made to compensate losses with savings. As an example, Uris et al. developed a methodology to determine the optimal size (with maximum profitability) of a biomass-fired organic Rankine cycle (ORC)-based CCHP system, taking into account the population, climate change, full and partial load operation impacts. It was concluded that CCHP, which is only worth where summer is highly severe and at full load operation,

Table 2.1

4-E evaluation criteria summary of literature: “energy criterion.” Evaluation criterion: Energy

Term

Equation

Reference P

Energy efficiency

ηtri ¼ ηcombustion 

Primary energy savings (ratio)a

ΔPE ¼

Primary energy rate

F SP ¼

P P P Qh + QDHW + Qb + Qc P Qf in



P i

Qc,i Q QDHW,i Ee,CPVT  Ee,aux + h,i + + ηe,t COPEHP,RS,i ηt ηt ηe,t

TPES,PESR ¼

FSP  FCCHP ¼1 FSP P

ηPM e ηPM e,ref

! +

P

(Mostafavi Tehrani et al., 2013; Chen and Ni, 2014; Al-Sulaiman et al., 2010; Al-Sulaiman et al., 2012a; Martins et al., 2012; Al-Sulaiman et al., 2011; Ebrahimi and Keshavarz, 2012; Al-Sulaiman et al., 2012b; Onovwiona and Ugursal, 2006; Ghaebi et al., 2012a; Caresana et al., 2011; Ma et al., 2011; Basrawi et al., 2013; Tse et al., 2011; Badami and Portoraro, 2009; Puig-Arnavat et al., 2014; Meratizaman et al., 2014; Borg and Kelly, 2013; Ahmadi et al., 2011; Colombo et al., 2007; Bilgen, 2000; Ranjbar et al., 2014; Lian et al., 2010) 

1    P ηh ηc + ηt ,ref ηe,ref  COPref



(Calise et al., 2014)

(Mago and Chamra, 2009), (Abdollahi and Sayyaadi, 2013), (Mostafavi Tehrani et al., 2013), (Al-Sulaiman et al., 2010), (Ma et al., 2011), (Ahmadi et al., 2011), (Liu et al., 2013), (Rocha et al., 2012), (Barbieri et al., 2012), (Fang et al., 2012), (Wu et al., 2012), (Popli et al., 2013), (Nesheim and Ertesva˚g, 2007), (Gładysz and Ziębik, 2013), (Gamou et al., 2002), (Behbahani-nia et al., 2010), (Sayyaadi et al., 2011), €p, 2005), € lo (Kavvadias and Maroulis, 2010), (Fu (Hinojosa et al., 2007)

      PECSeparate + ENet,Imports =ηGrid  PECμTRIGEN + ENet,Imports =ηGrid  100    PECSeparate + ENet,Imports =ηGrid

(Borg and Kelly, 2013)

Euser + EpSP

(Liu et al., 2013)

PES ¼ Primary energy consumption

EPM +

P

ηSP e ηgrid

+

Qc =COPe,c Qh =ηh + ηSP ηb e ηgrid

PEC ¼ (Egrid  Eexport)PECe + (Fpgu + Fb)PECNG P P P E + Qh + Qc P PER ¼ Qf PERCCHP ¼ P

Qh +

P

QCCHP P P Qc + QDHW + Pe

(Mago and Hueffed, 2010), (Fang et al., 2012) ( Jiang-Jiang et al., 2010), (Angrisani et al., 2012), (Wang et al., 2011), (Huangfu et al., 2007) (Kong et al., 2004), (Zhang et al., 2012), (Chua et al., 2012)

Energy utilization factor

EUFimproved ¼ EUF ¼

(Fuel) energy saving ratio/ indexb

EUF  EUFbase EUFbase

Pnet + Qs + Qhw + Qchw _ f LHVf m

( Jannelli et al., 2014), (Soroureddin et al., 2013; Ameri et al., 2010; Behboodi Kalhori et al., 2012; Espirito Santo, 2012)

Qf ,Conv:  Qf ,CCHP Qf ,Conv:

(Ebrahimi and Keshavarz, 2012), (Basrawi et al., 2013), (Wu et al., 2012; Suamir et al., 2012; Angrisani et al., 2014)

FESI,FESR ¼

FESRCCHP ¼

P Qh ηb

+

P Qc ηb

PPnet  Qf ηe + X Qc X Pnet P Qh + + ηb ηb ηe

mean mean mean FESRmean CCHP ¼ tpower  FESRpower + th  FESRh,alone + tc  FESRc,alone

Fuel energy utilization ratioc

FEURconv ¼

Eh + Er + Ec + Eeothers P  100% Ef conv

FEURCCHP ¼ Artificial thermal efficiencyd

ATE ¼

ηA ¼

(Soroureddin et al., 2013; Ameri et al., 2010; Behboodi Kalhori et al., 2012)

(Kavvadias et al., 2010) (Suamir et al., 2012)

Eh + Er + Ec + Eeothers + Eeexport  100% Ef CCHP

1 P_ ¼ _h _c 1 PESR Q Q  F_   ηh, ref ηh, ref  COPref ηe, ref ηPM ð1  PESRÞ

(Hatami-Marbini et al., 2013)

P_ net

(Soroureddin et al., 2013), (Behboodi Kalhori et al., 2012)

Q_ e Q_ g  ηe

Electrical equivalent efficiency

Pe   REE ¼  Qth PEC  ηh, ref

Power-toheat ratio

PHRcogen ¼

Cooling factor

C ¼ Q_ Q+cQ_

_

c

a

(Al-Sulaiman et al., 2012a)

P P ;PHRtrigen ¼ Qh Qh + Qc

(Rocha et al., 2012)

(Ghaebi et al., 2012a), (Bilgen, 2000), (Huangfu et al., 2007), (Ameri et al., 2010), (Onovwiona et al., 2007) (Maraver et al., 2013a), (Maraver et al., 2013b)

h

EHP, electric heat pump; RS, refrigeration system; E, energy; CPVT, concentrating photovoltaic/thermal collectors; SP, separate production; F, primary energy. t, working time during a year. c Er, energy used for refrigeration. d ̇ Q g, heat transfer rate in generator b

