BADANIA I ANALIZA NUMERYCZNA RAMY GOKARTA fileBADANIA I ANALIZA NUMERYCZNA RAMY GOKARTA

Scientific monograph: Modeling of energy systems with fixed and moving porous media by artificial intelligence methods

Scientific editor: Prof. dr hab. inż. Wojciech Nowak

Centre of Energy, AGH University of Sciences and Technology in Cracow

Reviewers: Dr hab. inż. Krzysztof Koszela

The Institute of Biosystem Engineering, Poznań University of Life Sciences

Dr hab. inż. Wojciech Sobieski Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn

Author: Jarosław Krzywański

Faculty of Mathematics and Natural Sciences, Jan Długosz University in Czestochowa

Publishing editor: Waldemar Dudda

Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn

Cover design: Waldemar Dudda Publisher: Department of Mechanics and Machine Design, Faculty of Technical Sciences,

University of Warmia and Mazury in Olsztyn ISBN 978-83-60493-05-2 Printing based on materials provided by the author.

Copyright by: Department of Mechanics and Machine Design, Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn

Olsztyn 2018 Address: Katedra Mechaniki i Podstaw Konstrukcji Maszyn ul. Michała Oczapowskiego 11, pok. E126 10-719 Olsztyn tel./fax: + 48 89 523 32 55 e-mail: [email protected]

Arkuszy wydawniczych 6,8; nakład 100 egz.

Druk Zakład Poligraficzny UWM w Olsztynie

This scientific work is supported by the project: “Long-term research activities in the area of

advanced CO2 Capture Technologies for Clean Coal Energy Generation”: the People Programme

(Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-

2013/ under REA grant agreement n° PIRSES GA-2013-612699.

The scientific work was performed also within the confines of subsidies granted by the Faculty of

Mathematics and Natural Sciences of Jan Dlugosz University in Czestochowa, Poland.

The support is gratefully acknowledged

… to family and friends

CONTENTS page

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1. NEURAL NETWORKS, GENETIC ALGORITHMS AND FUZZY LOGIC

METHODS - A SHORT INTRODUCTION 7 1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2. Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3. Genetic algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4. Fuzzy Logic methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.5. Tools [How-to] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.5.1. The Neuro Net application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.5.2. The Qtfuzzylite application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 1.7. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2. FUZZY LOGIC MODELING OF HEAT TRANSFER COEFFICIENT

FOR WING WALLS IN A LARGE-SCALE CFBC 38 2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.2. Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.1. An object of investigations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.2. Fuzzy logic modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.3. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.1. The effect of bed temperature and voidage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.3.2. The effect of gas velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.3.3. The effect of the MCR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.3.4. Overall heat transfer coefficient for real conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3. NEUROCOMPUTING APPROACH FOR HYDROGEN PRODUCTION

VIA CaO SORPTION ENHANCED GASIFICATION OF SAWDAST IN FLUIDIZED BED 54

3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.2. Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.3.1. Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.3.2. Influence of operating parameters on hydrogen production . . . . . . . . . . . . . . . . . . . . . . . 61 3.3.2.1. Effect of CaO/C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.3.2.2. Effect of H2O/C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.3.2.3. Effect of temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.2.4. Effect of the equivalence ratio of air (ER) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.3. The best strategy for hydrogen production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4. FUZZY LOGIC TREATMENT FOR NOx EMISSIONS FROM CALCIUM

LOOPING PROCESS IN FLUIDIZED BEDS 67 4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2. Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.1. An object of investigations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.2. Application of fuzzy logic method and calculating conditions . . . . . . . . . . . . . . . . . . . . 71 4.3. Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.3.1. Effect of oxygen concentration in flue gas from the regenerator and total CO and CO2 concentrations in flue gas from absorber . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.3.2. The effect of volatile matter, nitrogen and fixed carbon content in fuel . . . . . . . . . . . . 76 4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5. A COMPUTATIONAL STUDY OF CaSO4 DECOMPOSITION DURING

COAL PYROLYSIS BY GENETIC ALGORYTHMS AND NEUROCOMPUTING APPROACH 84

5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2. Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.3. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.3.1. Genetic algorithm and artificial neural networks approach . . . . . . . . . . . . . . . . . . . . . . . 88 5.3.2. Validation of the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.3.3. Influence of operating parameters on CaSO4 decomposition rate . . . . . . . . . . . . . . . . . 94 5.3.3.1. Effect of temperature and CaSO4/coal ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.3.3.2. Effect of inherent minerals content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.3.3.3. Effect of coal type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.3.3.4. Effect of coal particle size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.3.3.5. Effect of holding time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 STRESZCZENIE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 LIST OF THE MOST IMPORTANT SYMBOLS AND ACRONYMS . . . . . . . . . . . . . . . . 107

