Control of Fuel Combustion in Boilers (Studies in Systems, Decision and Control, 287) 3030462986, 9783030462987

Table of contents :
Introduction
Contents
List of Abbreviations
List of Symbols
1 Methods and Means for the Control of the Fuel Combustion Process
1.1 Features of the Formation of Heat Balance of the Boiler
1.2 Approaches to the Formation of Air-Fuel Mixture in the Burner Devices
1.3 Methods for Controlling the Composition of Flue Gases of Boilers
1.4 Analysis of Control Systems for the Combustion Process
1.5 Current State of Boiler Units of Municipal and Industrial Power System and Ways of Their Modernization
References
2 Research of the Process of Fuel Combustion in Boilers
2.1 Features of Thermophysical Processes in the Working Area of the Boiler
2.2 Modeling of the Air-Fuel Path of the Boiler
2.3 Features of the Formation of Stoichiometric Air-Fuel Mixtures
2.4 Methods for Improving the Accuracy of Determining the EAC
2.5 Method for Determining the VOC in the Air
References
3 Hardware and Software Implementation of Modules of the System of the Fuel Combustion Control Process
3.1 Generalized Structure of the Combustion Control System in Boilers
3.2 Means of Monitoring the Process of Fuel Combustion Based on Oxygen Sensor
3.3 Formation of AFM of a Given Composition Based on Frequency Regulators
3.4 Software for Combustion Control System
References
4 Experimental Research of a Computer System for the Control of the Fuel Combustion Process
4.1 Results of Experimental Studies of VOC Changes
4.2 Metrological Evaluation of Experimental Studies of VOC Changing
4.3 Forecasting of VOC in the Air
4.4 Results of Experimental Studies of a Computerized System for Controlling the Fuel Combustion Process
4.5 Ecological Analysis of System Operation
4.6 Economic Analysis of System Efficiency
References

Citation preview

Studies in Systems, Decision and Control 287

Artur O. Zaporozhets

Control of Fuel Combustion in Boilers

Studies in Systems, Decision and Control Volume 287

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Systems, Decision and Control” (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control–quickly, up to date and with a high quality. The intent is to cover the theory, applications, and perspectives on the state of the art and future developments relevant to systems, decision making, control, complex processes and related areas, as embedded in the fields of engineering, computer science, physics, economics, social and life sciences, as well as the paradigms and methodologies behind them. The series contains monographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. ** Indexing: The books of this series are submitted to ISI, SCOPUS, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink.

More information about this series at http://www.springer.com/series/13304

Artur O. Zaporozhets

Control of Fuel Combustion in Boilers

123

Artur O. Zaporozhets National Academy of Sciences of Ukraine Institute of Engineering Thermophysics Kyiv, Ukraine

ISSN 2198-4182 ISSN 2198-4190 (electronic) Studies in Systems, Decision and Control ISBN 978-3-030-46298-7 ISBN 978-3-030-46299-4 (eBook) https://doi.org/10.1007/978-3-030-46299-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Introduction

The problems of increasing the efficiency of fuel combustion and reducing emissions of harmful substances are especially relevant at present and in those industries where the burning of large amounts of fuel occurs with insufficient completeness and with relatively low efficiency. This group includes boiler houses for the household sector and industrial enterprises with boiler units with a capacity of up to 3.5 MW. The efficiency of the operation of boiler plants depends on the availability of reliable information on the progress of technological processes. The absence of control and measurement systems for the composition of the flue gases leads to the low efficiency of the boiler, in particular, due to poor-quality fuel combustion. Therefore, in the current operating conditions of boiler plants, the development of technological solutions aimed at finding and minimizing the causes and mechanisms of the formation of harmful substances in flue gases is relevant. Due to the fact that replacing worn-out boiler units with new ones requires significant investment, a promising direction is the modernization of existing boiler units. This is a low-cost and efficient way of rational use of fuel while reducing the level of harmful substances in the flue gases. It remains relevant to ensure the functioning of control systems for the composition of the air-fuel mixture with a given speed and high reliability of maintaining the excess air coefficient at a stoichiometric level. Improving the efficiency of fuel combustion is an important task, the solution of which will lead to the saving of fuel materials by reducing heat loss with flue gases. The development and implementation of methods and means of monitoring the composition of the flue gases and automatic control of the fuel combustion process will increase the energy efficiency of boiler units and improve the environmental situation by reducing harmful emissions into the atmosphere (CO, NOx, SOx, etc.), which is therefore an urgent scientific applied problem, the solution of which is given in this monograph. The monograph consists of four chapters. Chapter 1 analyzes the problems of increasing the efficiency of the use of fuel resources and reducing the level of emissions of harmful substances in boilers and v

vi

Introduction

discusses the current state of the boilers of municipal and industrial heat power systems of Ukraine and ways to modernize them. The analysis of methods for determining the concentration of gas mixtures in order to control the composition of the flue gases of boiler plants was carried out. In Chapter 2, a model for monitoring and controlling the fuel combustion process based on a stepwise change in the composition of the air-fuel mixture and a method for increasing the accuracy of measuring the excess air coefficient taking into account the current concentration of oxygen in the air are developed and investigated. To ensure the efficiency of the fuel combustion process by increasing the accuracy of determining the excess air coefficient, the influence of meteorological parameters on the daily/seasonal change in the concentration of oxygen in the air is studied. Chapter 3 discusses the hardware and software implementation of a computerized system for monitoring the process of fuel combustion in boiler units of small and medium capacity. The structure of the hardware–software complex for monitoring and controlling the fuel combustion process is justified. It is a system for collecting, recording and processing the corresponding informative parameters and is intended to obtain arrays of source data and generate control signals. Chapter 4 presents the results of experimental studies of the developed methodology and system for monitoring the process of fuel combustion in boiler units. An experimental verification of the adequacy of the proposed model of the daily/seasonal change of the oxygen concentration in the air was carried out by comparing the results of direct measurements, by using a gas analyzer, and indirect ones based on the analysis of meteorological parameters. Functional tests of the automatic control system for the fuel combustion process at the NIISTU-5 series boiler were carried out. The study of the content of harmful substances in the flue gases of the boiler is completed. Book is for researchers, engineers, as well as lecturers and postgraduates of higher education institutions dealing with heat engineering equipment. 2020

Artur O. Zaporozhets

Contents

1 Methods and Means for the Control of the Fuel Combustion Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Features of the Formation of Heat Balance of the Boiler . . . 1.2 Approaches to the Formation of Air-Fuel Mixture in the Burner Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Methods for Controlling the Composition of Flue Gases of Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Analysis of Control Systems for the Combustion Process . . 1.5 Current State of Boiler Units of Municipal and Industrial Power System and Ways of Their Modernization . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Research of the Process of Fuel Combustion in Boilers . 2.1 Features of Thermophysical Processes in the Working Area of the Boiler . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Modeling of the Air-Fuel Path of the Boiler . . . . . . . . 2.3 Features of the Formation of Stoichiometric Air-Fuel Mixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Methods for Improving the Accuracy of Determining the EAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Method for Determining the VOC in the Air . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

..... .....

1 1

.....

5

..... .....

15 20

..... .....

25 30

.........

35

......... .........

35 37

.........

45

......... ......... .........

49 55 59

3 Hardware and Software Implementation of Modules of the System of the Fuel Combustion Control Process . . . . . . . . . . . 3.1 Generalized Structure of the Combustion Control System in Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Means of Monitoring the Process of Fuel Combustion Based on Oxygen Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61 61 66

vii

viii

Contents

3.3 Formation of AFM of a Given Composition Based on Frequency Regulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Software for Combustion Control System . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Experimental Research of a Computer System for the Control of the Fuel Combustion Process . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Results of Experimental Studies of VOC Changes . . . . . . . . 4.2 Metrological Evaluation of Experimental Studies of VOC Changing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Forecasting of VOC in the Air . . . . . . . . . . . . . . . . . . . . . . 4.4 Results of Experimental Studies of a Computerized System for Controlling the Fuel Combustion Process . . . . . . . . . . . . 4.5 Ecological Analysis of System Operation . . . . . . . . . . . . . . . 4.6 Economic Analysis of System Efficiency . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

.... ....

70 81 86 89 89

.... 93 . . . . 102 . . . .

. . . .

. . . .

. . . .

109 115 116 122

List of Abbreviations

AFM AFR DCF EAC ECE FRC FUF GQPC HCS IMS KCF PQPC VOC

Air-fuel mixture Air-fuel ratio Diffusion combustion front Excess air coefficient Energy conversion efficiency Frequency ratio controller Fuel utilization factor Generalized qualitative performance criterion Housing and communal services Information-measuring systems Kinetic combustion front Partial qualitative performance criteria Volume oxygen concentration

ix

List of Symbols

dа dair g P Pj Т Qin Qr q2 q3 q4 q5 q6 VH2 O Vd.p. gb u [O2] [O2]dir [O2]indir [O2]out [CO] [СО2] [NO] [NO2]

Fuel moisture content, g/m3 Air moisture content, g/m3 Acceleration of gravity, m/s2 Absolute atmospheric pressure, Pa Probability distribution of volume oxygen concentration value Ambient temperature, K Input heat, kJ/m3 Thermal effect of the reaction, J/mol Heat loss with flue gases, % Heat loss with chemical incomplete combustion, % Heat loss with mechanical incomplete combustion, % Heat loss through boiler walls, % Losses with physical heat of slag, % Volume of water vapor in combustion products, m3 Volume of dry combustion products, m3 Boiler efficiency, % Relative humidity, % Environmental oxygen concentration, % Oxygen concentration, measured by direct method, % Oxygen concentration, measured by indirect method, % Oxygen concentration in flue gases, % Carbon monoxide in flue gases, % Carbon dioxide in flue gases, % Nitrogen monoxide in flue gases, % Nitrogen dioxide in flue gases, %

xi

Chapter 1

Methods and Means for the Control of the Fuel Combustion Process

1.1 Features of the Formation of Heat Balance of the Boiler Monitoring the process of fuel combustion is reduced to controlling the content of flue gases, while the objects of study are the boiler and the air-fuel path [1]. The block diagram of monitoring the process of burning fuel is shown in Fig. 1.1. The efficiency of the boiler unit is determined on the basis of the efficiency of the functioning of its units: burners, heating surfaces, heat exchangers (economizers, air heaters), rotary machines and other devices [2]. One of the most important components of the combustion process is the efficiency of fuel combustion, that is, the efficiency of the work of the burners and the associated equipment (blow fan and smoke exhausters) [3]. The equation of the heat balance of the boiler in general form in the stationary mode of operation has the following form: QN = Q1 + Q2 + Q3 + Q4 + Q5 + Q6 , where QN —produced heat; Q1 —useful heat; Q2 —heat losses with flue gases; Q3 —chemical losses with incomplete combustion; Q4 —heat losses from mechanical incomplete combustion; Q5 —heat losses from heating surfaces; Q6 —heat losses from slag. The efficiency of fuel combustion is characterized by the value of energy conversion efficiency (ECE), which in turn represents the difference between thermal energy, released during fuel combustion, and energy losses in the boiler unit. ECE can be determined by direct and reverse balance: • Q1 /QN = q1 = ηd —ECE by direct balance; • ηr = 100 − (q2 + q3 + q4 + q5 + q6 )—ECE by inverse balance.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 A. O. Zaporozhets, Control of Fuel Combustion in Boilers, Studies in Systems, Decision and Control 287, https://doi.org/10.1007/978-3-030-46299-4_1

1

2

1 Methods and Means for the Control …

Fig. 1.1 Structural diagram of monitoring the process of fuel combustion

Fig. 1.2 Heat losses with flue gases in the boiler unit with different composition of combustion products

The main heat losses from the combustion of natural gas is the heat losses from the flue gases; heat losses associated with chemical incomplete combustion; heat losses from heating surfaces [3]. Heat losses with flue gases depends on: the temperature difference between the flue gases and the air supplied to the boiler furnace, and the residual oxygen content in the flue gases, which characterizes by the excess air coefficient (EAC, α) or the air-fuel ratio. These losses are significant: for boilers of small and medium power they can be from 10 to 26%, for gas boilers and boilers of power plants—6 − 12%, and mainly affect on the ECE of the boiler.

1.1 Features of the Formation of Heat Balance of the Boiler

3

Figure 1.2 shows the heat losses from the flue gases, calculated by the method of Ravych [4], at various temperatures of the flue gases. According to the methodology based on the generalized characteristics of the fuel, q2 during combustion of natural and associated gases is determined by the formula (%): q2 = 0, 01 · z · (tg − ta ), where z takes a tabular value, t g is the temperature of the flue gases, t a is the ambient temperature. Increasing t g by 10 °C over a normal value for a given load of the boiler increases q2 by at least 0.5%, and increasing the EAC by 0.1 increases q2 by about 1%. At the same time, taking into account other heat losses, the real ECE of the boiler depending on the total content of CO2 , CO and CH4 in the flue gases at different temperatures of the flue gases is shown in Fig. 1.3. Heat losses with chemical fuel deficiency depend on: EAC, quality of fuel and air mixing; the completeness of fuel combustion and the content of combustible residues in the flue gases ([CO] + [H2 ] + [CH]). These losses should be minimized with proper organization of the combustion process. According to the method given above, heat losses with chemical incomplete combustion q3 is determined according to the data on the content of combustion products by the formulas: • q3 =

35·[CO]+30·[H2 ]+100·[CH4 ] —for CO2 +CO+CH4

natural gas;

• q3 =

40·[CO]+30·[H2 ]+110·[CH4 ] —for CO2 +CO+CH4

oil field gas.

Fig. 1.3 The change of the ECE of the boiler depending on the composition of the flue gases

4

1 Methods and Means for the Control …

Heat losses from incomplete combustion of fuel can be significant, and reach values in the range from 3.5 to 7% (depending on EAC). Moreover, with certain structural features, combustible gases can be burned without loss of q3 . Heat losses from heating surfaces q5 include heat, which is given to the lining and other parts of the boiler to the environment. The value of q5 depends from the quality of the lining and insulation of the external walls of the unit and from the temperature difference between its outer surface and the environment [5–10]. For water boilers of the type KV-GM, KVG, TVG, KSVT, KSO, Turbomat, the dependence of heat losses to the environment q5 from the boiler thermal power is shown in Fig. 1.4. It should be noted that for all kinds of boilers the value of the parameter q5 according to Fig. 1.4 does not correspond to reality due to their structural features. Therefore, for comparison, the functioning of boilers and other thermal units operating on natural gas is based on the fuel utilization factor (FUF, ηu ): ηu = 100 − (q2 + q3 ). Figure 1.5 presents a theoretical graph of the composition of the products of natural gas combustion from the EAC with full fuel combustion. Table 1.1 presents data on the amount and composition of methane combustion products (as the main component of natural gas). From the above data it can be seen that a decrease in the EAC contributes to a decrease in the oxygen concentration in the flue gases, an increase in the ECE and, consequently, a decrease in the temperature of the flue gases and the power consumption of the blow fan and smoke exhauster. At the same time, emissions of harmful oxides (NOx , SOx ) are reduced, which leads to a decrease in environmental pollution [11–14]. The appearance of chemical underburning (CO) in the products of fuel combustion determines the limit of permissible impact on reducing the air supply. This boundary is unstable and depends on both the characteristics of the burners and the load of the boiler. Its position is also affected by the composition of the fuel; climatic conditions; fuel and air temperature; technical condition of the equipment and many other factors. The area of the economically advantageous mode of fuel combustion corresponds to a low value of oxygen

Fig. 1.4 Dependence of heat loss to the environment on the heat power of the boiler

1.1 Features of the Formation of Heat Balance of the Boiler

5

Fig. 1.5 Dependence of the composition of the combustion products on the EAC

content (0.5–1.5%) and the appearance of “traces” of chemical underburning, that is, the CO content at more than 200 ppm [15]. The optimal composition of the flue gases of the boiler plant are given in Table 1.2. Analysis of heat losses during the operation of the boiler showed that heat losses with the flue gases are the most significant. Thus, the urgent task is to minimize heat losses with flue gases by ensuring the formation of stoichiometric AFR in the burner devices.