48

Towards Sustainable Chemical Processes

Table 2.2

4-E evaluation criteria summary of literature: “exergy criterion.” Evaluation criterion: Exergy Reference

Term

Equation

Exergy efficiencya

D ηex ¼ 1  ExExsupply

(Al-Sulaiman et al., 2012a)

_ _ _ _ _ f ¼m _ f echf ψ Tri ¼ P net + Exh +Ex_Exc + ExDHW ;Ex

( Jiang-Jiang et al., 2010), (Puig-Arnavat et al., 2014), (Ahmadi et al., 2011), (Bilgen, 2000), (Lian et al., 2010), (Wang et al., 2011), (Behboodi Kalhori et al., 2012), (Espirito Santo, 2012), (Ahmadi et al., 2012), (Deng et al., 2008)

f

  P_ net +

ηex,tri ¼

Exergy utilization factorb a b

1

T0 Thp

 Q_ h +

  1

_f Ex

T0 Tev

 Q_ ev

P ε ¼ ( (fsQs + fhwQhw + fcQc + fhQh) + PCCHP)/Exf



 + ϕQHTG a a ;ϕ ¼ 1  TxðTHTG ExUF ¼ Pe + Qmth_ fφLHV ;φ ¼ 1  TxTðecs Þ Þ

(Al-Sulaiman et al., 2012b), (Renzi and Brandoni, 2014) (Nesheim and Ertesva˚g, 2007) (Sayyaadi et al., 2011)

ExD, exergy destruction; echf, fuel chemical exergy; hp., heating process; o, atmospheric conditions; f, quality factor. Tx(ecs) and Tx(HTG), temperature available at engine cooling system and high temperature generator, respectively.

has a lower optimal size than CHP. Increased population or partial load operation leads to increased optimal plant size. Feasibility in small to medium villages (10,000–20,000 inhabitants) was confirmed, with an optimum plant size of 2–9 MWe, leading to an internal return rate (IRR) of 6–18% and thermal energy demand coverage ranging from 40% to 80% (Uris et al., 2015).

2.6 Fitness function method A fitness function is a specific type of objective function used to show how close a given design solution is to achieving the set objectives. It is usually used with optimization algorithms that perform random iterations, by giving a hint or figure of merit for each iteration. A proper fitness function must correlate closely with the set goals and compute as quick as possible. If a fitness function was designed badly, the algorithm may converge to an inappropriate solution or may not converge at all.

3 Evaluation criteria Performance evaluation in multigeneration systems is very important in the design and operation of such systems in order to decide whether to add a component, change its size, adjust different operating parameters and conditions, or even suggest system configuration improvements. The assessment is carried out by comparing the performance of separate and multigeneration systems. Accordingly, there are four main evaluation criteria: energy, exergy, economy, and environment. Moreover, in specific

Table 2.3

4-E evaluation criteria summary of literature: “economy criterion.” Evaluation criterion: economy

Term Operating cost (savings)a

Equation ΔCop ¼

Reference

P

i

Qc,i COPEHP,RS,i

+

Ee,CPVT Ee,aux ηe,t



Ce,unit + Cth,unit ðQh,i + QDHW,i Þ + mwater Cwater,unit  η

12 P

nk

k¼1

24 h           i P fuel OandM cμCHP  fμCHP h + cbfuel  ðfb Þh + cμCHP  eμCHP h + cebuy  ebuy h  cesell h  esell  refeedin  eμCHP h

j¼1

h

i

Qth,CHP =ηb m3 LHV year

n

1 + i Þ 1 NPV ¼ CC + ðAR  ACÞ  ði + SV  ð1 +1 i Þn ð1 + i Þn + CC NPVR ¼ NPVNPV n P k¼0

NPV,PW ¼ CRFðiACS ,Yearproj Þ

(Meratizaman et al., 2014), (Basrawi et al., 2014) P

8760

NPV,PW ¼ CInv +

Internal return rate/ return on investmentc

(Mostafavi Tehrani et al., 2013), (Behboodi Kalhori et al., 2012) (Eveloy et al., 2014)

Rk ð1 + i Þk

NPV ¼  CInv +

( Jiang-Jiang et al., 2010), (Wang et al., 2011), (Liu et al., 2013), (Fang et al., 2012) (Mostafavi Tehrani et al., 2013) (Mago and Hueffed, 2010)

SavingsCCHP ¼ Cref  Ct

NPV ¼

(Renzi and Brandoni, 2014)

h¼1

m  P Nj CCj OC ¼ Fpgu + Fb ðCf + μf Cc Þ + Egrid Ce + CRF 365

FCS ¼

(Calise et al., 2014) (Rosato et al., 2013), (Pagliarini et al., 2012)



Net present value (ratio)/ present worthb

Cbiomass,unit

ΔOC ¼ (OCConv  OCCCHP)/OCConv

Cop ¼

(Fuel) Cost savings

PE

t ,AH,i LHVbiomass

P

t¼LT P

ðIE + IAC Cf COandM Þi

i¼1

ð1 + i Þ

t¼0

t

(Rt ∗ (1 + i/100) t + LT ∗ (1 + i/100) t) n

ð1 + IRRÞ 1 CC + ðAR  ACÞ  IRR + SV  ð1 + 1IRRÞn ¼ 0 ð1 + IRRÞn

(Borg and Kelly, 2013), (Campos Celador et al., 2011) (Khan et al., 2004)

ROR ¼ NAB/CC

(Mostafavi Tehrani et al., 2013), (Behboodi Kalhori et al., 2012) (Meratizaman et al., 2014)

ROI% ¼ AOP CC

(Kavvadias et al., 2010) Continued

Table 2.3

4-E evaluation criteria summary of literature: “economy criterion.”—cont’d Evaluation criterion: economy

Term

Equation

Reference

(Simple) Payback periodd

SPBP ¼ (CC,CcHP)/Δ(TOandME) P capital invested ref CCCCHP PP ¼ P project cash flows in financial period ¼ CC Savings

(Pagliarini et al., 2012) (Meratizaman et al., 2014), (Kong et al., 2004), (Zhang et al., 2012), (Mago and Hueffed, 2010)