6

Preface The presented book deals with artificial intelligence (AI) methods, i.e. the techniques of modeling of complex systems. Artificial neural networks (ANN), genetic algorithms (GA) and fuzzy logic (FL) methods are applied in the study. Some of the developed models have been performed on the base of my experiences acquired during the two internships, held in Niigata University in Japan and in Zhejiang University in Hangzhou, China within the project: “Long-term research activities in the area of advanced CO2 Capture Technologies for Clean Coal Energy Generation”: the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013. The following monograph covers a wide range of applications and includes an actual part of research in the field of heat transfer in a large-scale circulating fluidized bed boiler, hydrogen production via CaO sorption enhanced gasification of sawdust in a fluidized bed unit, NOx

emissions from calcium looping in fluidized bed systems and the CaSO4 decomposition during coal pyrolysis. The unique capabilities of AI methods are shown by the presented case studies. On the other hand, accounts are given of some explanations including basic information on the considered AI techniques: ANN, FL and GA. All presented cases occur in fixed or fluidized beds, including circulating fluidized beds. Taking into account the definition given literature, they all can be considered as fixed or moving porous media1. The developed models constitute a series of easily-applicable and powerful tools allowing to describe complex energy systems.

I wish to express my grateful thanks to my wife Anita for her love, support and patience during writing of this book as well as to my daughter Marysia, for motivation to work. I would also like to thank: Izabela Majchrzak-Kucęba, Tomasz Czakiert and Przemek Szymanek

from Czestochowa University of Technology, Marcin Sosnowski from Jan Dlugosz University in Czestochowa, Tadaaki Shimizu and Liuyun Li from Niigata University in Japan as well as Mengxiang Fang, Qiunhi Wang, Hongato Fan, Yi Feng and Abdul Rahim Shaikh from Zhejiang University in Hangzhou, China for any help and assistance during the research, which helped me covering in this book such a wide range of issues. I’d also like to appreciate the commentary provided by the reviewers: Wojciech Nowak form Centre of Energy, AGH University of Science and Technology, Cracow, Wojciech Sobieski from University of Warmia and Mazury in Olsztyn, and Krzysztof Koszela from Poznań University of Life Sciences. The thoughtful, in-depth commentary on the original manuscript were very valuable. Finally I would like to thank several people, who have been instrumental in the completion of this book.

Częstochowa, March 2018 Jarosław M. Krzywański

1 Sobieski W., Lipiński S., Dudda W., Trykozko A., MarekM., Wiącek J., Matyka M., Gołembiewski J., Granularne

Ośrodki Porowate (Granular Porous Media), Katedra Mechaniki i Podstaw Konstrukcji Maszyn, Uniwersytet Warmińsko-Mazurski w Olsztynie, Olsztyn 2016.

7

1. NEURAL NETWORKS, GENETIC ALGORITHMS AND FUZZY LOGIC METHODS - A SHORT INTRODUCTION

Nomenclature:

e voidage, - f activation function, -

h heat transfer coefficient, Wm-2K-1

i input, m load of the boiler μ degree of membership, - o output, T temperature, oC U velocity, m s−1 z the distance from the gas distributor, m ρ density, kg m-3

Subscripts b bed g gas

Acronyms AI Artificial Intelligence ANN Artificial Neural Networks BP Backpropagation CC Cooling Capacity CFB Circulating Fluidized Bed CFBC Circulating Fluidized Bed Combustor FL Fuzzy Logic GA Genetic Algorithm GUI Graphical user interface MCR Maximum Continuous Rating MSE Mean Squared Error

1.1. Introduction

As the experiments are the basic cognitive methods which allow one to specify empirical dependencies in Energy Engineering Science, most of discussed in literature results are usually obtained via measurements on real objects. However a big challenge, as well as the high costs, time-consuming and limitations make the experiments often an insufficient method of data mining. An alternative method of data handling could be a mathematical modeling approach (Figure 1.1) [1-3]. However, since the complexity of many processes in energy engineering is still not sufficiently recognized, the development of simple, non-iterative, models of such cases is of practical significance [4, 5].

1. Neural networks, genetic algorithms and fuzzy logic methods - a short introduction

8

Fig. 1.1. Modeling approach as an alternative method of data handling

Many aspects we should consider when modeling of an Energy System, e.g. what approach should be used, what is the aim of the model research, do we have validation data, what kind of validation data can we use, what is the accuracy accepted for the model research, etc. (Figure 1.2).

Fig. 1.2. Some aspects for considering when modeling

Different modeling approaches can be distinguished. One of the classifications includes [4]:

• the programmed computing approach and

• the artificial intelligence (AI) methods. The first one is based on writing algorithms many of which exist in literature. Different in details or/and sophistication models of solid fuels combustion, especially in fluidized beds, can be found in literature. As an example, a review of existing circulating fluidized bed combustor models was performed by Basu [1]. The author discussed two approaches to the performance modeling, i.e. furnace approach and system approach. The furnace approach describes the details of what goes on in the furnace while the system approach takes an interest in system integration. In the furnace approach, models can be grouped under three levels of details or/and sophistication: level I: 1-D, plug flow/stirred tank, using simple mass and energy balance; level II: core-annulus, 1.5-D with broad consideration of combustion and other related processes; level III: 3-D model based on Navier-Stokes Equation with detailed consideration of chemical kinetics and individual physical processes [6, 7].