1.2 Approaches to the Formation of Air-Fuel Mixture in the Burner Devices Material balances of elementary reactions allow to calculate the mass flow rate of oxygen and the number of reaction products per unit mass for a particular type of fuel. Knowledge of the density of gases makes it possible to go from mass to volumetric units. The calculation is usually carried out under normal physical conditions (T = 273.15 K, P = 1.01325 × 105 Pa, g = 9.80665 m/s2 ). In the general case, the combustion of any fuel is described by the equation of unsteady heat and mass transfer in the presence of internal sources and sinks of heat and mass, and the equation of motion of the medium (Navier-Stokes equations and continuity): dT λ 2 dT dT dT dT = + υX + υY + υZ = ∇ T + QP ω; dτ dτ dx dy dz cp ρ

Composition of the combustion products, vol. %

dry – 88.26

[N2 ]

71.38

[N2 ] [O2 ]

–

[O2 ] 11.74

9.51

[CO2 ]

[CO2 ]

19.11

[H2 O]

8.52

dry wet

9.52 10.52

wet

Relative air flow,

Amount of combustion products, m3 /m3

1

EAC, α

m3 /m3

Parameter

Table 1.1 Composition and quantity of methane combustion products

87.33

2.11

10.56

72.11

1.74

8.72

17.43

9.47

11.47

10.47

1.1

86.57

3.84

9.59

72.63

3.22

8.05

16.1

10.42

12.42

11.42

1.2

85.94

5.27

8.79

73.08

4.49

7.48

14.95

11.38

13.38

12.38

1.3

85.4

6.49

8.11

73.48

5.58

6.98

13.96

12.33

14.33

13.33

1.4

84.94

7.53

7.53

73.83

6.54

6.54

13.09

13.28

15.28

14.28

1.5

83.37

11.09

5.54

75.05

9.98

4.99

9.98

18.04

20.04

19.04

2

6 1 Methods and Means for the Control …

1.2 Approaches to the Formation of Air-Fuel Mixture in the Burner … Table 1.2 Optimal composition of dry products of natural gas combustion

7

Components

Optimal concentration, %

Note

[O2 ]

0.5–5

Oxygen

[CO2 ]

12–16

Carbon dioxide

[CO]

0.25 . k

Another approach to modeling the air-fuel path [7] is based on the following conditions: the control signal is the deviation of the pressure in the furnace from a predetermined value; the input value is the change in the position of the directing influence of the exhaust fan ϕe f . As an external disturbance is taken the changing in the flow of flue gases in the inflow M. Thus, the differential equation of a given area has the form: Y2 ·  P¨ f + Y1 · P f + P f = q2 ·  M˙ + q1 · M + q4 · ϕ˙e f + q3 · ϕe f . (2.7) The dynamic properties of the air path, which determine the relationship between the change in the air supply to the boiler furnace M L and the position of the guide apparatus of the blow fan ϕb f , are described by the equation: Y4 ·  M¨ L + Y3 ·  M˙ L + M L = q6 · ϕ˙b f + q5 · ϕb f .

(2.8)

EAC is a quantity that cannot be determined using direct measurements, in general it is determined by the oxygen concentration in the flue gases CO2 (regardless of the composition of the fuel). At the same time, it is not possible to measure CO2 at the point directly behind the burning zone. In most cases, the sampling point is located in the convective part of the boiler. In this case, the gases are first mixed in the combustion zone, and then transferred through the radiation surfaces to the zones of convective heating surfaces. In general, this process can be written using an approximating equation: Y6 · C¨ O2 + Y5 · C˙ O2 + CO2 = q7 · M L · (t − τ ) + q8 · M f uel · (t − τ ), (2.9) where τ is the transport lag time (equal to the inertia value of the gas analysis unit).

42

2 Research of the Process of Fuel Combustion in Boilers

Fig. 2.5 Parametric scheme of the air-fuel path

Thus, according to the considered systems, the regulating parameters of the airfuel path of the boiler unit are the deviation from the nominal value of oxygen CO2 and carbon monoxide CCO concentration in the flue gases and the negative pressure deviation in the combustion furnace P f , the control signals are the change in the position of the blow fan ϕb f and exhaust fan ϕe f , the disturbing signal—the change in fuel consumption M f uel (Fig. 2.5). The numerical values of the coefficients of the differential Eq. (2.7) are determined using the following equations: mΓ · β; Y2 = ((a LΓ )2 + 2 · a ΓP · a LΓ ) · P¯Γ mΓ · β; q1 = a LΓ + a ΓP ; q2 = ((a LΓ )2 + a LΓ · a ΓP ) · 2 · P¯Γ mΓ q3 = −bΓP ; q4 = −a LΓ · bΓP · · α, 2 · P¯Γ Y1 = (2 · a LΓ + 2 · a ΓP ) ·



mΓ ·β P¯Γ

2 ;

where m Γ —the mass of flue gases in the fuel path of the boiler in the stationary mode of operation of the boiler, kg; P¯Γ —the pressure of the flue gases in the upper part of the combustion chamber, Pa; β—balanced coefficient (for gas ~ 0.85); the F H M , a ΓP = H , bΓP = ϕϕ are determined graphically by the flow values a LΓ = H Q Q F characteristics of the duct and the fan (Fig. 2.6). The numerical values of the coefficients of the differential Eq. (2.8) are determined using the following equations:    2 mB 2 · a PB · a LB + (a LB )2 m B a PB · (a LB )2 · β ; Y4 = B ·β ; Y3 = a LB + a PB a L + a PB 2 · P¯B P¯B   mB bB bB · a B q5 = B P B ; q6 = BP PB ·β , aL + a P a L + a P 2 · P¯B where m B —the mass of flue gases in the air path of the boiler in the stationary mode of operation, kg; P¯B —the average value of air pressure in the duct, Pa; a L =

2.2 Modeling of the Air-Fuel Path of the Boiler

43

Fig. 2.6 Pressure characteristics of the blow fan and air path HM Q

H

, a P = H , b P = ϕϕ —the values are determined graphically by the flow Q F characteristics of the fuel path and the exhaust fan (Fig. 2.7). Differential Eq. (2.9) is parametrized using the following relations: F

T¯B ; 3 21 21 q7 = − ; q8 = − . ¯ ¯ MB · λ ML · λ

T5 = 2 · TB ; T6 = TB2 ; TB =

The numerical values of the coefficients of differential Eqs. (2.7–2.9) were determined by the design and thermal parameters of the GM-50 drum boiler for 3 nominal modes corresponding to 50, 75 and 100% of the thermal load (Table 2.1). The resulting mathematical model of the air-fuel path of the average power boiler allows to explore the possibilities of applying new control algorithms, including optimal multi-dimensional control.

44

2 Research of the Process of Fuel Combustion in Boilers

Fig. 2.7 Pressure characteristics of the exhaust fan and the fuel path Table 2.1 Numerical characteristics of the model of the air-fuel path of the boiler Number

Coefficient

Dimension

Steam capacity, t/h 25

37.5

50

1

T1

s

1.67

1.33

1.04

2

T2

s2

0.0566

0.0574

0.0572

3

T3

s

0.0748

0.112

0.152

4

T4

s2

3.47 × 10−4

8.16 × 10−4

7.22 × 10−4

5

T5

s

16

16

16

6

T6

s2

56

56

56

7

q1

Pa s/kg

1080

861

673

8

q2

Pa s2 /kg

80.3

87.6

106

9

q3

Pa

−278

−254

−125

10

q4

Pa s

−20.6

−25.8

−19.7

11

q5

kg s

0.224

0.178

0.147

12

q6

kg s2

4.55 × 10−3

6.73 × 10−3

6.71 × 10−3

13

q7

s/kg

−39.8

−26.5

−19.9

14

q8

s/kg

2.74

1.82

1.37

2.3 Features of the Formation of Stoichiometric Air-Fuel Mixtures

45

2.3 Features of the Formation of Stoichiometric Air-Fuel Mixtures A number of requirements are put forward for the design and use of burners operating on liquid gaseous fuels, including compactness and ease of use, long service life and relatively low cost. But one of the most important requirements is the need to ensure complete and reliable combustion of fuel with a minimum EAC, that is, the burner devices must ensure the formation of a stoichiometric AFM. Figure 2.8 shows the factors affecting the process of fuel combustion [8–10]. A stoichiometric AFM is a mixture containing exactly the same amount of oxidant as is necessary to completely oxidize the fuel. In practice, lean AFM is formed for fuel combustion, due to a number of factors: insufficient mixing of fuel and oxidizer, structural features of the boiler, and the like. For forming a stoichiometric mixture, it is necessary to know the amount of oxygen that took part in the combustion reaction. To do this, consider the equation of perfect complete combustion: Ck Hl Om + O2 → CO2 + H2 O,

(2.10)

where k, l, m—the amount of carbon, hydrogen and oxygen atoms, respectively. Let us balance Eqs. (2.10) using molar coefficients   m l l · O2 → k · CO2 + · H2 O. Ck Hl Om + k + − 4 2 2

(2.11)

From Eq. (2.11) it can be seen that the combustion reaction will be complete (a stoichiometric sum of «air-fuel» is formed) if the number of moles of oxygen per 1 mol of fuel material is equal to: Nstoich = k +

Fig. 2.8 Influencing factors for fuel combustion

m l − . 4 2

46

2 Research of the Process of Fuel Combustion in Boilers

Based on the definition of EAC: α=

N N M = = Mteor Nstoich k + 4l −

m 2

,

(2.12)

where N—amount of oxygen moles that took part in the combustion reaction. According to [11, 12], it will be define the stoichiometric mass content of air (per unit mass of fuel) in the mixture: AF =

α m Mair l × × (k + − ), (k · a + l · b + m · c) [O2 ]out 4 2

where a, b, c are the atomic masses of carbon, hydrogen and oxygen, respectively, M air —the molar mass of air, [O2 ]out —the mass percentage of oxygen in the air. Figure 2.9 shows the theoretical dependences of the change in the mass ratio of the amount of air per unit of fuel for different EAC values for some hydrocarbons

Fig. 2.9 Theoretical dependence of the change in air flow per unit of various types of fuels on the coefficient EAC

2.3 Features of the Formation of Stoichiometric Air-Fuel Mixtures

47

Table 2.2 Stoichiometric mass composition of the AFM №

Name

1

Hydrogen

2

Carbon monoxide

3

Methane

4 5

Formula

Mass stoichiometric ratio

Volume stoichiometric ratio

H2

34.21:1

31.04

CO

2.46:1

2.23

CH4

17.20:1

15.60

Ethane

C2 H6

16.06:1

14.57

Propane

C3 H8

15.64:1

14.19

6

Butane

C4 H10

15.43:1

13.99

7

Ethylene

C2 H4

14.75:1

13.38

8

Propylene

C3 H6

14.75:1

13.38

9

Butene

C4 H8

14.75:1

13.38

[11, 12]. Table 2.2 shows the mass stoichiometric ratio «air-fuel» for some gaseous hydrocarbons. In practice, the actual composition of the flue gases contains not only CO2 and H2 O, but other chemical compounds CO, NOx , H2 , O2 , and others. We write the equation of the combustion process, including the instability of the chemical reaction in the boiler furnace: Ck Hl Om + n(O2 + A · N2 + B · CO2 + C · Habc · H2 O) → a · CO2 + b · CO + c · H2 + d · H2 O + e · O2 + f · N2 + g · NOx + h · Ck  Hl  Om  (2.13) In order for calculating EAC, from this equation it is necessary to find n [13]. Let’s write down 5 additional equations: 4 equations of atomic balance (C, H, O, N) and the equation of total molar balance: • carbon balance (C)

k + n · B = a + b + h · x

(2.14)

2 · n · C · Habs + l = 2 · c + 2 · d + h · l 

(2.15)

• hydrogen balance (H)

48

2 Research of the Process of Fuel Combustion in Boilers

• oxygen balance (O)

m + 2 · n + 2 · n · B + n · C · Habs = 2 · a + b + d + 2 · e + g + h · z 

(2.16)

• nitrogen balance (N)

2·n· A =2· f +g

(2.17)

n sum = a + b + c + e + f + g + h.

(2.18)

• total molar balance:

Under normal conditions, it is possible to measure the concentrations of CO, CO2 and NOx in the flue gases behind the gas detector. Based on Eq. (2.13), we can write the ratio between the level of concentration and the number of moles of gas, took part in the combustion reaction: [X ] =

nX , n sum

(2.19)

where [X]—concentration of the test gas, nx —amount of moles of gas, took part in the reaction. Considering that the concentrations of CO and CO2 are known, according to (2.14) and (2.19) it follows: n sum =

k+n· B . [CO] + [CO2 ]

Given that n = 1, it can be found the value nsum . The next step should be to find the amount of moles of water in the exhaust gases. This can be done using 2 methods: – Bretschneider method [14]:

d=

l + 2 · n · C · Habs − h · l   b  , 2 · a·K +1

(the value of the coefficient K depends on the type of fuel and lies where K = b·d a·c in the range from 3.5 to 3.8);

2.3 Features of the Formation of Stoichiometric Air-Fuel Mixtures

49

– Simons method [15]:

d=

n sum − a − b −



l−h·l  2

1−

 + n · C · Habs − e −

g 2

−h

2+2·B+C·Habs A

×

 2 + 2 · B + C · Habs  −2·a−b−2·e−g−h·m +m . × A 

Calculated all the necessary elements (the amount of moles of gases that are the products of combustion) from Eq. (2.16), we can find n: n=

2 · a + b + d + 2 · e + g + h · m − m . 2 + 2 · B + C · Habs

(2.20)

From Eqs. (2.12), (2.14–2.18), (2.20), it can be finally found EAC: α=

2 · a + b + d + 2 · e + g + h · m − m  .  (2 + 2 · B + C · Habs ) · k + 4l − m2

Thus, in order to increase the accuracy of determination of EAC in flue gases, it is necessary to have information on the composition of incoming AFM and products of fuel combustion. The proposed algorithm for calculating EAC can be used in technological processes to increase the efficiency of the process of burning various types of hydrocarbon compounds.

2.4 Methods for Improving the Accuracy of Determining the EAC At the Institute of Engineering Thermophysics of the National Academy of Sciences of Ukraine, an experimental study of the operation of a hot-water boiler using the rbrECOM-KD gas analyzer was conducted [16]. The results of 12 measurements by a gas analyzer with an electrochemical-type sensor were the values of the EAC (α), oxygen concentration (O2 ), carbon dioxide (CO2 ), carbon monoxide (CO), nitrogen oxide (NO) (Table 2.3). Before starting the boiler gas analyzer was measured characteristics of the air supplied to the furnace; the value of the VOC in the air was set at 20.3% (with the fact of incomplete cleaning of gas analysis paths is allowed). Figure 2.10 with the solid line represents the dependence of the oxygen concentration as a percentage of the EAC α by the inverse formula (2.21), the correlation coefficient is 0.997. Dotted lines depict the confidence interval. The expanded uncertainty determined by the formula (2.22) [17] is 0.12% of the oxygen concentration.