CCHP

CAPEX PP ¼ NetEquipment annual OPEX savings

SPB ¼ EC=

n P

(Eveloy et al., 2014), (Popli et al., 2013), (Popli et al., 2012) (Angrisani et al., 2014)

Rk ¼ EC=ðOCref  OCCCHP Þ

k¼1

Exergetic production cost

EPC ¼ Ep  ce + Es  cs + (Ereq  Ep)  Ce

(Silveira and Tuna, 2004)

Capital cost/ life cycle cost

CCL ¼ TRRL  Cf,L  COandM,L

(Ghaebi et al., 2012b)

Net profit Annual/ hourly total cost (savings)/ ratioe

1

TCC ¼ ACS  n(1 + i)

(Asaee et al., 2015)

LCCtotal ¼ CInv + LCCenergy + LCCOandM + LCCothers

(Marimo´n et al., 2011)

NP ¼ Profitpower  (Ceqm + Cinstallation + COandM + Creplacement  CSV + Cf)

   i TAC ¼ Qf  Cf + Pimport  CP ,import + life  1  Cequipment

(Basrawi et al., 2014)

TAC ¼

P

PM + b CC +

P

PM + b C

ð1 + i Þ

M

op + Cfop + Ccw + C P,purchase 

P

C P,sale

Fuentes-Cortes et al. (2015)

Ctot ¼ Ceqm + Cint + COandM

(Basrawi et al., 2013)

C_ ð$=yearÞ ¼ PW  CRFði, nÞ

(Oh et al., 2012)

SP

ATCS ¼ ATCATCATC SP CCHP

Capital recovery factor

(Ghaebi et al., 2012a)

( Jiang-Jiang et al., 2010), (Wang et al., 2011)

HTCS≜1  HTC HTCSP

(Liu et al., 2013)

CCHP CSR ¼ CconvCC conv

(Wu et al., 2012)

ACS ¼ CC(Ins) ∙ CRF(i, Yproj) + CM,A(Ins) + Cop(Labor + Fuel + Insurance)

(Meratizaman et al., 2014) (Meratizaman et al., 2014), (Fang et al., 2012), (Ghaebi et al., 2012b), (Khaljani et al., 2015)

n

ð1 + ieff Þ CRF ¼ ðieff 1 + i Þn 1 eff

Total revenue requirementf

TRRj ¼ TCRj + ROIj + Cf,j + COandM, j n P TRRj TRRL ¼ CRF j 1

(Ghaebi et al., 2012b)

ð1 + ieff Þ

Estimated economic returng

EER ¼ Eth ∙α∙Vh + Eth ð1αÞ∙Vc + Eet∙ðVe Vf OVC =ηe ÞPe ∙cInv

Amortization factor

AMF ¼

Energy nonrelated cost flowh

Z¼

Avoided cost

AC ¼ Vh 

Annual savingsi

AS ¼ AC  VNG 

Cash flow

CF ¼ (ENet,Export  FeedinTariff ) + (ETotal  Tariff )  (ENet,Export  Tariff )  (ENet,Export + ENet,DemandμTRIGEN) CM  (FuelμTRIGEN + FuelSeperate)Cf

Single cost present valuej

SCPn ¼

Recurring cost present valuej

RCPn ¼ RCn

Levelized cost of products a

(Martı´nez-Lera and Ballester, 2010)

IRR 1  ð1 + IRRÞn

(Roque Dı´az et al., 2010)

CInv,eqm  AMF  fOandM FLT P

Qh + Vc  P

P

Qc + Vhw 

(Roque Dı´az et al., 2010)

P

Qhw + Ve 

P

Pe

NGBC  COandM

SCn ½1 + ði  f Þn 1½1 + ðif Þn  if

systemPrice of produced water in market LCOPElectrical ¼ ACS ofAnnual Electrical Energy producation

(Kong et al., 2004), (Zhang et al., 2012) (Kong et al., 2004), (Zhang et al., 2012) (Borg and Kelly, 2013) (Marimo´n et al., 2011) (Marimo´n et al., 2011)

(Meratizaman et al., 2014)

of produced Electrical Energy in market LCOPWater ¼ ACS of systemPrice Annual Freshwater producation

EHP, electric heat pump; RS, refrigeration system; E, energy; CPVT, concentrating photovoltaic/thermal collectors; AH, auxiliary heaters; c, specific cost; e, electric energy; r, specific revenues; m, emissions factor; N, installed capacity. b AR, annual revenue; AC, annual cost; SV, salvage value; i, discount rate; n, project life time; Rk, net cash inflow in kth year; IE, income from electricity sale; IAC, annual hour income; Rt, return in time period; LT, liquidation yield at the end of service life. c NAB, net annual benefit; AOP, annual operation profit. d TOandME, total operation and maintenance expenditure. e M, maintenance; A, annualized. f TCR, total capital recovery. g E, energy; a, fraction of the used thermal energy as heat for cooling; V, price value. h f, factor; FLT, total annual operating time at full load. i NGBC, annual natural gas burning capacity. j f, inflation rate or price escalation.

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cases, a miscellaneous criterion might be employed. It is noticed that there are several subassessment criteria definitions in each of the four main criteria being used. A comprehensive survey is presented in Tables 2.1–2.5 containing the most common/ frequent evaluation definitions or terms found in literature. Note that in the tables, some terms are found to have different equations as the data source change. Consequently, general expressions for such terms are proposed such that they fit all the equations by excluding the unneeded terms each time. In multigeneration projects, although technical and environmental considerations should be taken into account, economic benefit is often the primary prerequisite for project acceptance unless government laws and legislations are enforced. Fortunately, even if the basic objective is to maximize the profits of the system, energy and environmental savings are usually subsequent outcomes. In certain sensitive cases, however, other factors such as reliability of energy supply or environmental issues may be equally or more important.

4 Optimization methods In most cases, the prior design of a CCHP system is not the final one to be implemented since an evaluation would usually show that the system can be improved. Hence, optimization of system design and operational strategy is a key element to improve energy efficiency or to reduce costs and emissions. The optimization process can involve the used prime mover (e.g., type, size, capacity…), the employed heat recovery equipment, the operating strategy of the system, and the energy storage system if installed. However, optimization methods can vary and may depend on the evaluation method. For instance, evaluation may be based on results acquired from field tests, which leads to an on-site optimization process. Although this method gives accurate and real results, it needs high costs, requires a lot of time, and is limited to a specific system configuration. However, evaluations using transient simulation models are much more time-efficient, need simple implementations, and easily allow modifications on the system (e.g., different type, size, or climate). In this case, optimization can be performed using many algorithms, some of which are mentioned below.

4.1 Mixed-integer linear programming (MILP) This is an optimization or feasibility program that involves problems in which some of the variables are constrained to be integers while other variables are allowed to be nonintegers. The objective function and the noninteger constraints are linear, and the classic method to solve such problem is to find the relaxation via Branch and Bound method. A lot of researchers would use this method in CCHP optimization. Ameri and Besharati described a model based on MILP to optimize the size and operation of seven CCHP systems. The optimized objective function was relative to capital and operational costs. When a network of district heating and cooling, CCHP system, and solar PVs was adopted,

Table 2.4

4-E evaluation criteria summary of literature: “environment criterion.” Evaluation criterion: Environment

Term

Equation

Reference h

Carbon dioxide abatement Emissions factora

CDA ¼ FCS  0:001914

Emission reduction (index/ratio)b

ERI,ERR,CDER,CO2 ER ¼ E

EFCO2,MT ¼ 0.00758. exp(LF  20.9LF6) EFNOx,MT ¼ (23 + 15  LF  13LFLF)3.07 EFCO,MT ¼ 0.659 + exp(2.25  LF  5.19  LF2) SP

ΔCO2 ¼

Carbon dioxide emissionsc Greenhouse gas emissionsd Greenhouse gas emissions reduction Total equivalent warming impacte Emission savingsf

a

tonCO2 year

i

ECCHP ESP

Emissions Emissionsconv

 _ CO2 = P_ net + Q_ h + Q_ eva  3600 EmiðCO2 , triÞ ¼ m CDE ¼ (Fpgu + Fboiler)μf + Egridμe h i  P P P P PM GHGET ¼ HD GHGFCHP t + GHGFB t F B + GHGFGRID t Wpurchase t PM F

GER ¼

GHGCONV  GHGCCHP GHGCONV

TEWI ¼ GWP  La  n + GWP  mcharge  (1  α) + n  Ea  β

    EmissionsSeparate + ENet,Imports ðeGrid Þ  EmissionsμTRIGEN + ENet,Imports ðeGrid Þ  100   EmissionsSeparate + ENet,Imports ðeGrid Þ SavingsCCHPcarbon ¼ (CDEref  CDECCHP)CC ES ¼

(Mostafavi Tehrani et al., 2013)

(Basrawi et al., 2014)

( Jiang-Jiang et al., 2010), ( Jiang-Jiang et al., 2010), (Basrawi et al., 2013), (Angrisani et al., 2012), (Wang et al., 2011), (Wang et al., 2011), (Liu et al., 2013), (Rosato et al., 2013), (Roselli et al., 2011), (Angrisani et al., 2010), (Angrisani et al., 2014), (Basrawi et al., 2014) (Freschi et al., 2013) (Al-Sulaiman et al., 2011) (Liu et al., 2013), (Mago and Hueffed, 2010), (Fang et al., 2012) Fuentes-Cortes et al. (2015)

(Asaee et al., 2015)

(Suamir et al., 2012)

(Borg and Kelly, 2013) (Mago and Hueffed, 2010)

LF, load factor. E, emissions. c pgu, power generation unit; m, carbon conversion factor. d HD, operating days; t, time. e La, annual refrigerant leakage; n, system operating time; a, recovery factor; Ea, annual energy consumption; b, CO2 emissions factor. f E, energy product; e, specific emissions. b

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Table 2.5

4-E evaluation criteria summary of literature: “miscellaneous criterion.” Evaluation criterion: Miscellaneous

Term

Equation

Performance factor indicator Evaluation criteria function

CCHP PFI ¼ PECPEC conventional

Reference +

CostCCHP Costconventional

+

CDECCHP CDEconventional

EChour ≜ 1  (ω1PES + ω2HTCS + ω3CDER); ω : weight coefficient 3P 65 P 24 EChourij ECannual ≜

(Mago and Chamra, 2009), (Chua et al., 2012) (Liu et al., 2013)

i¼1 j¼1

Reliabilitya Time availability

%Reliability ¼ a

Tplant ðMs + Mu Þ  100 Tplant Ms

%Availability ¼ b

Tplant ðMs + Mu Þ  100 Tplant

(Onovwiona and Ugursal, 2006) (Onovwiona and Ugursal, 2006)

Sustainability index

SI ¼ D1p

(Ahmadi et al., 2011)

Impact factor

standalone IMPACTCCHP ΔIMPACT ¼ IMPACTIMPACTstandalone

(Maraver et al., 2013a)

Fuel-free electricityc Quality indexd

Wfree ¼ PES  ηe,ref QI ¼ X  ηe + Y  ηh; QI > 105 ¼) good quality CHP

(Nesheim and Ertesva˚g, 2007) (Nesheim and Ertesva˚g, 2007)

a

Tplant, plant time service; Ms, scheduled maintenance time; Mu, unscheduled maintenance time. Dp, depletion number. c Expression of energy savings in the Netherlands. d Term used in the UK; X and Y, coefficients related to alternative electricity and heat supply options, respectively. b

reductions of 40.8% in energy cost and 38.7% in primary energy consumption (PEC) were achieved. By excluding the PVs, the lowest payback period (PBP) of 57 months was attained with 35.8% emissions savings (Ameri and Besharati, 2016). Piacentino et al. adopted the MILP method to optimize the layout of a CHP plant, size of its main components, and their operation strategy. A hotel was considered with three different plant configurations: two consisting of an internal combustion engine (ICE) with either single- or double-effect absorption chiller and hot water storage, and the other consisting of a microturbine with double-effect absorption chiller and pressurized superheated water storage. The main objective function of optimization was to minimize the net present cost beside maximization of energy and emission savings (Piacentino et al., 2015). Buoro et al. also presented a MILP model to optimize complex distributed energy systems that integrate CHP with solar heating and thermal storage. Their model considered the total annual investment, operating, and maintenance costs of the plant as an economic objective function. The method showed that an optimal solution can be reached by the adoption of distributed systems that include district heating networks, CCHP system, solar thermal plant, and thermal storage. Reductions up to 5% in total annual cost and 15% in PEC were observed (Buoro et al., 2014). A linear mathematical algorithm was presented by Gladysz and Zeibik to find the optimal coefficient of share of CHP in district heating systems, taking into account the European legislations regarding CO2 emissions. The objective function in the algorithm was to maximize the mean annual profit. It was concluded that the coefficient of CHP share can be increased by 0.1 through biomass co-firing or by the installation of a hot-water tank network (Gładysz and Ziębik, 2013). Oh et al.