Air temperature °C

19

19

19

19

19

20

20

20

22

22

22

22

Measurement number

1

2

3

4

5

6

7

8

9

10

11

12

167

163

158

145

56

173

162

151

141

133

122

102

Fuel gas temperature °C

Table 2.3 Experimental data mode-commissioning boiler type «Victor-100»

0.7

1.4

2.3

2.4

20.3

2.6

4.8

3.5

3.4

3.4

4

4.7

[O2 ] %

2128

726

668

726

5

62

150

137

160

178

222

370

[CO] ppm

71

65

64

62

0

65

61

61

61

60

53

49

[NO], ppm

14.9

14.4

13.7

13.6

0.6

13.5

11.9

12.8

12.9

12.9

12.5

12

[CO2 ] %

94.1

94.1

94.1

94.6

64.7

93.3

93

93.9

94.4

94.8

95.2

95.9

ECE, %

6

6

6

5

35

7

7

6

6

5

5

4

q2 , %

1.03

1.07

1.12

1.13

30

1.14

1.3

1.2

1.19

1.19

1.24

1.29

EAC

50 2 Research of the Process of Fuel Combustion in Boilers

2.4 Methods for Improving the Accuracy of Determining the EAC

51

Fig. 2.10 Dependence of the VOC in the flue gases from the EAC with confidence intervals (the dots indicate the experimental data of the volume concentration of oxygen in the flue gases, the crosses—the calculated values of EAC by the VOC in the air 20.3%)

According to the passport data of the electrochemical sensor, the relative error in determining the concentration of gases is 5% of the measured value (in the experiment it ranges from 0.04% to 0.24% by VOC in the flue gases). The «dots» are the experimental data, the “crosses” are the calculated EAC values at the oxygen concentration in the air of 20.3%. The average deviation of EAC, issued by the gas analyzer, from the EAC with the oxygen concentration in the air of 20.3% was 0.09. [O2 ]out (α) =

21 · (α − 1) , α

(2.21)

where [O2 ]out (α) is the function of the dependence of the VOC in the flue gases from the EAC. For determining the confidence interval [17], the standard deviation s of the measured values from the inverse theoretical dependence (1.6) was first estimated.    s=

n−1  1 · ([O2 ]outi − [O2 ]out (αi ))2 , n − 2 i=1

where [O2 ]outi —the value of the VOC experimentally measured by the gas analyzer, [O2 ]out (αi )—the value of the air concentration in the flue gases (%) according to the formula (1.6) for certain EAC gas analyzers, n—the amount of measurements taken. From the obtained values of S[O2 ]out (α), the confidence interval of the random error ([O2 ]out ) (extended uncertainty U ([O2 ]out )) of the individual value [O2 ]out was estimated: ([O2 ]out ) = U ([O2 ]out ) = t(1−P)/2 (v) · S[O2 ]out (α),

(2.22)

52

2 Research of the Process of Fuel Combustion in Boilers

where t(1−P)/2 (v)—the quantile of the Student’s distribution, for a confidence probability of P = 0.95 and v = n − 2 degrees of freedom. For constructing the calibration characteristics, it is important to choose the mathematical model of the approximation function [18]. For determining the dependence of the values of the concentrations of waste gas components on the EAC, polynomial nonlinear regression (Chebyshev polynomials) was used [19]. The dependence of the volume concentration of carbon dioxide (2.23) with a correlation coefficient of 0.995 (Fig. 2.11a), the mass concentration of carbon monoxide (2.24) with a correlation coefficient of 0.71 (Fig. 2.11b) and the mass concentration of nitric oxide (2.25) with the coefficient correlation 0.82 (Fig. 2.11c) were calculated. Statistical data processing was carried out in the software package MathCAD [20]. [CO2 ]out (α) = −1582 + 5583 · α − 7269 · α 2 + 4181 · α 3 − 898.4

(2.23)

[CO]out (α) = 456632.9 − 1114000.4 · α + 905420.4 · α 2 − 245044.4 · α 3 (2.24) [NO]out (α) = −1502.3 + 4249.1 · α − 3776 · α 2 + 1099.2 · α 3

(2.25)

These dependences and the values of the correlation coefficients show that for the calculated interpolants, based on the calculated EAC, by the VOC in the flue gases and in the air, it is possible to predict the values of the concentrations of the components of the flue gas. Of course, the calculated equations with a small amount of measurement information do not provide sufficient accuracy. Therefore, this approach, for its confirmation, requires a planned experiment with a large number of measurements with multiple observations. Taking into account the above data on the concentration of oxygen in the air, the actual task is to take into account the daily/seasonal change of climatic parameters of the environment and operating conditions when monitoring the process of burning fuel. It is recommended to take into account the amendment to the current value of the VOC in the air, by introducing feedback into the unit for calculating the EAC of the gas analyzer. Figure 2.12 reflects the two-parameter dependence of the amendment (absolute methodological error in the determination of EAC) on its arguments, which is calculated as follows: method α([O2 ], [O2 ]out ) =

[O2 ]out · (21 − [O2 ]) . ([O2 ] − [O2 ]out ) · (21 − [O2 ]out )

The problem of saving fuel resources today is relevant not only for Ukraine, but also for the whole world, therefore, control over the combustion processes in boiler units should be as accurate as possible. On the basis of the conducted research, a new method for determining EAC (55) was proposed, based on the current oxygen concentration in the air, the formula for which is as follows:

2.4 Methods for Improving the Accuracy of Determining the EAC

53

Fig. 2.11 Dependence of the concentrations of the combustion products from the EAC: a—carbon dioxide, b—carbon monoxide, c—nitrogen monoxide

54

2 Research of the Process of Fuel Combustion in Boilers

Fig. 2.12 Two-parameter dependence of the amendment (absolute methodical error in the determination of EAC) on the VOC in the air and the VOC in the flue gases

α =1+

[O2 ]out . [O2 ] − [O2 ]out

(2.26)

The proposed method of improving the accuracy of measuring the EAC in boilers has an extraordinary promise, since it takes into account the methodological measurement error, which is incorporated in the electronic system of calculating devices. For determining the EAC of the gas analyzer, it is necessary to determine the current volume concentration of oxygen in the flue gases and compare it with the value of the VOC in the air, which is considered to be 21% (1.6). However, this parameter is not a constant and depends not only on the height of the object under control, but also on such parameters as humidity and air temperature, and atmospheric pressure. The method for determining the EAC, based on the measurement of the current oxygen concentration in the environment, is implemented using the diagrams shown in Fig. 2.13 [21], where 1—flue gases, 2—an internal oxygen sensor, 3—an analytical unit, 4—a display, 5—an external oxygen sensor. Method #1 (a). An internal oxygen sensor is installed in the opening of the exhaust path, which measures the residual oxygen concentration in the flue gases. An external oxygen sensor is placed in the environment (outside the exhaust path) and measures the oxygen concentration in the air. The signals from both sensors are fed to the analytical unit, which determines the EAC by the formula (2.26). The value of EAC is shown on the display. Method #2 (b). Using a gas analyzer, a preliminary measurement of the oxygen concentration in the environment is carried out. The value of this parameter is stored in the memory of the gas analyzer as O_2’. Next, the oxygen sensor of the gas analyzer is placed inside the exhaust path and conduct measurements of the EAC by the formula (2.26), considering the parameter O_2’ constant.

2.4 Methods for Improving the Accuracy of Determining the EAC

55

Fig. 2.13 Technical methods of implementation of improving the accuracy of determining the EAC: a using two oxygen sensors, b using a single oxygen sensor

Method #1 has a greater accuracy in determining the EAC, since it takes into account the daily/seasonal fluctuation of the oxygen concentration in the air, however, Method #2 does not require an additional number of sensors, being financially advantageous during developing gas analyzers. Measurement of EAC taking into account the current concentration of oxygen in the air allows to significantly improve the accuracy and stability of the determination of EAC, reduce operating costs to optimize the process of fuel combustion. Based on theoretical calculations, it is shown that the application of the proposed method for determining the EAC significantly reduces the methodological measurement error (up to 0.4 EAC value), which contributes to an increase in energy saving during combustion of various types of fuel and reducing operating costs.

2.5 Method for Determining the VOC in the Air Air is a natural mixture of gases, 98–99% consists of nitrogen and oxygen, as well as carbon dioxide, water, hydrogen, inert gases, etc. (Table 2.4). In industry and everyday life, air oxygen is used in fuel combustion processes, while its concentration is one of the most important parameters in determining the optimal combustion regimes. According to Dalton’s law, the percentage of gases entering the air is strictly constant, both in volume and in mass. The ratio of air gases is considered to be the same in any area of the globe, almost unchanged either from the height or the latitude of the terrain. In high altitude mountains, at the equator and in the region of the poles, as well as in the plain, the oxygen content in the air is determined at the same level.

56 Table 2.4 Volume and mass composition of air

2 Research of the Process of Fuel Combustion in Boilers Component

Formula

Volume concentration, %

Mass concentration, %

Nitrogen

N2

78.084

75.50

Oxygen

O2

20.948

23.15

Argon

Ar

0.934

1.29

Carbon dioxide

CO2

31.4 × 10−3

4.6 × 10−2

Helium

Ne

18.18 × 10−4

1.4 × 10−3

10−4

8.4 × 10−5 7.3 × 10−5

Methane

CH4

2×

Helium

He

5.24 × 10−4

Kr

10−4

Krypton

1.14 ×

10−5

Hydrogen

H2

5×

Xenon

Xe

8.7 × 10−6

3 × 10−3 8 × 10−5 4 × 10−5

Thus, the percentage of oxygen in ambient air as a relative value indicates only the stability of the gas composition and the ratio of gases in the air, and cannot be used as a quantitative parameter of oxygen. It can be assumed that, due to the constant percentage of air gases, significant fluctuations in the absolute values of the air gases, including oxygen, are veiled, since it is quite clear that the “proportion” of one percent of oxygen in the gas mixture will be different under different physical conditions of the air (wet or dry, dense or thin, cold or warm). On the basis of these statements and the experiments conducted, a new quantitative value of the oxygen parameter—the partial oxygen density [22, 23], was proposed. Its essence is that the diurnal (seasonal) dynamics of oscillations of the basic meteorological characteristics (temperature, absolute humidity, absolute atmospheric pressure), due to the dynamics of atmospheric processes, is redistribution in time and space of partial density of oxygen in the air, which is manifested in the diurnal (seasonal) periodicity in weather anomalies. Thus, it is safe to say that oxygen concentration (including volume) is not a constant value and can fluctuate significantly not only throughout the calendar year, but also the day, thus affecting the current value of such a value as EAC. The analytical value of the partial density of oxygen (E, g/m3 ) is directly proportional to the atmospheric pressure (P, hPa) excluding partial pressure of water vapor (e, hPa) and inversely proportional to the air temperature (T, K): E = 23.15 × 103 ·

P −e , R·T

where R—specific gas constant for dry air; 23.15%—mass concentration of oxygen in dry air. In this case, the partial pressure of water vapor: e = ϕ · pvap ,

References

57

where ϕ—the humidity of air, %; pvap —the quantity, which can be determined according to recommendations of the Guide to Meteorological Instruments and Methods of Observation (Switzerland) [24]:     pvap P, T  = f (P) · r T  , f (P) = 1.0016 + 3.15 · 10−6 · P − 0.074 · P −1 ,   17,62·T  r T  = 6, 112 · e 243,2+T  , where T  is the temperature of air in the Celsius degrees, °C Transition to the VOC was held with the following equation: [O2 ] =

6, 236 · E · T , P  · MO2

where [O2 ]—VOC in the air, %; P —the atmospheric pressure, mm Hg; MO2 —the molar mass of oxygen, g/mol. The final analytical representation of functional dependence of the VOC in the air on meteorological parameters takes the form [22, 23]:   e(P, T  , ϕ) . [O2 ](P, T  , ϕ) = 20.957 · 1 − P

(2.27)

As a result of the conducted studies, the change in the VOC in the air in the plains was found. It consists in the fact that during daily (seasonal) dynamics and fluctuations of the basic meteorological characteristics (temperature, absolute humidity, absolute atmospheric pressure) (Fig. 2.14), due to the dynamics of atmospheric processes, there is a redistribution in time and space of VOC in the air (Fig. 2.15), which manifests itself in daily (seasonal) periodicity and weather anomalies. The study analyzed changes in the main meteorological parameters (temperature, pressure, humidity) during the year (September 2014—August 2015) in Kyiv (according to the meteorological portal www.wunderground.com at the point of Zhulyany airport, ICAO code: UKKK) and a seasonal change in the VOC in the air in this territory was obtained. The study analyzed changes in the concentration of oxygen in the air during 2014 throughout the territory of Ukraine. The corresponding map with extreme values of the VOC in the air is shown in Fig. 2.16. The identified pattern provides a basis for improving the standard method for the determination of EAC by measuring the current oxygen concentration in the environment. This assumption eliminates the methodological error and improves the measurement accuracy of the specified method. Also, these studies can be used not only in the field of energy, but also in medicine, ecology, agriculture and the like.

58

2 Research of the Process of Fuel Combustion in Boilers

Fig. 2.14 Seasonal change of the main meteorological parameters in Kyiv in the period September 2014—August 2015: a temperature, b pressure, c humidity

Fig. 2.15 Estimated change of VOC in the air in Kyiv from September 2014 to August 2015

References

59

Fig. 2.16 Extreme values of the VOC in the air on the territory of Ukraine in 2014

References 1. Dolinskiy, A.A., Khalatov, A.A., Kobzar, S.G., Nazarenko, O.A., Mescherjakov, A.A.: Using of computer modeling at low-cost modernization of NIISTU-5 boiler. Heat Power Eng. 29(5), 08–91 (2007) 2. Sigal, A.I., Bykoriz, E.I., Puzanov, I.V.: Features of modernization of the boiler NIISTU-5, 41(1), 88–96 (2018). https://doi.org/10.31472/ttpe.1.2019.12 3. Shkarovsky, A.L., Novikov, O.N., Okatiev, A.N.: Energy-ecological principles of fuel combustion process control. Sensors and systems 10, 41–44 (2002) 4. Lambert, K., Welch, S., Merci, B.: The use of positive pressure ventilation fans during firefighting operations in underground stations: an experimental study. Fire Technol. 54(3), 625–647 (2018). https://doi.org/10.1007/s10694-018-0700-8 5. Janco, R., Elesztos, P.: Thermal field simulation of repair threads in a hole in the cover of a pressure vessel by welding using sysweld. In: Beran J., Bílek M., Žabka P. (eds.) Advances in Mechanism Design II. Mechanisms and Machine Science, vol. 44, pp. 191–198. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44087-3_25 6. Kuznetsov, N.V., Alexeeva, T.A., Leonov, G.A.: Invariance of Lyapunov exponents and Lyapunov dimension for regular and irregular linearizations. Nonlinear Dyn. 85(1), 195–201 (2016). https://doi.org/10.1007/s11071-016-2678-4 7. Getsoian, A.B., Theis, J.R., Lambert, C.K.: Sensitivity of three-way catalyst light-off temperature to air-fuel ratio. Emiss. Contr. Sci. Tech. 4(3), 136–142 (2018). https://doi.org/10.1007/ s40825-018-0089-3 8. Zaporozhets, A., Eremenko, V., Redko, O.: Metrological assessment of the indirect method of measuring the concentration of oxygen in the air. In: 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) (2019). https://doi.org/10.1109/CAOL46282. 2019.9019506