Chapter 2 • Optimization and decision-making methods

55

applied an optimal planning method to case studies in Seoul, Korea, to check the profitability of CHP system adoption. The method was based on MILP solved through branch and bound algorithms and had the energy demand pattern as a crucial design parameter. PBP and IRR, as measures of economic feasibility, were found to be dependent on the change of tariffs of fuel and electricity (Oh et al., 2007). Wakui et al. developed a MILPbased optimal structural design model of residential power and heat supply devices, including CHP, heat pumps, and storage devices. Their objective was to minimize annual PEC under design constraints such as operable hours, daily selection of heat pump outlet water temperature, and capital recovery. The application of an ICE, a proton exchange membrane fuel cell (PEMFC), and a solid oxide fuel cell (SOFC) as the prime mover was considered. The results revealed the necessity of trade-offs between energy savings and initial costs in the optimal systems. Yet, the SOFC-CHP system had the greatest energy saving effect (Wakui et al., 2016). Gamou et al. proposed an optimal unit sizing method for CHP systems that treats energy demands as continuous random variables, by applying sensitivity analysis to linear programming models and enumeration method to mixedinteger programming ones. When applied to a phosphoric acid fuel cell (PAFC)-CHP system installed in an office building, the optimal capacity of the fuel cell decreased when uncertainties of energy demands were considered in the CHP system design (Gamou et al., 2002). MILP can be combined with other methods when needed. In a study by Velasco-Garcia et al., an optimization model to determine the most efficient operation of CHP utility systems by minimizing operating costs (including start-up) of system components was presented. Here, the optimization problem was solved by successive mixed-integer linear programming (SMILP) with computations implemented in MATLAB environment. MILP was combined with successive rigorous simulations to perform a sequence of MILP runs without losing precision. The results showed that up to 20% sizable cost savings can be achieved (Velasco-Garcia et al., 2011).

4.2 Mixed-integer nonlinear programming (MINLP) The difference between this method and MILP is that it deals with equations or inequalities that involve nonlinearities in objective functions or constraints. Besides branch and bound, major algorithms to solve an MINLP problem are generalized benders decomposition (GBD), outer-approximation (OA), and extended cutting plane (ECP) (Grossmann, 1990). One study regarding the use of MINLP in CCHP system optimization s et al., who presented an approach for designing resis that performed by Fuentes-Corte idential CHP systems that satisfy hot water and electricity demands at the lowest cost and minimum environmental impact. Optimization was formulated as a mixed-integer nonlinear programming (MINLP) problem and involved the selection of technology, size of required units, and operating modes of system equipment. A proper solution approach will trade off problem objectives that are greatly influenced by energy demands and climatic conditions. Nevertheless, CHP has been proven to be economically superior, environmentally competitive, and increasingly stable when combination

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s et al., 2015). Another iterative method of of technologies is involved (Fuentes-Corte nonlinear optimization is sequential quadratic programming (SQP) adopted by Liu et al., who proposed an intuitive approach of matrix modeling for a CCHP system with chillers under different evaluation criteria. Optimization was carried out considering linear weighted objective function and nonlinear equality/inequality constraints using the SQP algorithm, with the aid of MATLAB optimization toolbox. For the tested facility of 4500 m2 area, optimal power generation unit capacity was found to be 30 kW (Liu et al., 2013).

4.3 Stochastic optimization Stochastic optimization is a random search method that generates and uses random variables and picks a random solution for evaluation, then chooses the best solution over a number of samples. Although reaching the exact global optimal is not guaranteed, such a method can be helpful when the problem is highly complex or nonlinear. Many algorithms stem from this general optimization concept, such as simulated annealing, hill climbing, swarm and evolutionary algorithms. For example, Azizipanah-Abarghooee et al. developed a stochastic optimization framework based on chance-constrained programming to handle system economic load dispatch. Their technique also made use of a jointly distributed random variables method together with a hybrid modified cuckoo search algorithm and differential evolution to determine the maximum probability of meeting target costs (Azizipanah-Abarghooee et al., 2015).

4.4 Genetic algorithm Genetic algorithms (GAs) are stochastic optimization algorithms that emulate the mechanics of natural evolution (Xiong et al., 2015), such as inheritance, mutation, selection, and crossover. As any stochastic algorithm, GAs have no clear stop criterion in every problem. They are also difficult to operate on dynamic datasets, and may not scale well with complexity. However, they are especially attractive due to their reduced tendency of converging to local optima, without the need of a differentiable or continuous search space. In fact, GAs are one of the most common methods used in CCHP optimization. Some studies employing the use of such algorithms include that by Behbahani-nia et al., who discussed the optimization of a small CHP system consisting of a microturbine and heat recovery steam generator (HRSG) through exergetic and economic analyses. The GA method was used to find optimum values of design parameters, mainly of an HRSG. The results showed that fuel expenses and pressure drop in an HRSG constitute the major part of costs, and optimization can lead to reduced operational costs but not capital costs, with an optimal pinch-point temperature (PPT) in an HRSG of 6.26°C (Behbahani-nia et al., 2010). Sayyaadi considered the stability of optimal solutions while optimizing a CHP system by the means of evolutionary GAs (Sayyaadi, 2009). Frangopoulos and Dimopoulos included reliability considerations in the analysis and optimization of a CHP system, using the state-space method combined with the Intelligent Functional

Chapter 2 • Optimization and decision-making methods

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Approach. In case of no failure, GA was used to solve the optimization problem. A numerical example showed that profits are overestimated when reliability aspects are ignored (Frangopoulos, 2004). Ghaebi et al. also carried out an optimization study of a gas turbine-based CCHP system using GAs based on economic objective functions. The optimization led to an improvement of 15% in the objective function compared to the base case design value (Ghaebi et al., 2012b).