60

2 Research of the Process of Fuel Combustion in Boilers

9. Zaporozhets, A.: Development of software for fuel combustion control system based on frequency regulator. CEUR Workshop Proceedings 2387, 223–230. http://ceur-ws.org/Vol-2387/ 20190223.pdf 10. Maurya, R.K.: Characteristics and control of low temperature combustion engines (2018). https://doi.org/10.1007/978-3-319-68508-3 11. Zaporozhets, A.A.: Investigation of stoichiometric “air-fuel” ratio of organic compounds. Part 1. Alkanes. Sc. Based Technol. 22(2), 163–167 (2014). http://doi.org/10.18372/2310-5461.22. 6803 12. Zaporozhets, A.A.: Investigation of stoichiometric “air-fuel” ratio of organic compounds. Part 2. Alkenes, alkynes. Sci. Based Tech. 24(4), 393–399 (2014). https://doi.org/10.18372/23105461.24.7506 13. Silvis, W.M.: The algorithmic structure of the air/fuel ratio calculation. Readout 15, 17–24 (1997) 14. Brettschneider, J.: Berechnung des Luftverhältnisses λ von Luft-Kraftstoff-Gemischen und des Einflusses von Meßfehlern auf λ. Bosch Technische Berichte 6, 177–186 (1979) 15. Simons, W.: Berechnungen zur Bestimmung der Luftzahl bei Ottomotoren. MTZ Motortechnische Zietschrift 46, 257–259 (1985) 16. Babak, V.P., Zaporozhets, A.O., Redko, O.O.: Increasing the accuracy of measuring the air excess coefficient in the boilers using the electrochemical gas analyzer. Industr. Heat Eng. 1, 82–96 (2015). https://doi.org/10.31472/ihe.1.2015.10 17. Babak, V., Eremenko, V., Zaporozhets, A.: Research of diagnostic parameters of composite materials using Johnson distribution. Int. J. Comput. 18(4), 483–494 (2019) 18. Keener, J.P.: Principles of applied mathematics: transformation and approximation (2019). https://doi.org/10.1201/9780429503511 19. Razmjooy, N., Ramezani, M., Estrela, V.V.: A solution for Dubins path problem with uncertainties using world cup optimization and Chebyshev polynomials. In: Iano Y., Arthur R., Saotome O., Vieira Estrela V., Loschi H (eds.) Proceedings of the 4th Brazilian Technology Symposium (BTSym’18). BTSym 2018. Smart Innovation, Systems and Technologies, vol. 140, pp. 45–54. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-16053-1_5 20. Ochkov, V., Orlov, K.: My first power engineering mathcad-calculation. In: Rogalev N. (ed.) Thermal Engineering Studies with Excel, Mathcad and Internet, pp. 65–76. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26674-9_4 21. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Technical provision of diagnostic systems. In: Diagnostic systems for energy equipments. Stud. Syst. Decis. Control 281, 91–133 (2020). https://doi.org/10.1007/978-3-030-44443-3_4 22. Zaporozhets, A.O., Redko, O.O., Babak, V.P., Eremenko, V.S., Mokiychuk, V.M.: Method of indirect measurement of oxygen concentration in the air. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 5, 105–114 (2018). https://doi.org/10.29202/nvngu/2018-5/14 23. Babak, V.P., Mokiychuk, V.M., Zaporozhets, A.A., Redko, A.A.: Improving the efficiency of fuel combustion with regard to the uncertainty of measuring oxygen concentration. EasternEuropean Journal of Enterprise Technologies 6(4), 54–59 (2016). https://doi.org/10.15587/ 1729-4061.2016.85408 24. Guide to Meteorological Instruments and Methods of Observation World Meteorological Organization, 8 (2008)

Chapter 3

Hardware and Software Implementation of Modules of the System of the Fuel Combustion Control Process

3.1 Generalized Structure of the Combustion Control System in Boilers Currently, there are a significant number of schemes for the implementation of automatic control of the ratio of the AFM. So, in work [1] the possibility of forming an information signal for influencing a butterfly valve installed on the air path after the blow fan is described by adjusting the frequency of the blow fan when the angle of opening of the butterfly valve exceeds the specified limits. This scheme is characterized by the absence of large inrush currents and a decrease in energy consumption due to a decrease in the frequency of the power supply of the blow fan engine. At the same time, the method is implemented by slow control of the operation of technological electrical equipment, does not allow to quickly respond to changes in the quantity and quality of fuel entering the burners. In [2], a scheme of regulating the mode of fuel combustion in the boiler furnace is given by measuring the carbon monoxide content using a sensor in the exhaust path (Fig. 3.1). This method generates an output signal from the gas analyzer, which is proportional to the carbon monoxide content in the flue gases, and together with the output signal of the generator creates a control signal supplied to the frequency converter unit of the electric motor of the exhaust fan and (or) the blow fan to the boiler furnace, constantly maintaining the concentration of carbon monoxide in the flue gases at the level of 0.1–0.2%. The disadvantage of this method is the inconsistency of the flow rate of gas, air and vacuum with the inertia of exhaust and blow fans, creates the possibility of short-term release of air pressure and vacuum beyond the permissible limits, as a consequence— the occurrence of an emergency, and the analysis of carbon monoxide content in the flue gases does not prevent the formation of products incomplete combustion (H2 , CH4 , CO, C) and reduce emissions of harmful substances into the atmosphere [3]. The proposed method is based on the task of improving the method of automatic control of the process of fuel combustion in boilers by continuously measuring the © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 A. O. Zaporozhets, Control of Fuel Combustion in Boilers, Studies in Systems, Decision and Control 287, https://doi.org/10.1007/978-3-030-46299-4_3

61

62

3 Hardware and Software Implementation of Modules of the System …

Fig. 3.1 Block diagram of the system of automatic control of the combustion process for largecapacity boiler units ECO-3

oxygen content in flue gases using an oxygen sensor, controls the combustion of combustible materials regardless of the change in the amount of gas entering the boiler furnace, provides significant energy savings to the boiler room system. The task is solved by the fact that in the automatic control system of the combustion process in boilers by measuring signals, the gas flow is recorded, using oxygencontrolled blow and exhaust fans, the oxygen content in the flue gases is continuously measured using an oxygen sensor located at the beginning of the gas path of the chimney. Next, the measurement results of the probe α-indicator are measured by a predetermined stoichiometric AFR, and the fuel supply to the burner is corrected by feedback signals from the oxygen sensor, while the EAC in the flue gases is maintained at a constant level (α = 1.1–1.2). Types of practical developments, which allow today to determine the EAC of flue gases for various types of machines in real time are given in [4–6]. The using of an oxygen sensor at the beginning of the exhaust path has several advantages over traditional gas analyzers: an increase in the accuracy of measuring residual oxygen concentration, the lack of sampling and sample preparation systems, stable operation and long service life, ease of replacing parts without dismantling, adaptation to installation on various types of heat units.

3.1 Generalized Structure of the Combustion Control System …

63

The using of a digital alpha-indicator allows real-time monitoring of the EAC value in the flue gases, with high accuracy to quickly make changes to the mode of operation of the boiler burner, resulting in maximum efficiency of the boiler plant. Correction of the fuel supply to the burner by the feedback signal from the oxygen sensor allows you to maintain the stoichiometric air-to-fuel ratio in the boiler furnace [7, 8], reduce toxic emissions to the atmosphere and increase the boiler efficiency by preventing air deficiency or its excess in the flue gases. The theoretical values of the stoichiometric «air-fuel» ratio for some types of fuel are shown in Chap. 2 of this book. The scheme of the method of automatic control of the combustion process in boilers, based on the use of an oxygen sensor and a digital probe α-indicator, is implemented using the block diagram shown in Fig. 3.2. The proposed method is implemented as follows. The required AFR during the operation of the boiler is set by the control panel and compared with the current value received from the oxygen sensor. The frequency regulator controls the electric motor of the blow fan to the boiler furnace by changing the frequency of the output power network. The oxygen sensor located at the beginning of the exhaust path continuously analyzes and records the oxygen content in the flue gases, and generates an information signal, the voltage of which characterizes the EAC α. The digital probe α-indicator produces the signal received from the broadband oxygen sensor on the LED array, and together with the previously entered data about the applied fuel, forms a signal applied to the frequency regulator unit, which in turn changes the fan operation mode to correct the air supply to the burner, supporting thus EAC α ≈ 1. The metering station of harmful substances is used for analysis and correction with the help of a frequency regulator of the content of toxic substances in the flue gases. For this method of automatic control of the process of fuel combustion in boilers, a patent of Ukraine for an invention has been received.

Fig. 3.2 Block diagram of the technological process of regulating the ratio of AFM for the burner with feedback on the oxygen sensor signals (BF—blower fan, FR—frequency regulator, AI—alpha indicator, OS—oxygen sensor, MU—hazardous substances metering unit)

64

3 Hardware and Software Implementation of Modules of the System …

The algorithm of the automatic control system for the process of fuel combustion in boilers by adjusting the ratio of AFM for the burner with feedback from the oxygen sensor signals is shown in Fig. 3.3. A special feature of the algorithm is the using of frequency control, by which the amount of air supplied to the combustion zone varies smoothly. The sensor measures the concentration of oxygen in the flue gases and the system processes the receiving information. A control signal is generated depending on the received EAR value— the fan rotation frequency is reduced or increased by 0.1 Hz (for α > X or α < X,

Fig. 3.3 The algorithm of functioning of the monitoring system based on the frequency regulation of the ratio of AFM for the burner with feedback on the oxygen sensor signals

3.1 Generalized Structure of the Combustion Control System …

65

respectively, where X is the measured EAC value). After the steady-state operation of the fan is established, the system is questioned again. The dependence of the blower fan speed on the EAC is determined by the following system of equations: ⎧ ⎨ f 0 −  f at X > α0 , f (α) = f 0 at X = α0 , ⎩ f 0 +  f at X < α0 , where f 0 —the operating speed of the blow fan, Δf —the step of change of the rotational speed, X—the current value of the EAC, α 0 —the working value of the EAC. The features of the developed system make it possible to use it in the system of automatic control of the process of burning fuel in small and medium-capacity boilers. Figure 3.4 shows a block diagram of the functioning of such a system [9]. The main purpose of the control system is to regulate the speed of the fan motor drive, so that in the furnace of the boiler to maintain an optimal combustion mode, that is, to provide the most favorable conditions for complete fuel combustion. For

Fig. 3.4 Block diagram of the system of automatic control of the process of fuel combustion in low and medium power boiler units on the basis of an oxygen sensor

66

3 Hardware and Software Implementation of Modules of the System …

this, the system, based on information received from its primary sensors (oxygen concentration, temperature and vacuum sensors), supplies the required amount of air to the furnace. The application of the proposed automatic control system will allow maintaining energy-efficient operation of the boiler at a high level, achieving ~95% of ECE.

3.2 Means of Monitoring the Process of Fuel Combustion Based on Oxygen Sensor The use of gas-air ratio regulation with automatic adjustment of the fan rotation speed depending on the natural gas supply will ensure low-toxic combustion of natural gas with an insignificant emission of nitrogen oxides. A broadband oxygen sensor (lambda probe) [10, 11] manufactured by Bosch became the basis for creating an experimental sample probe α-indicator (Fig. 3.5a). To date, lambda probes are widespread in the automotive industry due to the ever-growing stringent standards for exhaust emissions, and are often installed with catalytic converters. One oxygen sensor is installed in the exhaust path directly in front of the catalyst. The use of the second sensor directly after the converter in the exhaust system makes it possible to maximize the efficiency of the system for regulating EAC in the flue gases and to ensure complete and low toxicity of burning the AFM. Optimum performance is achieved by using the stoichiometric AFR (determined depending on the type of fuel). The deviation from the optimal ratio of AFM leads to a deviation in the levels of toxic gases. Excess fuel leads to the formation of products of incomplete combustion and carbon monoxide (CO). Excess air leads to an increase in the level of nitric oxide (NOx ) and causes excessive heat consumption for heating the air [12]. The oxygen sensor distinguishes the deviations of the AFR from the stoichiometric and through the electronic regulation system changes the quantitative content of excess oxygen in the flue gases.

Fig. 3.5 Oxygen wideband sensor (lambda probe): a appearance; b block diagram (1 nernst cell, 2 reference cell, 3 preheater, 4 diffuse gap, 5 pumping cell, 6 air-fuel path)

3.2 Means of Monitoring the Process of Fuel Combustion Based …

67

The use of oxygen sensors of this type in electronic control systems the AFR for burners type PBGM will improve the efficiency of fuel combustion. An oxygen sensor based on the Nernst cell principle was used to implement the monitoring and control system for the combustion process in small and medium capacity boilers. A significant advantage of such sensors is the additional oxidation of CO on the sensor surface, containing ZrO2 in its composition. This makes it possible to obtain information on the actual oxygen concentration in the combustion products. The disadvantage of their use is the impossibility of detecting chemical underburning in the zone α > 1, however, as shown by experimental data, the support of the functioning of the boiler with α ≥ 1.1–1.15 excludes the possibility of formation of CO at more than 200 ppm. The sensor design provides for the presence of two chambers (cells): measuring and pumping (Fig. 3.5b). Through an opening in the wall of the pumping cell, the flue gases enter the measuring chamber (diffusion gap) in the Nernst cell. This configuration is characterized by the constant maintenance of the stoichiometric AFR in the diffusion chamber. The electronic modulation circuit of the supply voltage maintains the composition of the mixture corresponding to α = 1 in the measuring chamber. For this, the pumping cell with a lean mixture and excess oxygen in the flue gases removes oxygen from the diffusion gap to the external environment, and with an enriched mixture and insufficient oxygen, pumps ions oxygen from the environment in the diffusion gap. The direction of current during pumping oxygen in different directions is also different (Table 3.1). Thus, if conventional sensors use the voltage of the Nernst cell to directly measure and determine one of two states (α > 1 or α < 1), then the wide-band oxygen sensors use a special circuit controlling the pumping current of the pumping cell. The magnitude of this current determines the EAC in the flue gases. Since the operation of the oxygen sensor is no longer dependent on the gradation in the operation of the Nernst cell, the EAC α can be measured in a wide range from 0.7 to 4. Accordingly, the control of the boiler operation by the value of the EAC α can occur throughout the whole range of values and modes, and not only at a point close to α = 1. The use of a broadband oxygen sensor in the monitoring and control system of the fuel combustion process has several advantages over traditional gas-analyzing devices: the lack of a system for sampling and sample preparation, rapid measurement of oxygen concentration (0.1–0.2 s), uninterrupted operation, long service life, easy installation on various types of heat units. A general view of the control unit of the developed system based on an oxygen sensor is shown in Fig. 3.6. According to these developments, 5 patents of Ukraine were obtained. In Table 3.2 shows the main technical characteristics of the developed control unit for the process of burning fuel. The reduced measurement error of the EAC was determined by the formula: δxr me = δmme / X N ,

−3

0.753

I, mA

α

0.818

−2

0.818

−1 0.948

−0.5 1

0 1.118

0.5 1.266

1 1.456

1.5

Table 3.1 The dependence of the current strength at the output of the broadband oxygen sensor from the EAC α 1.709

2

2.063

2.5

2.592

3

68 3 Hardware and Software Implementation of Modules of the System …

3.2 Means of Monitoring the Process of Fuel Combustion Based …

69

Fig. 3.6 Photo of the control unit of the oxygen content in the flue gases of the control system of the fuel combustion process in the boiler: 1 oxygen sensor, 2 alpha-indicator, 3 controller, 4 USB interface

Table 3.2 Main technical data of the fuel combustion control system

Parameter

Value

Output signal of the measuring probe, V

+0.1 … +5.0

Recall (time delay of indication) for 50% step perturbation, s

0.1 … 0.3

Initial preparation time for measurements, s

≤30.0

Measuring range of the parameter, α

0.5 … 1.5

Relative error, %

3

Indication of measurement results

LED

Cable length, m

≤5

Ambient temperature at relative humidity up to 80%: Display unit, °C

5 … 50

Boxes of the measuring probe, °C

5 … 70

Conditions at the measurement point: Ambient temperature, °C

50 … 250

Flow rate, m/s

≤15

Pressure, Pa

≤ + −500

where δ mme —the marginal measurement error, X N —the normalizing value. The margin of error is determined from the following relations [13–16]:  xmme =

xran

2  (xran )2 + xsys ,

 = tα (n) ·

 1 (xi )2 , n · (n − 1) i=1 n

70

3 Hardware and Software Implementation of Modules of the System …

xsys = tα (n) ·

δx · X N 3

2

+

d √ 12

2 ,

where x ran —the random measurement error, x sys —the systematic measurement error, t α (n)—the Student coefficient, d—the unit scale value of the instrument. In general, the control system allows to: • optimize the combustion of fuel, taking into account actual conditions, operating conditions of the boiler and fuel characteristics; • reduce fuel consumption at least 10%; • reduce nitrogen oxide emissions to 40%; • reduce the level of carbon monoxide emissions to 50%; • increase the efficiency of the boiler at least 5%; • simply the work of staff.

3.3 Formation of AFM of a Given Composition Based on Frequency Regulators Analysis of the structure of energy losses in the production, distribution and consumption of electricity indicates that the main part of the losses (up to 90%) is in the area of energy consumption. Considering that more than 60% of the total energy produced is consumed by electric drives, it can be concluded that energy saving tasks are highly relevant in the design, operation, and modernization of electric drive systems. Large distribution of electric drives received at the HCS. At the same time, the introduction of energy-saving technologies in this area allows to get not only economic, but also social effect. One of the ways to increase efficiency is to save electricity during the boiler is running. Due to the fact that the main consumer of electrical energy in the operation of a heating boiler is blow and exhaust fans, one can single out an urgent task—the development and implementation of an energy-saving algorithm for controlling the performance of blow and exhaust fans [17, 18]. Figure 3.7 shows the structural diagram of the boiler. In order to ensure the process of fuel complete combustion, it is necessary to supply a sufficient amount of air, while EAC in the combustion furnace is also unacceptable. In practice, the following α values are oriented for various fuels: for pulverized and gaseous fuels—1.03 … 1.06, for liquid fuels (fuel oil)—1.2 … 1.25, for solid fuels—1.3 … 1.65 (condition 1). To ensure the conditions of a normal furnace mode, it is necessary to have a small constant vacuum in the upper part of the furnace (up to 20–30 Pa) (condition 2).

3.3 Formation of AFM of a Given Composition Based …

71

Fig. 3.7 Block diagram of a double-circuit gas boiler

In cases where the natural thrust is insufficient to overcome the air and gas resistances of the boiler, special mechanisms are used (blow and exhaust fans) by which the so-called balanced artificial thrust is carried out [19–21]. Failure to comply with conditions 1 and 2 leads to emergency situations in the operation of the boiler: leakage of the flame, separation of the flame and reduction of the efficiency of fuel combustion. At the same time, the ambient air temperature during the heating period varies over a wide range; therefore, it becomes necessary to control the fuel supply in order to ensure the specified coolant temperature. In connection with the foregoing, during the operation of the heating boiler, it becomes necessary to regulate the air supply and vacuum. At the same time, the control process must provide the necessary modes in accordance with the operating map of the heating boiler, since even a short-term exit of the air pressure and vacuum pressure parameter beyond the set limits can lead to an emergency situation. Let us consider the basic methods of regulating the modes of operation of exhausters and fans: 1. 2. (a) (b) 3. 4. (a) (b) 5. 6.

throttling; changing the design characteristics of the fan: changing the angle of attack of the fan blades; change in the number of fan blades; intermittent regulation; speed control of the rotor of the fan motor: reconfiguration of the magnetic field; parametric regulation; variators and slip clutches; DC motors;

72

3 Hardware and Software Implementation of Modules of the System …

7. changing the frequency of the supply voltage of the electric drive of the blow and exhaust fans. The simplest and most common method of fan regulation is throttling [22, 23]. In this case, the fan idle spends some of its power, overcoming the resistance of the air supplied. The additional equipment required in this case has low reliability, is difficult to regulate and consumes a lot of energy. Thus, the throttle control technology (using the gate): uneconomical, requires constant monitoring by the staff on duty, allows large fluctuations, increases the level of equipment wear. The most effective and economical method of controlling the performance of fans is a smooth change in their rotational speed, which is achieved by using a frequencycontrolled electric drive. Changing the frequency of the mains is a task solved by frequency converters. Modern advances in electronics allow frequency change with an accuracy of 0.01%. The use of frequency-controlled electric fans allows to achieve the following advantages compared with traditional methods: • • • •

reduction of energy consumption by an average of 30–40%; maintaining specified air flow rates; elimination of starting currents and motor overloads; reduction of mechanical wear of equipment and reduction of costs for its maintenance and repair; • increase the service life of contact-switching equipment and reduce the likelihood of engine failure. The disadvantages include the fact that it is necessary to take into account the inertia of blow and exhaust fans in order to ensure reliable operation. For solving the problem of effective regulation of fan operation modes, an algorithm is proposed that combines fan performance control both by changing the rotational speed and by changing the position of the flap. Figure 3.8 shows a flowchart that implements the proposed method for a boiler fan. Fig. 3.8 The block diagram of the algorithm for regulating the ratio of AFM with the blow fan and feedback on the oxygen sensor signals

3.3 Formation of AFM of a Given Composition Based …

73

The configuration consists of a fan connected to a frequency converter, a valve position sensor, a regulator connected by an oxygen sensor, which measures the current concentration of excess oxygen in the flue gases, a valve and the setting device. Thus, the control system allows to compensate for rapidly changing disturbances by regulating the valve, and slow disturbances by compensating for the rotational speed of the fan impeller. The control of the air-fuel system of the boiler plant aims to maintain a stable vacuum in the upper part of the furnace, which is a necessary condition for the stability of the combustion process. The disturbing effect here is a change in air flow caused by a change in the technological mode of combustion, mainly to regulate the thermal capacity of the boiler plant. Until recently, the only method of regulation was the control of the air damper (gate). The introduction of energy-saving technologies has led to the widespread use of transistor converters for the implementation of frequency control of an asynchronous fan drive. In this case, an attempt to work out quick-change processes leads to the inclusion of emergency modes in the frequency controller (due to the high inertia moment). Increased fan performance leads to the danger of exceeding the critical torque of an asynchronous drive and, as a result, buckling. The way out of this situation is to preserve the damper mechanisms for parrying the rapidly changing processes of putting it into the open state under stable conditions and adjusting the frequency drive to preserve the effect of energy saving. To implement the proposed algorithm, the FRC regulator (Fig. 3.9) was used to control the amount of air supplied to the burners device (type PBGM) to support the AFR by changing the rotational speed of an asynchronous three-phase electric motor. The amount of air supplied to the burner depends on the position of the regulating fuel body and is programmed with the mode adjustment for 8 values (points) of AFR. Algorithms of FRC functioning The proposed FRC can operate in the following modes: • Operation; • Setting the parameters of the regulator; • Set the AFR; Fig. 3.9 Frequency ratio controller (by PROMEL)

74

3 Hardware and Software Implementation of Modules of the System …

• View; • Correction of the AFR; • Recovery. «Operation» mode is the main operating mode, in which the regulator automatically enters when the supply voltage is turned on. In this mode, the regulator automatically starts the electric motor, conducts a “poll” of the position sensor of the fuel regulator, calculates the current value of its position from the data obtained. With the help of the programmed dependence in the form of a piecewise linear curve, for 8 values (points) of optimal correspondence of the rotational speed of the electric motor to the position of the fuel regulator (the ratio “air consumption–fuel consumption”), the regulator calculates the value of the rotational speed of the fan electric motor. The calculation of the motor speed is displayed on a digital indicator and is used as an automatically supporting value. Thus, the AFR is maintained according to the principle «air follows fuel» . Visual control over the operation of the control outputs of the regulator is carried out using a red V1 LED. The pulsating glow of the LED indicates the change in frequency at the control outputs of the regulator. The visual control over the operation of the regulator is carried out using a digital controller, which displays the value (change in value) of the rotational speed of the electric motor (Fig. 3.10). The mode « Setting the parameters of the regulator » is intended for input and writing into the non-volatile memory of the parameters of the regulator. The mode “Setting the parameters of the regulator” consists of the following items: • setting the motor speed; • setting the motor stop speed; • setting the resistance value of the potentiometer—sensor position of the fuel regulator. Entering the “Setting the parameters of the regulator” mode is performed from the « Operation » mode by simultaneously pressing and holding the FU, < , > buttons for no longer than 3 s. When entering the “Setting the parameters of the regulator” mode, the name of the first chapter of the “Setting the parameters of the regulator” mode is displayed on the digital indicator in the form of US.SR. Using the control buttons < and > it can be selected the desired section. Selection of sections occurs in a «circle» , in the forward and reverse directions. At the same Fig. 3.10 The front panel of FRC

3.3 Formation of AFM of a Given Composition Based …

75

time on the digital indicator is set to display the name of the selected section in the form: • US.SR—setting the speed of the electric motor; • US.SO—setting the speed of the motor stop; • U.gos—setting the potentiometer resistance value. For entering the selected section, you must briefly press the WR button. The algorithm of work with the regulator in the mode “Setting the parameters of the regulator” is shown in Fig. 3.11. «Set the AFR » mode For economical fuel combustion, it is necessary to optimally maintain the ratio of air consumption in relation to fuel consumption. Fuel consumption is proportional to the position of the regulatory body on the fuel path, and the blow fan motor speed characterizes the amount of air supplied to burn a given amount of fuel.

Fig. 3.11 Algorithm of the FRC in the mode «Setting the parameters of the regulator»

76

3 Hardware and Software Implementation of Modules of the System …

Thus, the operation of the regulator with the maintenance of the AFR is based on measuring the position of the regulatory body, calculated according to the programmed dependence in the form of a piecewise linear curve corresponding to the optimal value of the frequency of rotation of the blow fan motor. The calculated value of the rotational speed of the electric motor is used as the norm for automatically maintaining the optimal combustion mode. If you create a graph for 8 points, as shown in Fig. 3.12, it can be seen that the AFR, which is guided by the frequency regulator, looks like a broken line formed by segments, the beginning and end of which are obtained experimentally. The resulting broken line is close to the real curve, reflecting the dependence of the optimal AFR. The regulator in each segment of the polygonal line maintains the dependence of Qair (equals the rotational speed of the electric motor) from Qf (position of the regulatory body), is described by a linear equation of the type y = kx + b, with the coefficients k and b on each segment being different. Entering to the «Set the AFR» mode is carried out by simultaneously pressing and holding the control buttons < and > for at least 3 s. If the «Set the AFR» mode is opening, the digital indicator displays the number of the first point of the AFR curve in the following format: -00-, where 0 is the number of the first point of the AFR. At the same time, the V4 LED lights up continuously. To adjust the AFR at the first point, briefly press the control button WR. In this case, the regulator automatically determines from the previously programmed AFR

Fig. 3.12 Graphical representation of the dependence of in air flow from fuel in AFM

3.3 Formation of AFM of a Given Composition Based … Table 3.3 Frequency range of FRC

77

Point’s number

Markings on the digital display

Frequency range, Hz

1

0.f0

from 0 to 50

2

1.f1

from f0 to 50

3

2.f2

from f1 to 50

4

3.f3

from f2 to 50

5

4.f4

from f3 to 50

6

5.f5

from f4 to 50

7

6.f6

from f5 to 50

8

7.f7

from f6 to 50

the speed of the electric motor for the current state of the fuel regulating body and displays the value in the following format on the digital indicator: 0.XX.X, where 0 is the number of the first point in the AFR, XX.X—current frequency value, Hz. Using the control buttons < and > you can change the current value of the motor speed for a given point of AFR in the range specified in Table 3.3 from fmin to fmax , where fmin = 0—the minimum value of the rotation frequency of the electric motor, Hz; fmax = 50—the maximum value of the rotation frequency of the electric motor, Hz. If the control button < is pressed, the motor speed decreases; if the control button> is pressed, the motor speed increases. At the same time, the digital indicator shows the change in the value of the motor speed, and the pulsating indication of the LED V1 reflects the change in frequency at the control outputs of the controller. After ignition of the burner in the low-combustion mode, changing the value of the rotational speed of the electric motor and focusing on the readings of a gas analyzer or oxygen sensor with installed software, it is necessary to establish the optimal mode of fuel combustion. After setting the required value of the motor speed for a given point of the AFR, it is necessary to record (store) a new frequency value. For saving the new value of the motor speed for this point of the AFR, it must be briefly pressed the control button WR. In this case, the new value of the electric motor rotation speed is automatically saved for a given point of AFR and the regulator automatically moves to the next point in the AFR curve. «View» mode is designed to view the stored values of the motor speed for each point in the AFR without the possibility of editing the recorded values. Entrance to the «View» mode is carried out from the «Operation» mode by pressing and holding the FU button for at least 3 s. If entering the «View» mode, the digital indicator reflects the expression List. For viewing the recorded frequency values, briefly press the WR control button, while the controller displays on a digital indicator the frequency value recorded for the first point in the following format: 0.XX.X—where 0 is the number of the first point in the AFR; XX.X—recorded frequency value in Hz for a given point.

78

3 Hardware and Software Implementation of Modules of the System …

Viewing the recorded values of the motor rotation frequencies for all points, the AFR is carried out using the control buttons < and > in a circle, in the forward and backward directions. For viewing the next recorded motor speed value, briefly press the control button > . In this case, the value of the rotational speed of the electric motor for the next point of AFR will be displayed on the digital indicator. For viewing the pre-recorded value of the motor speed, briefly press the control button < . At the same time, the value of the rotational speed of the electric motor for the previous point of AFR will be displayed on the digital indicator. Exiting the «View» mode is carried out to the «Operation» mode by pressing the FU control button. The controller operation algorithm in the «View» mode is shown in Fig. 3.13. «Correction of the AFR» mode is intended for correcting the rotational speed of the electric motor for each point in the AFR with the ability to edit previously recorded values. Entering the «Correction of the AFR» ratio is carried out from the «Operation» mode by simultaneously pressing and holding the FU and WR control buttons for at least 3 s. Upon entering the « Correction of the AFR » mode, the regulator automatically determines the closest point the AFR from the current position of the fuel regulator and reflects its value on the digital indicator in the following format: –0X–, where

Fig. 3.13 The algorithm for the FRC operation in the «View» mode

3.3 Formation of AFM of a Given Composition Based …

79

X is the number determined by the regulator of the AFR points. In this case, the V3 LED is in active mode. For correcting the speed at current point of the AFR, it necessary briefly press the control button WR. In this case, the digital indicator displays the current value of the electric motor rotation speed for a given position of the fuel regulatory body in the following format: X.XX.X, where X is the number of the AFR point determined by the regulator; XX.X is current value of frequency, Hz. Using the control buttons < and > it is possible to change the current value of the rotational speed of the electric motor for a given AFR point in the range from fmin to fmax , where fmin is the stored frequency value for this point in the AFR; fmax is the stored frequency value for the next point in the AFR. The algorithm for operation of FRC in the «Correction of the AFR» mode is shown in Fig. 3.14. If the control button < is pressed, the motor speed decreases; if the control button is pressed> , the motor speed increases. At the same time, the digital indicator shows the change in the value of the motor rotation frequency, and the pulsating glow of the LED V1 reflects the frequency change at the control outputs of the controller.