4.5 Particle swarm optimization (PSO) PSO algorithms mimic the flocking behaviors of animals in their movement (Xiong et al., 2015). A fitness function is assigned, and particles of the population, containing feasible solutions and certain velocity, are moved in the search space to improve their fitness (objective value) in an iterative manner. Although it is a relatively new method (proposed in 1995), PSO is being used increasingly in many energy optimization problems. For instance, Sayyaadi et al. performed optimization of a benchmark CHP system, including exergetic, exergo-economic, and environmental cost impact objectives, by applying a particle swarm optimizer. Optimized results showed that better exergetic and environmental operations can be achieved at higher product costs (Sayyaadi et al., 2011). Soppato et al. also presented and applied a particle swarm optimization (PSO) approach on a hybrid CHP system consisting of a diesel ICE, photovoltaic (PV) plant, boiler, pump, and storage devices. The optimization aim was to minimize the overall costs where the main constraint was to fulfill the user’s energy demands. A selected case study deduced that the optimal configuration led to an electrical and thermal coverage of 87% and 19% in winter, and 89% and 100% in summer, respectively (Stoppato et al., 2015). Although PSO and GA stem from the same optimization genre, they may differ in concept and operation. Some of their similarities and differences are presented in Table 2.6. Table 2.6

Some similarities and differences between GA and PSO.

Genetic algorithm

Particle swarm optimization

Similarities Randomly generated population Use of fitness function values to evaluate the population Iterative update of population and search for the optimum with random technique Differences Imitates the genetic behavior of living communities Parallel nature of search Discrete technique Generate solutions from parents via crossover Suitable for combinational problems Converge to high-quality solutions in few generations Available for more than 40 years

Imitates the social behavior of living communities Serial strategy in search Continuous technique Generate solutions via attractions to best positions Easy to implement with few parameters to adjust Faster convergence performance Only available since 1995

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4.6 Multiobjective optimization A multiobjective optimization, also called Pareto optimization, is an area of multicriteria decision-making in which multiple (two or more) objective functions are to be optimized simultaneously. When these objectives mutually conflict, the optimal solution of one function may exclude those of other functions. This imposes a new meaning of optimum that is rather to find a good compromise of objectives known as Pareto optimum. The set of Pareto optimal points in the objective space forms the Pareto front. Such algorithms lack speed and optimality guarantee and may require the presence of a domain expert decision-maker. However, they allow the identification of a wider range of alternatives, grants more realistic models, and promotes more appropriate roles for participants in the decision-making processes. Multiobjective optimization may use many techniques, some of which are mentioned above, according to the optimization problem. In fact, most CCHP optimization studies (especially the modern ones) consider multiobjective functions due to the importance of multiple involved criteria. In one study, Abdollahi and Sayyaadi presented a general methodology to perform a multiobjective optimization of CCHP systems using evolutionary algorithms. Three objective functions—exergetic efficiency, total levelized cost rate of system product, and environmental impact cost rate—were considered. The optimal CCHP configuration for a 2000 m2 case study building was obtained with an annual system cost of US$ 45.133/m2 and about US$ 8.106/m2 annual environmental impact cost (Abdollahi and Sayyaadi, 2013). In another study, Kavvadias and Maroulis developed a multiobjective optimization method for the design of CCHP plants, where economic, energetic, and environmental performance analyses were carried out. The nondominated sorting GA (NSGA-II) was employed to find the set of Pareto-optimal solutions, after which trade-off between efficient solutions can be decided according to the decision-maker (Kavvadias and Maroulis, 2010). Cardona and Piacentino, however, proposed a modified exergo-economic optimization based on the use of aggregate energy flows for CCHP plants serving civil buildings with irregular demand profiles. This approach on a CCHP plant supplying a hospital proved that CCHP is very competitive and reliable. The near-optimal design suggested an achievement of 45% reduced unit cost (Cardona and Piacentino, 2007).

5 Decision-making methods By definition, decision-making is to identify and choose alternatives based on values and preferences of a decision-maker. It is a problem-solving activity that terminates when a decision-maker is satisfied by a certain solution. In CCHP systems, gathered data from evaluation process, design analysis, and system optimization are often involved in the decision-making process. Consequently, a decision-making tool that depends on the decision problem must be selected.

Chapter 2 • Optimization and decision-making methods

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5.1 Cost-benefit analysis (CBA) CBA is a systematic approach for calculating and comparing benefits and costs of a certain project or decision. It is an analysis of the expected balance of benefits and costs of the proposed project expressed in terms of their net present values. In other words, it is a list of pros and cons that leads to a financial decision. The main advantages of CBA are its simplicity, clarity, and possibility for various scenarios. However, it requires the use of common measurements, making it unsuitable for qualitative benefit measurements.

5.2 Elementary methods These are simple noncomputational approaches that are best suited with single decisionmaker problems. They include the pros and cons analysis, which is a qualitative comparison between the good and bad features for each alternative, the maximin and maximax methods that are based on the avoidance of worst possible performance, the conjunctive and disjunctive methods that require just satisfactory performance for each criterion, and € lo € p, 2005). the lexicographic method (Fu