Fig. 3.14 The algorithm for the FRC operation in the «Correction of the AFR» mode

80

3 Hardware and Software Implementation of Modules of the System …

After setting the required value of the motor speed for a given point of the AFR, it is necessary to save the new frequency value. For saving the new value of the motor speed for this AFR point, it must be briefly pressed the control button WR. This automatically saves the new value of the motor speed for a given AFR point and the regulator automatically switches to «Operation» mode. In order for exiting the «Correction of the AFR» mode without saving the changes made, it must be briefly pressed the FU control button. In this case, the regulator automatically switches to the «Operation» mode with the previously set value of the electric motor rotation speed for a given AFR point. If you exit the «Correction of the AFR» mode, LED V3 fades. «Recovery» mode is intended for automatic restoration of the initial parameters of the regulator’s operation, installed at the manufacturer. The values of the output parameters of the controller should correspond to those given in Table 3.4. Entering the «Recovery» mode occurs from the «Operation» mode by simultaneously pressing and holding the control b uttons , WR for at least 3 s, until a sound signal appears and a symbol expression is displayed on the digital indicator: ===. For changing the current controller operation parameters to the factory ones, it is necessary to briefly press the WR control button, at the same time, the output parameters are automatically set and recorded in accordance with Table 3.4, after which the controller automatically switches to the «Operation» mode. In order for exiting the «Recovery» mode without saving changes, it is necessary to briefly press the FU control button, while the controller automatically switches to the «Operation» mode with the current operating parameters. Table 3.4 Factory settings of the FRC

AFR point number

Designations on the digital indicator

Value of the motor speed, Hz

1

0

6.30

2

1

12.50

3

2

18.80

4

3

25.00

5

4

31.30

6

5

37.50

7

6

43.80

8

7

50.00

3.4 Software for Combustion Control System

81

3.4 Software for Combustion Control System The programmer of the ratio of the air-fuel mixture of the burner with feedback on the signals from the oxygen sensor is made in the technical programming environment LM Programmer and works with Windows operating systems (XP, Vista, 7, 8) and oxygen sensors made by Bosch through a special controller [24, 25]. The developed software product is used to update the firmware, change the type of working fuel, and program the analog outputs (1 and 5 V). For starting the software environment, it is necessary to connect the oxygen sensor through the connecting cable using the COM port to the PC (if COM port is absent, the connecting cable can be used with a special COM-to-USB adapter) and plug it into the electrical network. The shortcut for starting the program after installation will be located in the «Programs» section of the «Start» panel. The working environment of the software product shown in Fig. 3.15. In the main window of the software environment, the user has the opportunity to change the name of the connected sensor and the type of fuel, according to which it is necessary to conduct monitoring. Standard settings include the following types of fuel:

Fig. 3.15 Workspace of LM programmer

82

• • • • • •

3 Hardware and Software Implementation of Modules of the System …

propane; methanol; ethanol; compressed natural gas; diesel; manual setting.

Manual tuning involves the self-introduction of the necessary stoichiometric airfuel value. Some theoretical values of this value are calculated in [8]. The software product has the ability to connect up to two analog outputs: 1–5 V (for a broadband oxygen sensor); 2–1 V (for a conventional planar oxygen sensor). The working environment of the software for connecting via the 5 V analog output is shown in Fig. 3.16. In this window, the user has the opportunity to calibrate the software product according to his own needs using the appropriate voltage values (ranging from 0 to 5 V) and the coefficient α or the required stoichiometric AFR (in the volume ratio). The type of dependence of the output voltage on the coefficient α is linear. By pressing the Advanced key, you can set the response speed of the sensor according to the set values: 1/12 s, 1/6 s, 1/3 s. Also, if desired, you can set the value of the output voltage for heating the sensor and generating information about false functioning (in the range from 0 to 5 V) (Fig. 3.17). The working environment of the software during connecting via an analog output of 1 V shown in Fig. 3.18. In this window, the user also has the opportunity to calibrate the software product according to 2 parameters: the voltage value (ranging from 0 to 1 V) and the coefficient α or the required stoichiometric AFR (in the volume ratio). The type of dependence of the output voltage on the coefficient α has a stepwise character. Fig. 3.16 Programmer working environment during connecting to 5 V analog output

3.4 Software for Combustion Control System

83

Fig. 3.17 Configuring the programmer with entering Advanced settings

Fig. 3.18 Programmer working environment during connecting to 1 V analog output

Pressing the Advanced key brings up the same settings and the operating mode with an analog output of 1 V. Pressing the Factory Defaults key resets the operating parameters according to the factory settings. Visualization of the process of controlling the ratio of AFM for a burner with feedback from oxygen sensor signals was performed in the technical programming environment LogWorks 3 (Fig. 3.19) and works in the environment of Windows operating systems (XP, 7, Vista, 7, 8) with oxygen sensors manufactured by Bosch through special controller [25].

84

3 Hardware and Software Implementation of Modules of the System …

Fig. 3.19 LogWorks 3 monitoring software work environment

The display mode of current information on the content of residual oxygen in the flue gases is possible in 3 display modes: (1) arrow; (2) linear; (3) columnar (Figs. 3.20 and 3.21) with the display of both information on the AFR (Figs. 3.19 and 3.20) and EAC (Fig. 3.22).

Fig. 3.20 Indication modes of the AFR in the flue gases (oxygen sensor is not in the working environment): a arrow; b linear; c columnar

Fig. 3.21 Indication modes of the AFR in the flue gases (oxygen sensor is in the working environment): a arrow; b linear; c columnar

3.4 Software for Combustion Control System

85

Fig. 3.22 Indication modes of the EAC in the flue gases (oxygen sensor is in the working environment): a arrow; b linear; c columnar

The software user can choose the type of indication of the monitoring parameter himself, while he can use from 1 to 3 types at the same time (Fig. 3.22). As part of a software product, the user can not only control the amount of residual oxygen in the flue gases, but also record the changes in this parameter using the internal Real-Time Log environment. The working environment of Real-Time Log shown in Fig. 3.23. The program controls the volume AFR depending on the duration of the analysis of the flue gases composition. The resulting graph can be obtained during the entire monitoring time and saved to a file with the .log extension for further analysis and information processing. This software has a direct connection with the developed digital α-indicator, which allows controlling EAC with the help of a frequency regulator, that is, it is possible to correct the operating modes of the boiler by introducing feedback on the signals of the oxygen sensor. Figure 3.24 reflects the interaction of the α-indicator with the developed software at various values of the AFR.

Fig. 3.23 Real-Time log workspace

86

3 Hardware and Software Implementation of Modules of the System …

Fig. 3.24 Interaction of the α-indicator and the developed software product under various modes of operation of the boiler unit: a enriched mixture; b a mixture close to stoichiometric; c learn mixture

The adaptation of these software tools allows to use them both directly in the automatic control system of the fuel combustion process, and in the control system (gas analyzing device) of the composition of the flue gases of the boiler units.

References 1. Liu, H., Chaney, J., Li, J., Sun, C.: Control of NOx emissions of a domestic/small-scale biomass pellet boiler by air staging. Fuel 103, 792–798 (2013). https://doi.org/10.1016/j.fuel.2012. 10.028 2. Barsky, V., Frishman, A., Lysenko, A.: Adaptive system for control and optimization of the fuel combustion in the boilers EKO-3. Electromech. Energy-Saving Syst. 3(19), 199–201 (2012) 3. Liu, X.J., Hou, G.L., Yin, C.: An energy saving control for combined cycle power plant by supervisory predictive scheme. In: Proceedings of the European Control Conference, pp. 2991– 2998 (2007) 4. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Simulation and software for diagnostic systems. In: Diagnostic systems for energy equipments. Stud. Syst. Decis. Control 281, 71–90 (2020). https://doi.org/10.1007/978-3-030-44443-3_3 5. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Principles of construction of systems for diagnosing the energy equipment. In: Diagnostic Systems For Energy Equipments. Stud. Syst. Decis. Control 281, 1–22 (2020). https://doi.org/10.1007/9783-030-44443-3_1 6. Babak, V.P., Mokiychuk, V.M., Zaporozhets, A.O., Redko, O.O.: Improving the efficiency of fuel combustion with regard to the uncertainty of measuring oxygen concentration. EasternEurop. J. Enterp. Technol. 6(8), 54–59 (2016). https://doi.org/10.15587/1729-4061.2016.85408

References

87

7. Saraswati, S., Chand, S.: Online linearization-based neural predictive control of air–fuel ratio in SI engines with PID feedback correction scheme. Neural Comput. Appl. 19(6), 919–933 (2010). https://doi.org/10.1007/s00521-010-0419-z 8. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., Zvaritch, V.M.: Technical provision of diagnostic systems. In: Diagnostic Systems For Energy Equipments. Stud. Syst. Decis. Control 281, 91–133 (2020). https://doi.org/10.1007/978-3-030-44443-3_4 9. Zaporozhets, A.: Analysis of control system of fuel combustion in boilers with oxygen sensor. Periodica Polytech. Mech. Eng. 64(4), 241–248 (2019). https://doi.org/10.3311/PPme.12572 10. Going, W., Hao, B., Mansy, S.S., Gonzalez, G., Gilles-Gonzalez, M.A., Chan, M.K.: Structure of a biological oxygen sensor: a new mechanism for heme-driven signal transduction. Proc. Natl. Acad. Sci. U.S.A. 95, 15177–15182 (1998). https://doi.org/10.1073/pnas.95.26.15177 11. Isermann, R.: Diagnosis of diesel engines. In: Combustion Engine Diagnosis, ATZ/MTZFachbuch, pp. 133–190. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3662-49467-7_5 (2017) 12. Diemel, O., Pareja, J., Dreizer, A., Wagner, S.: An interband cascade laser-based in situ absorption sensor for nitric oxide in combustion exhaust gases. Appl. Phys. B 123, 167 (2017). https:// doi.org/10.1007/s00340-017-6741-8 13. Babak, V., Eremenko, V., Zaporozhets, A.: Research of diagnostic parameters of composite materials using Johnson distribution. Int. J. Comput. 18(4), 483–494 (2019) 14. Zaporozhets, A., Eremenko, V., Isaenko, V., Babikova, K: Approach for creating reference signals for detecting defects in diagnosing of composite materials. In: Shakhovska, N., Medykovskyy, M (eds.) Advances in Intelligent Systems and Computing IV, vol. 1080, pp. 154–172. Springer, Cham. https://doi.org/10.1007/978-3-030-33695-0_12 (2020) 15. Jessop, A.: Margin of error. In: Let the Evidence Speak. Springer, Cham, pp. 39–51. https:// doi.org/10.1007/978-3-319-71392-2_6 (2018) 16. Krasilnikov, A., Beregun, V., Harmash, O.: Analysis of estimation errors of the fifth and sixth order cumulants. In: 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), pp. 754–759. http://doi.org/10.1109/ELNANO.2019.8783910 17. Babak, V., Dekusha, O., Kovtun, S., Ivanov, S.: Information-measuring system for monitoring thermal resistance. In: CEUR Workshop Proceedings, vol. 2387, pp. 102–110. http://ceur-ws. org/Vol-2387/20190102.pdf (2019) 18. Chajkovs’ka, J.: The development of energy-saving operation technology of the biodiesel plant as a part of the cogeneration system. East.-Eur. J. Enterp. Technol. 1(8(79)), 4–10. http://doi. org/10.15587/1729-4061.2016.59479 (2016) 19. Bailera, M., Lisbona, P., Romeo, L.M.: Power to gas-oxyfuel boiler hybrid systems. Int. J. Hydrogen Energy 40, 10168–10175. https://doi.org/10.1016/j.ijhydene.2015.06.074 (2015) 20. Luo, W., Wang, Q., Guo, J., Liu, Z., Zheng, C.: Exergy-based control strategy selection for flue gas recycle in oxy-fuel combustion plant. Fuel 161, 87–96 (2015). https://doi.org/10.1016/j. fuel.2015.08.036 21. Zaporozhets, A.A., Eremenko, V.S., Serhiienko, R.V., Ivanov, S.A.: Development of an intelligent system for diagnosing the technical condition of the heat power equipment. In: 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), pp. 48–51. http://doi.org/10.1109/STC-CSIT.2018.8526742 (2018) 22. Chen, H., Chang, S., Fan, A.: Model-based control of electromagnetic valve actuators for engine speed control. Int. J. Automot. Technol. 20(1), 127–135 (2019). https://doi.org/10. 1007/s12239-019-0012-0 23. Bai, Y., Fan, L.Y., Ma, X.Z., Peng, H.L., Song, E.Z.: Effect of injector parameters on the injection quantity of common rail injection system for diesel engines. Int. J. Automot. Technol. 17(4), 567–579 (2016). https://doi.org/10.1007/s12239-016-005724. Zaporozhets, A.: Development of software for fuel combustion control system based on frequency regulator. In: CEUR Workshop Proceedings, vol. 2387, pp. 223–230. http://ceur-ws. org/Vol-2387/20190223.pdf (2019) 25. Zaporozhets, A., Eremenko, V., Serhiienko, R., Ivanov, S.: Methods and hardware for diagnosing thermal power equipment based on smart grid technology. In: Shakhovska, N., Medykovskyy, M (eds.) Advances in Intelligent Systems and Computing III, 871, pp. 76–489. Springer, Cham. https://doi.org/10.1007/978-3-030-01069-0_34 (2019)

Chapter 4

Experimental Research of a Computer System for the Control of the Fuel Combustion Process

4.1 Results of Experimental Studies of VOC Changes For conducting experimental studies to determine the current VOC in the air using direct (gas analyzer) and indirect (based on meteorological data on temperature, absolute pressure and relative humidity) methods, described in Chap. 2 of this monography [1, 2], a locality was selected on the territory of the Lubny city, Poltava region, with the following geographical coordinates: latitude—50.013°, longitude—32.991°. On the territory of Lubny city dominates the temperate-cold climate. According to Keppen’s classification, the climate in the city corresponds to the Dfb level (the climate is moderately cold with uniform humidity). Based on statistical data between 1982 and 2012, the average annual temperature in Lubny is 8 °C. The average annual rainfall is 628 mm. The warmest month of the year is July, with an average temperature of 20.1 °C. The coldest month is January, the average temperature is −5.5 °C. Figure 4.1 shows the monthly average change in temperature and precipitation according to 1982–2012. For determining the current concentration of oxygen in the air, using the direct method, it was used a portable OKSI-5M gas analyzer with an absolute error in determining the oxygen concentration O2 = ±0.1%). Measurement of oxygen concentration using a gas analyzer was carried out 3 times a day at around 09:00, 15:00 and 20:00 local time in all weather conditions (rain, snow, gusty wind, etc.) in a special protected building at a local weather station [3]. For determining the concentration of oxygen in the air, using an indirect method, a set of measuring instruments was used, consisting of a TM4-1 meteorological psychometric thermometer and a BAMM-1 aneroid barometer. Figure 4.2 shows a general view of the applied set of devices. The temperature, absolute pressure and relative humidity were also measured 3 times a day at the same time as the measurements of the VOC in the air.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 A. O. Zaporozhets, Control of Fuel Combustion in Boilers, Studies in Systems, Decision and Control 287, https://doi.org/10.1007/978-3-030-46299-4_4

89

90

4 Experimental Research of a Computer System for the Control …

Fig. 4.1 Graph of average monthly temperature and precipitation changes on the territory of Lubny city

Fig. 4.2 Measuring instruments for determining the current concentration of oxygen in the air: a portable gas analyzer OKSI-5M; b meteorological psychrometric thermometer TM4-1; c BAMM1 aneroid barometer

4.1 Results of Experimental Studies of VOC Changes

91

The experiment for determining the current concentration of oxygen in the air by direct and indirect method lasted 8 months: from August 2015 to March 2016. At the same time, along with the measurement of meteorological parameters and the VOC in the air, observations were made of prevailing weather phenomena (rain, precipitation, wind force), however, no relationship was found between them and the VOC in the air. An experiment was conducted to verify the adequacy of the proposed model of the daily/seasonal change in the concentration of oxygen in the air by comparing the results of direct measurements of the concentration of oxygen in the air, carried out using the OKSI-5M gas analyzer (O2 = ±0.1%) and indirect ones, based on the analysis of meteorological parameters: temperature (TM4-1 meteorological psychrometric thermometer, T = ± 0.2 °C), absolute pressure (BAMM-1 aneroid barometer, P = ±20 hPa) and relative humidity (ϕ = ±3%). As a result of the experiment, 475 parallel measurements of the oxygen concentration in the air and meteorological indicators were carried out (3 per day from August 2015 to January 2016). Figure 4.3 shows the results of direct measurements of VOC in the air during the entire experiment. During the experiment, in parallel with the measurement of the VOC in the air using the OKSI-5M gas analyzer, the main meteorological parameters were measured—temperature, humidity and pressure (Figs. 4.4, 4.5 and 4.6). Based on the data obtained, taking into account dependence (2.27), a theoretical dependence of the VOC changing during the experiment was obtained (Fig. 4.7).

Fig. 4.3 VOC measured by the gas analyzer OKSI-5M

Fig. 4.4 Measured values of air temperature during experimental period

92

4 Experimental Research of a Computer System for the Control …

Fig. 4.5 Measured values of relative air humidity during experimental period

Fig. 4.6 Measured values of atmospheric pressure during experimental period

Fig. 4.7 Indirect values of the VOC during experimental period

Experimental studies were carried out in a wide range of meteorological parameters, while the maximum and minimum values of temperature, absolute pressure and relative humidity, respectively, were: Tmax = 31.8 °C; Tmin = −15.1 °C; Pmax = 1046 hPa; Pmin = 990 hPa; ϕmax = 86%; ϕmin = 29%. The observed minimum of VOC in the air by the direct measurement reached 20.5%, the maximum—21.3%; minimum value of VOC in the air by the indirect measurement reached 20.5%, the maximum—21.0%.

4.2 Metrological Evaluation of Experimental Studies of VOC Changing

93

4.2 Metrological Evaluation of Experimental Studies of VOC Changing Comparison of direct and indirect methods for the determination of VOC was carried out in 2 ways: (1) by comparing the method of median estimates; (2) calculation of uncertainties [4–7]. Median Estimation Method The results of direct and indirect values of the volumetric oxygen concentration are formed in 25 groups (19 measurements in each group). Figure 4.8 and the Table 4.1 show median estimates of oxygen concentration values obtained by the direct and indirect method (each value corresponds to approximately a time interval of 7 days). For comparing the two methods, confidence intervals were found for the obtained median estimates of the measured values in groups, which are the absolute error of multiple measurements (considering the distribution law of errors to be normal), which is calculated as follows: [O2 ]dir = [O2 ]dir ± [O2 ]dir , P = 0.95; [O2 ]indir = [O2 ]indir ± [O2 ]indir , P = 0.95;  2   2 2 tP,n · S[O2 ] + tP,∞ · θ[O2 ] , [O2 ] = 3 where (P)O2 = tP,n · SO2  and θ (P)O2 = tP,∞ · 23 θO2 are he random and systematic components, respectively, reduced to the same value of the confidence probability

Fig. 4.8 Graphical comparison of direct and indirect measurements of oxygen concentrations in air (each group contains 19 separate measurements)

94

4 Experimental Research of a Computer System for the Control …

Table 4.1 Results of direct and indirect measurements of the VOC in the air №

1

2

3

4

5

[O2 ]i (%)

20.72

20.69

20.62

20.74

20.70

[O2 ]d (%)

20.65

20.65

20.65

20.65

20.65

№

6

7

8

9

10

[O2

]i

(%)

20.65

20.78

20.77

20.76

20.87

[O2 ]d (%)

20.65

20.75

20.65

20.65

20.65

№

11

12

13

14

15

(%)

20.81

20.81

20.86

20.84

20.85

[O2 ]d (%)

20.85

20.95

20.85

20.85

20.85

[O2

]i

№

16

17

18

19

20

[O2 ]i (%)

20.88

20.88

20.88

20.86

20.85

[O2 ]d (%)

20.95

20.95

20.95

20.95

20.85

№

21

22

23

24

25

(%)

20.94

20.92

20.90

20.93

20.90

[O2 ]d (%)

20.95

20.95

20.95

20.95

20.95

[O2

]i

P = 0.95; tP,∞ is Student’s coefficient at n → ∞; θO2 is the marginal error of the measuring instrument. The average value of the error in measuring the oxygen concentration by the gas analyzer is calculated by the following formula:

S[O2 ]

where [O2 ] =

1 n

·

n 

   =

 2 1 [O2 ] − [O2 ] , n · (n − 1) i=1 n

(4.1)

[O2 ]i .

i=1

The determination of the absolute error of oxygen measurement by the derived formula (2.27) is calculated as for irreproducible indirect measurements. A random error in the oxygen measurement is found as for direct multiple measurements (4.1). The systematic component of the measurement error is determined by the formula:  θ[O2 ] =

∂[O2 ] ∂T

2

 · T 2 +

∂[O2 ] ∂P

2

 · P 2 +

∂[O2 ] ∂ϕ

2 · ϕ 2 ,

where T, P and ϕ—absolute errors of devices of directly measured values. It was established that the maximum margin systematic error of the calculation method is 3.3 times less than by the method using a gas analyzer. But the maximum random error of the calculation method in groups is 2.6 times greater than in the direct method.

4.2 Metrological Evaluation of Experimental Studies of VOC …

95

Figure 4.9 presents the median estimates of the oxygen concentration in the groups measured by the gas analyzer. «Dots» reflect calculated confidence intervals. Figure 4.10 shows the median estimates of the oxygen concentration values for the groups obtained by the formula. “Dots” reflect calculated confidence intervals. In Fig. 4.11, «dots» show the general confidence intervals for the two methods, «large crosses» indicate the median estimates of the oxygen concentration values for the groups obtained by the calculation formula, and «small crosses» indicate the median estimates of the oxygen concentration values for the groups measured by the gas analyzer. The hit of the median estimates of the measured oxygen values in the groups by two methods are shown in Table 4.2, where «1» indicates the hit value of the median score obtained by the appropriate method in the general confidence interval, and “0”—not hit, respectively.

Fig. 4.9 Display of calculated median estimates of measurements by the direct method with confidence intervals in groups

Fig. 4.10 Display of calculated median estimates of measurements by indirect method with confidence intervals in groups

96

4 Experimental Research of a Computer System for the Control …

Fig. 4.11 Median estimates of oxygen concentration values for groups calculated using two methods (direct and indirect) with general confidence intervals

Table 4.2 Results of hitting the median estimates of the measured oxygen values in the groups in the general confidence interval Method

№

1

2

3

4

5

Direct

1

1

1

0

1

Indirect

1

1

1

1

1 10

№

6

7

8

9

Direct

1

1

1

0

1

Indirect

1

1

1

1

1 15

№

11

12

13

14

Direct

1

1

1

1

1

Indirect

1

1

1

1

1

16

17

18

19

20

0

0

0

0

1

№

Direct Indirect

1

1

1

1

1

21

22

23

24

25

Direct

1

1

0

1

1

Indirect

1

1

1

1

1

№

From Table 4.2 it can be seen that all values of median estimates for groups of oxygen concentration in air determined by the calculation method, based on meteorological parameters, fell into the general confidence interval. 28% of the median estimates for the groups of oxygen concentration in the air measured by the gas analyzer did not fall into the general confidence interval.

4.2 Metrological Evaluation of Experimental Studies of VOC …

97

Thus, the proposed calculation method for determining the concentration of oxygen in the air is not inferior to the direct method using gas-analyzing devices. For improving the method under consideration, you can: use measuring instruments of the highest accuracy class, which will reduce the systematic error; increase the number of measurements to reduce the random component of the basic error. Calculation of Measurement Uncertainty Direct method for VOC measuring The gas analyzer OKSI-5M in the mode of determining has a scale value of 0.1%. Its absolute error of measurement (according to specifications) is also 0.1%. Experimental measurements were one-time and they were obtained at the different levels of output quantity. The array of the obtained values of VOC was divided into 95 groups of 5 values. An uncertainty of the A type is regarded as the median estimation of the mean-square deviations of the obtained groups, which comprised 0.024%. An uncertainty of the B type of the direct method of measurements for the uniform law of distribution of probabilities of random variables is: O uˆ B = √ 2 = 0.058%. 3

(4.2)

The estimation of summary standard uncertainty is equal to: uˆ c =



uˆ A2 + uˆ B2 = 0.063%.

The estimation of the expanded uncertainty of the direct method of measurements at the confidence coefficient 95% is calculated according to [8]:

Uˆ 1 = tP (ˆveff ) · uˆ c = t0,95 feff

2   uˆ A2 · 1+ 2 · uˆ c = 0.104%, uˆ B

where tP (ˆveff ) is the Student’s coefficient for probability P and the number of degrees of freedom feff = ni −1, ni is the number of measurements, carried out when assessing the i-th contribution of uncertainty. Indirect method of VOC measuring Conducting the evaluation of the expanded uncertainty of the proposed indirect method of the measurement of VOC by a classic method is impossible since the analytical representation of the model of measurement takes the form of complex nonlinear functional dependence on three input values. When differentiating function (2.27), we have the not simplified polynomials of partial derivatives, which complicates subsequent calculations, including determining the correlation coefficients of input quantities. Therefore, to solve this problem, we propose to estimate

98

4 Experimental Research of a Computer System for the Control …

the expanded uncertainty of the indirect method of measurement by using imitation simulation according to the Monte-Carlo method [9–11]. By the calculated values of VOC, based on the measurements of meteorological parameters, we isolated the sets of values of input values corresponding to 20 levels of output quantity. The values of each input quantity from the chosen sets were accepted as the estimation of mathematical expectation for the generation of arrays of random numbers. As an estimation of mean–square deviation (MSD), we accepted the ratio of absolute instrument error and coefficient, which connects MSD of the Gauss’ law with its boundaries with (k = 1.96 at P = 95%). A quantity of iterations of the generation of arrays of random input variables is equal to 105 . According to data on the generated arrays of input random quantities, we obtained 20 arrays of output random quantity. Mathematical expectations of the simulated array of the VOC values differ in the fifth sign after comma from those calculated by formula (2.27) by the VOC value (Table 4.3), respectively. A hypothesis on the normality of the law of distribution of the simulated output quantity is confirmed by the statistical criterion Pearson’s χ–square. The distribution of probabilities of the simulated VOC value is represented in Fig. 4.12 in the form Table 4.3 Calculated and simulated by the Monte Carlo method values of VOC Number of measurement

VOC values

Mathematical expectation of the simulated VOC

VOC uncertainty

0

20.71503

20.71498

0.01288

35

20.69303

20.69298

0.02079

70

20.76964

20.76961

0.01162

105

20.67201

20.67198

0.02150

130

20.77282

20.77281

0.00960

140

20.86138

20.86136

0.00815

172

20.84103

20.84102

0.00710

204

20.85621

20.85620

0.00662

236

20.85598

20.85597

0.00687

268

20.85677

20.85677

0.00658

270

20.84035

20.84032

0.00683

295

20.87586

20.87586

0.00475

320

20.88221

20.88221

0.00492

345

20.90193

20.90193

0.00367

370

20.87519

20.87517

0.00495

375

20.85319

20.85318

0.00580

400

20.93778

20.93778

0.00168

425

20.88545

20.88546

0.00453

450

20.93647

20.93647

0.00183

474

20.87782

20.87781

0.00494

4.2 Metrological Evaluation of Experimental Studies of VOC …

99

Fig. 4.12 Distribution of probabilities of the simulated VOC value that corresponds to the 236th set of input quantities

of histogram. An evaluation of the expanded uncertainty of VOC measured by indirect method according to the results of simulation by the Monte Carlo method is the interval of scope with confidence coefficient P = 95% (4.2). The values of estimation of the expanded uncertainty for the sets of input quantities are presented in Table 4.3. Uˆ 2 =

f (O2 )m

1− 1−P 2

− f (O2 )m 1−P 2

2

,

where f (O2 )mq is the value of q-quantile of the function of distribution of the density of probabilities of the VOC value being simulated. Figures 4.13, 4.14 and 4.15 demonstrate diagrams, which reflect dependence of the change in the expanded uncertainty of output quantity (P, T, ϕ) with an increase of MSD of one input physical quantity by 2, 3 and 4 times, respectively. We examined the sets of values of the input quantities that correspond to the minimum, mean and maximum levels of output quantity. Based on the received data of research into the nature of change in the expanded uncertainty of VCO in the air on the MSD of meteorological parameters, it follows that relative air humidity is the most influencing input physical quantity. In this case, the effect of temperature and of atmospheric air pressure on the estimation of the expanded uncertainty of VCO at the mean level is less than that at the minimum and maximum levels. Figure 4.16 demonstrates dependence of the expanded uncertainty on the VCO value, calculated by the indirect method. It follows from Fig. 4.16 that the proposed dependence has specific character, which is represented by the spread of estimation of the measurement uncertainty of VOC over the entire range. A variable set of values of input quantities that corresponds

100

4 Experimental Research of a Computer System for the Control …

Fig. 4.13 Relative changes in the expanded uncertainty of minimum VOC value (for the set of measurements № 105) on the change in MSD of measuring input quantities

Fig. 4.14 Relative changes in the expanded uncertainty of mean value of VOC (for the set of measurements № 270) on the change in MSD of measuring input quantities

to the narrow range of output quantity might be one of the probable reasons for this behavior of dependence. A comparison of numerical results of uncertainties of the VOC values measured by the direct (0.104%) and indirect (≤0.03%) methods reveals that the former can be applied in practice for the calculation of VOC with a better accuracy. Taking into account the data represented above on the instability of VOC in the air, it is relevant to consider daily/seasonal change in the meteorological parameters of medium and operating conditions of a boiler unit when executing control and managing the process of fuel combustion. The experiment performed in the work

4.2 Metrological Evaluation of Experimental Studies of VOC …

101

Fig. 4.15 Relative changes in the expanded uncertainty of mean value of VOC (for the set of measurements № 400) on the change in MSD of measuring input quantities

Fig. 4.16 Dependence of uncertainty on the indirect measurement of VOC

attests to the fact that VOC in the air (21%) accepted as the constant cannot be used for the technological and ecological calculations of thermo-technical equipment performance. Thus, to increase the accuracy of EAC measurement, Formula (1.7) from Chap. 1 of this book must be transformed as follows [1]: α=

[O2 ]out [O2 ]   =1+ .  [O2 ] − [O2 ]out 20.957 · 1 − e(P,TP ,ϕ) − [O2 ]out

Represented below is the two–parameter dependence of correction (absolute methodical error in the measurement of EAC): α([O2 ], [O2 ]out ) =

[O2 ]out · (21 − [O2 ]) . ([O2 ] − [O2 ]out ) · (21 − [O2 ]out )

102

4 Experimental Research of a Computer System for the Control …

It is shown based on theoretical calculations that the application of the proposed method of EAC measurement, taking into account the current VCO in the air, makes it possible to considerably reduce methodological error of the measurement (to 1, 2 of the absolute value of EAC quantity (at [O2 ] = 20,5%, [O2 ]out = 18%)). The conducted research allows us to considerably enlarge the understanding of the effect of meteorological parameters on the gas composition of medium [12, 13]. The discovered functional interrelations make it possible to qualitatively increase the efficiency of fuel combustion due to an increase in the accuracy of measurement of EAC. However, the elimination of methodological error when determining EAC requires additional equipment in the form of an oxygen sensor, or a set of temperature sensors, pressure and humidity sensors, which will be introduced to the analytical block of a gas analyzing device. This may lead to additional financial expenditures. Results of the conducted research can be used not only in the field of thermalpower engineering for the quality control over fuel materials combustion, but also: • in medicine – for the creation of microclimatic zones with the assigned gas composition of the environment; • in agriculture – to control growth of agricultural crops; • in ecology when compiling climatic maps, as well as other areas. We plan to conduct further experimental studies on the dynamics of change in VOC in the air in other climatic zones. The functional interrelations we received might be used as well for measuring the volumetric concentrations of nitrogen and carbon dioxide in the environment.