5.3 Multicriteria decision-making (MCDM) MCDM is the most well-known branch of decision-making that deals with decision problems in the presence of a number of decision criteria. It is a subdiscipline of operations research that can be further divided into either multiobjective decision-making (MODM) or multiattribute decision-making (MADM) that studies decision problems in which the decision space is continuous or discrete, respectively. In fact, numerous methods of MCDM are presently available (new, old, developed), each with its own characteristics, although some have common aspects, and many are implemented in specialized software. Hinojosa et al. compared the features of some available software packages and custom-built models used for CHP feasibility studies and thus decision-making processes. They suggested that market software packages tend to be either simple or sophisticated; however, custom-built models can offer more flexibility and transparency (Hinojosa et al., 2007). Al Asmar et al. also presented a review of useful software and algorithm tools that deal with optimization and decision-making regarding CHP systems and renewables. However, they suggested the decision-making process to be based on least operating costs and emissions (Asmar et al., 2015). MCDM methods can be classified in many ways. For instance, according to the type of data used, they can be deterministic, stochastic, fuzzy methods, or even a combination of those. Another classification could be according to the number of decision-makers involved, leading to single or group decisionmaking methods. From the wide variety of available MCDM methods, many are used in CCHP domains such as fuzzy sets, analytical hierarchy process (AHP), analytic network process (ANP), weighted sum method (WSM), weighted product method (WPM), technique for order preference by similarity to ideal solution (TOPSIS), elimination and choice

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translating reality (ELECTRE), preference ranking organization method for enrichment evaluation (PROMETHEE), gray incidence method (GIM), multiattribute value theory (MAVT) (Wang et al., 2008), linear programming technique for multidimensional analysis of preference (LINMAP) (Hatami-Marbini et al., 2013), and many others. Several studies on MCDM in the CCHP domain are available. Kabak et al. proposed a multicriteria decision-making method based on fuzzy logic theories to practically assess the energy performance of buildings. Cogeneration was one of the studied criteria besides location, climate change, building properties, and other parameters. A 20% share of cogeneration and renewables showed very good results upon the application of the optimization method (Kabak et al., 2014). Moghadam et al. performed a 3E analysis on a solar dish Stirling engine micro-CCHP system providing energy demands to residential buildings for five different climatic conditions and under different operational scenarios. Selection of the best engine size was done using TOPSIS decision-making method (Moghadam et al., 2013). Sayyaadi et al. employed a fuzzy approach in the decision-making of final optimal solution from the Pareto front. However, compared to the traditional LINMAP decision-making method, LINMAP leads to more appropriate final optimal solutions (Sayyaadi et al., 2011). Alanne et al. considered the selection of a residential energy supply system as a MCDM problem, where financial and environmental objectives were addressed. The Preference Assessment by Imprecise Ratio Statements (PAIRS) methodology, built with the WinPRE decision support tool, was carried out in accordance with the value tree analysis of MAVT that takes into account the preferences of decision-makers, performance uncertainties, and cost dependency. Numerical results suggested that micro-CHP can be a viable alternative (Alanne et al., 2007). Wickart and Madlener introduced a decision-making problem of industrial firms that aims to invest in CHP or heatonly systems, and where fuel and electricity price uncertainties are situated. The solution was suggested by applying a dynamic stochastic model that considers realistic values of economic and technical parameters. Results showed that critical volatility levels of electricity and fuel prices are 24% and 21%, respectively, and threshold CO2 tax was 40 €/ton (Wickart and Madlener, 2007). Sheen presented a Mellin Transform method in which fuzzy numbers were converted into a probabilistic density function, and fuzzy net present value (NPV) and PBP models were used as decision indices for CHP alternatives decisionmaking. Numerical examples showed the robustness of the proposed models that can serve as sensitivity analysis tools for uncertain decision-making. A practical case study also showed the effectiveness of an extracted-condensing steam turbine alternative over the back-pressure turbine for a CHP program in a petrochemical industry that has a widespread ratio between steam and electricity demands (Sheen, 2005). Wu and Rosen introduced an energy equilibrium approach in the modeling and optimization of a CHP-based district energy system to assist relative decision-making processes using WATEMS software. Results of considered examples suggested significant economic and environmental advantages of CHP over independent systems (Wu and Rosen, 1999). Jing et al. proposed an evaluation model based on fuzzy theory with multicriterion decision-making process to assess the merits of CCHP systems from technological, economic, environmental, and

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social points of view. A combination of fuzzy linguistics with subjective and objective weighting methods and their relative analysis allowed the evaluation under subcriteria ranked in a lower hierarchy than the primary criteria. According to a case analysis, the combination weights of the first hierarchy deduced that technology and environment are the most important evaluation indicators, whereas in the second hierarchy, investment recovery period, footprint, NPV, and CO2 emissions were the most important parameters. Among the five studied alternatives—separate energy generation, combined CCHP, biomass CCHP, fuel cell-CCHP, and ICE-CCHP—the combined gas steam–CCHP system had the best performance, although the fuel cell model appeared to be a promising choice ( Jing et al., 2012). The same modeling procedure was applied by Wang et al. on five options, including Stirling engine and gas turbine models. Results showed that an ICE with a LiBr absorption water chiller unit for a residential building in Shanghai, China, is the ideal solution. If the environmental aspect is solely considered, the SOFC option would be the best (Wang et al., 2008). Ebrahimi and Keshavarz used a hybrid decision-making method based on fuzzy sets and gray incidence approach in the decision process of CCHP systems under different climatic conditions (Ebrahimi and Keshavarz, 2014).

5.4 Sensitivity and risk analysis Sensitivity analysis is being employed in multicriteria decision problems where decision values are subjective and contain uncertainties, which makes it important to study how sensitive the final solution is to the changes of input parameters. Risk analysis is also being increasingly used and usually expressed by reliability. The latter relates to dependability, with successful operation and absence of breakdown or failure (Abdollahi and Sayyaadi, 2013). For example, Abdollahi and Sayyaadi selected the final optimal solution from the Pareto frontier using risk analysis as a novel decision-making tool, and the obtained results were compared to traditional LINMAP decision-maker, where reliability and availability were introduced in system modeling. It was shown that the proposed CCHP system is highly sensitive to the thermal efficiency of the boiler and COP of the absorption chiller, but less sensitive to fuel cost or its escalation rate (Abdollahi and Sayyaadi, 2013). Al-Mansour and Kozˇuh developed a computer program that models the economic evaluation of CHP systems using risk analysis method to help in CHP installation decisionmaking. Sensitivity analysis deduced that PBP and IRR are mainly sensitive to fuel prices (Al-Mansour and Kozˇuh, 2007).