4.3 Forecasting of VOC in the Air Table 4.4 shows the values of some methodological corrections that can occur in determining the EAR in the flue gases with different concentrations of residual oxygen. Based on the daily changing meteorological parameters in the Kyiv city for 4 years (from January 1, 2014 to December 31, 2017), it can be concluded that the temperature and the calculated oxygen volume concentration are periodically changed (Fig. 4.17). The determination of the results with an excessive error in the aggregate of measurement data of meteorological parameters is impossible, since there are only single measurements of non-stable physical quantities. To solve the problem of predicting the oxygen volume concentration, it is necessary to determine the form and coefficients of the approximation equation [13–15]. The predicted value is found by substituting in the found equation the ordinal number of the year’s day, as an argument. To find the best model of the approximation equation, we used: 1. the relationship that visually closely describes the nature of the change in the data over the entire range of values (Fig. 4.18a)

0.025

0.022

0.020

0.015

0.010

0.005

20.5

20.6

20.7

20.8

20.9

8

[O2 ]out

20.4

[O2 ]V

0.006

0.013

0.019

0.026

0.033

0.039

9

0.008

0.017

0.025

0.034

0.043

0.052

10

0.011

0.022

0.034

0.06

0.058

0.070

11

0.015

0.030

0.046

0.062

0.078

0.095

12

0.021

0.042

0.063

0.086

0.108

0.132

13

0.029

0.059

0.090

0.121

0.154

0.188

14

0.042

0.086

0.132

0.179

0.227

0.278

15

Table 4.4 The value of corrections in determining the EAR with the current oxygen concentration in the air 16

0.065

0.133

0.204

0.278

0.356

0.436

17

0.109

0.224

0.345

0.472

0.607

0.750

18

0.207

0.429

0.667

0.923

1.200

1.500

19

0.500

1.056

1.676

2.375

3.167

4.071

4.3 Forecasting of VOC in the Air 103

104

4 Experimental Research of a Computer System for the Control …

Fig. 4.17 Measured values of the meteorological parameters: a temperature, b humidity, c pressure and the oxygen volume concentration in the air (d) calculated from January 1, 2014 to December 31, 2017

4.3 Forecasting of VOC in the Air

105

Fig. 4.18 The functional dependences of the calculated value of the oxygen volume concentration on time (the points reflect the calculated values of the oxygen volume concentration in the air, the solid line is the approximation function, the dashed line is the minimum and maximum value of the approximation function): a an approximation function that visually approximates the nature of the data change on the whole range of values; b an approximation function defined by the built-in functions of the MathCAD software package; c the approximation function, determined with the use of Fourier series in accordance with formula (4.5)

106

4 Experimental Research of a Computer System for the Control …

2. the dependence defined by the built-in functions of the software package MathCAD (Fig. 4.18b) 3. the dependence determined with the using of Fourier series (Fig. 4.18c). The coefficient of determination of the functional dependence shown in Fig. 4.18a is 0.450, and its analytical representation looks like:  [O2 ]for (t) = 20.728 + 0.182 · sin

 t − 4.85 . 59.279

The coefficient of determination of the functional dependence shown in Fig. 4.18b is 0.709, and its analytical representation has the form:  t − 5.015 . [O2 ]for (t) = 20.761 + 0.126 · sin 58.885 

The choice of the discrete Fourier transform for approximate the data is due to their periodic nature and the possibility of finding the functional dependence with the coefficient of determination close to 1. The approximation model has the form of an expression of the inverse Fourier transform: [O2 ]for (t) = C0 + 2 ·

m

  Ck j cos ω0 · kj · t − θk j ,

(4.3)

j=1

where m—the number of harmonics of the Fourier series; k—the index of the harmonic; Ck = ak2 + b2k —the real Fourier coefficient of the kth harmonic, k = 0, m; θk = arctg bakk —the initial phase of the kth harmonic; ω0 = 2π —the circular freT quency of the zero harmonic; T —the period of a discrete sequence is equal to the number N (number of days); ak = Re[Xk ]; bk = Im[Xk ]; N −1of observations −i·k·n x · e —N complex amplitudes of the cosine components of the Xk = n n=0 Fourier series (direct Fourier transform), e−i·k·t = cos kt + i sin kt; xn —discrete values of n a posteriori data. The members of the Fourier series were harmonics with the largest value of the complex coefficient C k . The coefficient of determination R2 of the functional dependence of the predicted value of the oxygen volume concentration on the time obtained for 15 harmonics (Fig. 4.18c) is 0.803. The values of ordinal numbers, amplitudes and initial phases of the harmonic are given in Table 4.5. At m = 50 R2 = 0.870, R2 = 0.917 at m = 100, R2 = 0.944 at m = 150, R2 = 0.961 at m = 200. As can be seen from Fig. 4.18 the range of values of the functions found for the predicted oxygen volume concentration is narrower than the a posteriori range. As a predicted value of the oxygen volume concentration, it is proposed to use: • the value of the function of the Fourier series by the formula (4.4). The values of the function parameters are the same as for the function (3). The argument of the

k

0

4

8

2

6

j

0

1

2

3

4

0.006

0.008

0.013

0.063

20.760

Ck

8 9

−1.432

7

2.221

2.050

5 6

−0.479

j

0

Θk

47

16

30

39

21

k

0.005

0.005

0.005

0.006

0.006

Ck

Table 4.5 The values of the parameters of the Fourier series shown in Fig. 4.4c

34

15

28 55

13

42

20

67

k

14

−1.432 −1.028

12

11

10

j

2.107

0.936

2.104

Θk

0.004

0.004

0.004

0.005

0.005

0.005

Ck

−1.171

0.258

2.114

0.773

2.600

−1.420

Θk

4.3 Forecasting of VOC in the Air 107

108

4 Experimental Research of a Computer System for the Control …

function is the sum of the ordinal number of the day in a year and the period of the Fourier series. The adjustment for a leap year is not taken into account; [O2 ]for.1 (t) = C0 + 2 ·

m

  Ck j cos ω0 · kj · (t + q · T ) − θk j ,

(4.4)

j=1

where q = 0, 1, 2, . . .; • average predicted values of the oxygen volume concentration in the air calculated according to the formula (4.4) for the corresponding argument with a period of 365 days; [O2 ]for.2 (t) = C0 +

2·

y−1 m l=0

j=1

  Ck j cos ω0 · kj · (t + l · 365) − θk j y

, (4.5)

where y is the integer value of the number of years, which corresponds to the experimental data on which the approximation function was determined by formula (4.3). Estimation of forecasting reliability was chosen relative error: δ=

  [O2 ]V − [O2 ]pre  [O2 ]V

· 100%.

In order to assess the reliability of the use of the approximation function model for the prediction problem, the predicted and a posteriori values are compared for one day 2014–2018, with q = 0 for [O2 ]pre.1 (t) and for [O2 ]pre.2 (t)—y = [0, 1, 2, 3] in accordance with the year. For all studied functions R2 ≈ 0.8. The results of the corresponding calculations are given in Table 4.6. Table 4.7 shows the predicted values of the oxygen volume concentration in the air, obtained from formulas (4.4) and (4.5), with relative errors and calculated values obtained from formula (2.27) in Chap. 2 of this book. These values were obtained for several days in January and May 2018 (Fig. 4.19). Table 4.6 The predicted and calculated values of the oxygen volume concentration in the air (year) 03.01 (t = 3) [O2

]V

(%)

[O2 ]pre.1 (t) (%) δ1 (%) [O2 ]pre.2 (t) (%) δ2 (%)

2014

2015

2016

2017

20.83

20.79

20.785

20.849

20.858

20.847

20.826

20.858

0.13

0.28

0.20

0.04

20.836

20.835

20.854

20.847

0.03

0.22

0.33

0.01

4.3 Forecasting of VOC in the Air

109

Table 4.7 The predicted and calculated values of the oxygen volume concentration in the air (days) Date

01.01.18

02.01.18

03.01.18

08.05.18

09.05.18

10.05.18

t (day)

1

2

3

127

128

129

]V

20.827

20.823

20.808

20.709

20.734

20.829

[O2 ]pre.1 (t) (%)

20.860

20.860

20.858

20.769

20.774

20.778

δ1 (%)

0.16

0.18

0.24

0.29

0.19

0.25

[O2 ]pre.2 (t) (%)

20.848

20.848

20.847

20.782

20.779

20.777

δ2 (%)

0.10

0.12

0.19

0.35

0.22

0.25

[O2

(%)

Fig. 4.19 Graphical representation of the predicted and calculated values of the oxygen volume concentration in the air in January and May 2018 (the points reflect the calculated values [O2 ]V , the solid line—the approximation function [O2 ]pre.1 (t), the crosses—the predicted values [O2 ]pre.2 ): a three days in January; b three days in May 2018

Taking into account the value of the calculated relative errors given in Tables 4.4, 4.5 and the graphical representation of the calculated predicted values of the oxygen volume concentration in the air in Fig. 4.19, the author prefers calculating the predicted value according to the formula (4.5).

4.4 Results of Experimental Studies of a Computerized System for Controlling the Fuel Combustion Process The research of the developed of automatic control system of the fuel combustion was carried out on the basis of the water heating boiler NIISTU-5. It was highlighted the main approach to improve the efficiency of this boiler – modernization of the

110

4 Experimental Research of a Computer System for the Control …

Fig. 4.20 Reconstruction of NIISTU-5 boiler: a the view before the reconstruction; b the view after the reconstruction

Table 4.8 Technical characteristics of the NIISTU-5 boiler

Parameters

Value

Volume of the heated room (m3 )

15,000

Nominal heating capacity (MW)

0.63

Gaseous fuel efficiency (%)

75

Outlet water temperature (°C)

115

Dimensions with walling (cm)

316 × 210, 5 × 280

furnace space of the boiler with a complete replacement of the morally and physically obsolete burner and automatics [16–18]. The replacement was carried out on the basis of an automated block of burner PBGM-0.85 ND (Fig. 4.20), equipped with a developed automatic control system of the fuel combustion for regulation a work of the burner and boiler as a whole. Technical characteristics of the NIISTU-5 boiler are shown in Table 4.8. Due to the fact that the developed control system of the fuel combustion does not use CO sensors, the main criterion of optimality has been the [CO] concentration in the flue gases. The appearance of chemical underburning (CO) determines the limit of permissible impact on the reduction of air supply. This limit is flexible and depends both from the characteristics of the burners and the load of the boiler. Its position is also affected by: the composition of the fuel (the heat of its combustion); climatic conditions; fuel and air temperature; technical condition of the equipment and many other current factors [19]. Studies of the [CO] concentration changing in the flue gases from the EAC in the nominal operating mode of the boiler were carried out (Fig. 4.21). It was established experimentally that this boiler operates with the lowest [CO] concentration in the flue gases in the regime at α = 1.2. In the cases when the developed control system will be operate on the other boilers, it is necessary to conduct preliminary regime tests to determine the current optimality criterion.

4.4 Results of Experimental Studies of a Computerized System …

111

Fig. 4.21 Dependence of [CO] concentration from EAR in the flue gases

Figure 4.22 shows the experimental graphs of the dependence of the boiler power from the oxygen concentration of in the flue gases. In the course of the experiment it was found that the control system allows to maintain the concentration of residual oxygen in the flue gases at the level of 3.3– 3.5%, which in the EAC values is 1.19–1.2. Also, the dependence of air consumption from fuel consumption in the boiler power range from 10 to 100% are established (Fig. 4.23).

Fig. 4.22 Experimental results of the dependence of the boiler power from the concentration of residual oxygen in the flue gases at various boiler loads

112

4 Experimental Research of a Computer System for the Control …

Fig. 4.23 Value of the air consumption from fuel consumption at various boiler loads

Obtained graphs showed that the system provides a linear dependence of the air consumption from fuel consumption with the determination coefficient R2 in the range from 0.9985 to 0.9999. Figures 4.24 and 4.25 show the dependence of the boiler ECE and the heat losses with the flue gases from the boiler power with control system. During calculating the boiler ECE, it was taken into account that the heat losses from the boiler walls is no more than 0.25%, and the heat losses with chemical underburning of the fuel is completely absent. Thus, during the experiment it was shown that the maximum ECE of the boiler is ~97.4% achieved at the level of 10% of the rated power. Its value decreases linearly and assumes a minimum value (92.4%) with the maximum boiler power. In this case, there are minor deviations from a linear drop in the range from 0.2 to 0.3 Gcal/h, which may be due to an increase in the rate of temperature growth of the flue gases. The total determination coefficient R2 for the dependence of the boiler ECE changing is 0.997. The increasing in heat losses with flue gases is also linear, with a minimum value of 2.4% at 10% boiler ECE, and a maximum of 7.3% at 100% of the boiler efficiency. Figure 4.26 shows an indicative comparison of the dependence of the boiler ECE

4.4 Results of Experimental Studies of a Computerized System …

113

Fig. 4.24 Dependence of the boiler ECE from the boiler power

Fig. 4.25 Dependence of the heat losses with the flue gases from the boiler power

from its power during the boiler operating on a mode map and using the developed system. As can be seen from Fig. 4.26, the using of the automatic control system for the fuel combustion can significantly increase the boiler ECE at any boiler load. The maximum ECE difference occurs at 20% of the boiler load and its level is 22.1%, the minimum ECE difference occurs at the rated boiler load and its level is 6.5%. Table 4.9 shows the results of ecological and heat engineering tests of the NIISTU-

114

4 Experimental Research of a Computer System for the Control …

Fig. 4.26 Comparison of the boiler efficiency with a mode map and with automatic control system based on broadband oxygen sensor Table 4.9 Ecological and heat engineering characteristics of the NIISTU-5 hot water boiler based on the automatic combustion process control system Parameters Heat output

Symbol Q

Dimension

Boiler load in % of maximum value 10%

30%

50%

75%

100%

Gcal/h

0.054

0.16

0.22

0.32

0.43

MW/h

0.06

0.19

0.32

0.47

0.64

m3 /h

6.57

19.72

32.87

49.3

65.74

Fuel consumption

B

Air consumption

L

75.1

225.3

375.5

563.3

751

Flue gas temperature

Tout

°C

70

93

114

151

180

Mass concentration of pollutants

C(NO)

mg/m3

13.4

22.78

38.86

34.84

41.54

C(CO)

mg/m3

50

27.5

12.5

25

38.75

EAC

α

1.2

1.2

1.2

1.2

1.2

Heat loss with flue gas

q2

%

2.38

3.41

4.34

8.25

10.05

Loss of heat from chemical underburning

q3

%