References Abdollahi, G., Sayyaadi, H., 2013. Application of the multi-objective optimization and risk analysis for the sizing of a residential small-scale CCHP system. Energy Build. 60, 330–344. Ahmadi, P., Rosen, M.A., Dincer, I., 2011. Greenhouse gas emission and exergo-environmental analyses of a trigeneration energy system. Int. J. Greenhouse Gas Control 5 (6), 1540–1549. Ahmadi, P., Dincer, I., Rosen, M.A., 2012. Exergo-environmental analysis of an integrated organic Rankine cycle for trigeneration. Energy Convers. Manag. 64, 447–453.

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Wu, Y.J., Rosen, M.A., 1999. Assessing and optimizing the economic and environmental impacts of cogeneration/district energy systems using an energy equilibrium model. Appl. Energy 62 (3), 141–154. Wu, J., Wang, J., Li, S., 2012. Multi-objective optimal operation strategy study of micro-CCHP system. Energy 48 (1), 472–483. Xiong, N., Molina, D., Ortiz, M.L., Herrera, F., 2015. A walk into metaheuristics for engineering optimization: principles, methods and recent trends. Int. J. Comput. Intell. Syst. 8 (4), 606–636. Zhang, C., Yang, M., Lu, M., Zhu, J., Xu, W., 2012. 2012 international conference on medical physics and biomedical engineering thermal economic analysis on LiBr refrigeration-heat pump system applied in CCHP system. Phys. Procedia 33, 672–677.

Further reading Campanari, S., Valenti, G., Macchi, E., Lozza, G., Ravida`, N., 2014. Development of a micro-cogeneration laboratory and testing of a natural gas CHP unit based on PEM fuel cells. Appl. Therm. Eng. 71 (2), 714–720. Moya, M., Bruno, J.C., Eguia, P., Torres, E., Zamora, I., Coronas, A., 2011. Performance analysis of a trigeneration system based on a micro gas turbine and an air-cooled, indirect fired, ammonia–water absorption chiller. Appl. Energy 88 (12), 4424–4440. Somcharoenwattana, W., Menke, C., Kamolpus, D., Gvozdenac, D., 2011. Study of operational parameters improvement of natural-gas cogeneration plant in public buildings in Thailand. Energy Build. 43 (4), 925–934. Valenti, G., Silva, P., Fergnani, N., Di Marcoberardino, G., Campanari, S., Macchi, E., 2014. Experimental and numerical study of a micro-cogeneration Stirling engine for residential applications. Energy Procedia 45, 1235–1244.

3 Techno-economic assessment of an integrated bio-oil steam reforming and hydrodeoxygenation system for polygeneration of hydrogen, chemicals, and combined heat and power production Kok Siew Nga, Elias Martinez-Hernandezb a

DEPARTMENT OF ENGI NEERING SCIENCE , UNIVERSITY O F O XFORD, OX FORD, UN I TE D KI NG DO M b BIO MASS CO N VE RSIO N D EP AR TMENT , T H E ME XI CAN INSTI TUT E O F P ET ROLE U M , M E XI CO CIT Y, MEX IC O

1 Introduction The overexploitation of fossil fuels for the production of energy, fuels, and chemicals has caused severe destruction to the environment. Thus, extensive research is undergoing in light of minimizing the use of fossil fuels while promoting the utilization of other cleaner and renewable sources of energy. Biomass is the only renewable source containing carbon, and it is abundant worldwide. Biorefinery is often referred to a combination of processing facilities using biomass as a feedstock for the production of transportation fuels, chemicals, and combined heat and power (CHP) (Sadhukhan et al., 2014). Biorefinery can be considered as a type of polygeneration system that is designed to be highly flexible in terms of feedstock utilization and product generation, and highly integrated to maximize the efficiency of resource utilization (Ng and Martinez Hernandez, 2016; Ng, 2011; Ng et al., 2012, 2013). The technical and economic feasibilities of implementing a biorefinery system are always of interest to investors. From the economic standpoint, a biorefinery often involves high capital investment, and thus various process-to-process and site-wide integration strategies are needed to minimize the cost. Bio-oil is essentially a complex mixture of liquid made up of different classes of compounds, i.e., ketones, aldehydes, carboxylic acids, and other oxygenated compounds. Liquid bio-oil is a useful feedstock compared to solid biomass because it has a higher bulk Towards Sustainable Chemical Processes. https://doi.org/10.1016/B978-0-12-818376-2.00003-X © 2020 Elsevier Inc. All rights reserved.

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energy density that mitigates logistics and transportation difficulties, thereby minimizing the cost for transportation and enabling less space for storage. Furthermore, bio-oil has negligible amount of sulfur, nitrogen, ash, and other impurities; hence it is cleaner and gas cleaning might not be needed at downstream processing. Bio-oil offers a wide range of applications in the industry, including the use as an energy carrier; co-firing with conventional fossil fuel such as coal; upgrading for second-generation biofuels; and coproduction of fuels, chemicals, and energy through biorefinery concept (Bridgwater, 2018a). Much recent research on bio-oil upgrading has been devoted to conventional transportation fuel (e.g., gasoline and diesel) production ( Jones et al., 2009, 2013; Abdullah et al., 2015; Sadhukhan and Ng, 2011). These systems utilize the whole bio-oil into hydrocracking and hydrotreating, while the source of hydrogen required in the system comes from steam reforming of natural gas. Alternative transportation fuel (e.g., methanol and Fischer-Tropsch) production through bio-oil gasification route has also been investigated (Ng and Sadhukhan, 2011a,b; Zheng et al., 2019). Bio-oil derived from lignocellulosic biomass feedstock can be separated into aqueous and lignin fractions. An alternative route to generating hydrogen in situ is by steam reforming of aqueous phase of bio-oil (Vagia and Lemonidou, 2007; Lemonidou et al., 2013). The remaining lignin fraction of bio-oil can be depolymerized and upgraded through hydrodeoxygenation (HDO) into a spectrum of chemicals (Schutyser et al., 2018; Mabrouk et al., 2018; Saidi et al., 2014). HDO is rather an uncommon process in petroleum refinery by virtue of the negligible amount of oxygen components present in petroleum fuel, i.e.,