9

So the question is why and when to use AI methods in Energy Engineering. One can say: we have already lots of models, correlations and even experimental results, which can describe the reality good enough. Why should we consider another approach with new software or tools? In order to answer the question, let us consider heat transfer in the furnace of a circulating fluidized bed (CFB) boiler. Heat transfer processes which occur in the combustion chamber of a circulating fluidized bed boiler are affected by complex factors, such as: suspension density, bed temperature, boiler load, gas velocity, geometry of the chamber and flue gas properties. Therefore usually expensive and laborious measurements are necessary. In this case the question is: are obtained values still valid when boiler’s operational parameters change? In such a situation it is often essential to make another experiment. Another way to evaluate the heat transfer coefficient is the use of multiple equations, correlations and models. The correlations given in literature usually differ from one another and are valid only for limited ranges of operating parameters. It also limits their accuracy and generality especially for the reason, that there are limited experimental data on the overall heat transfer coefficient for heating surfaces in the furnace of a CFB boiler in the literature. On the other hand models usually need some additional data, e.g. to adjust parameters and sometimes are time consuming in order to obtain accurate predictions. The parameters of the model also could not be determined immediately, especially for different operating conditions. Sometimes additional assumptions should be made to get a trackable solution. The algorithms are often complicated and are based on the solution of complex and time consuming sets of differential equations. As an example Zhou et al. pointed out that simulations conducted on the platform of FLUENT 12 software with three processors parallel for 70 s with the number of meshes nearly 8500 cost nearly 60 days [8]. Artificial intelligence (AI) approach, such as artificial neural networks (ANN), Fuzzy Logic (FL) and genetic algorithms (GA) can be the alternative methods for the above technics of data handling. The most convenient approach would be to apply a non-iterative procedure (Figure 1.3), where one only needs to enter input parameters and call the developed AI model. The presented approach resembles the working principles of sensitivity analysis, where a validated model is used for testing of the influence of a selected input variable on the model’s response [9, 10]. The heat transfer coefficient or other outputs are generated as an answer to the input data set as similar procedure can be applied for different output values, e.g. NOx an SO2 emissions, cooling capacity CC of an adsorption chiller, etc. (Figure 1.3).

Fig. 1.3. A flow chart for a non-iterative procedure


10

Artificial neural networks, genetic algorithms and fuzzy-logic techniques constitute the basic representatives of the artificial intelligence methods [11]. The AI models are considered to be tools which sometimes can overcome the shortcomings of the programmed computing approach and the experimental procedures. Main features of the above discussed methods of data handling are listed in Table. 1.1. Table 1.1. Main features of different methods of data handling

Measurement Modeling

Big challenge

(expensive and laborious)

Time-consuming

Limitations in operating parameters

Programmed computing approach - complicated algorithms, based on the solution of complex and sometimes time consuming sets of

differential equations.

Programmed computing approach - the parameters could not be determined immediately, especially for different operating

conditions; additional data (also limited) and assumptions are sometimes necessary to adjust parameters and get a trackable

solution.

AI approach – in some cases require a large number of different data, covering the whole domain of variability of model parameters

AI approach - quick (due to non-iterative procedure) and accurate

results for any input pattern.

The aim of this book is to develop models of some energy systems with fixed and moving porous media. A wide range of applications are considered in the monograph. Heat transfer in a large-scale circulating fluidized bed boiler, hydrogen production via CaO sorption enhanced gasification of sawdust in a fluidized bed unit, NOx emissions from calcium looping in fluidized bed systems and the CaSO4 decomposition during coal pyrolysis are discussed in the work. Using the term moving porous media, we mean fluidized beds (both bubbling and circulating fluidized bed) since they can be considered as porous material moving in a reaction chamber. Selected artificial intelligence techniques, i.e. artificial neural networks, fuzzy logic and genetic algorithms are applied in the study.

1.2. Artificial Neural Networks

An artificial neural network operate like a ‘‘black box’’ and its learning process resembles a human brain operation. ANNs have the ability to simulate uncertain, ill-defined, big and complex systems. Since a single neuron has a limited memorizing capacity a neural network constitutes a group of interconnected (by so-called weights) neurons (perceptrons) (Figure 1.4). A neuron (perceptron) is an artificial model of a foundational unit of the human brain which can be built on a computer [12-22]. Similar to its biological equivalents each i-th artificial perceptron takes in some number of inputs xi multiplied by a weight wi, which then are summed

together. Such obtained logit i

n

0i ixwz is passed through an activation function f generating

the output y, which then can be transmitted to other neurons [12-22]. During the learning (training) process the weights wi are modified to adjust the output to the desired value, taking into account the assumed error of approximation.


11

Among input signals biases should be distinguished. Biases are scalars added to the input to ensure that at list a few nodes in a layer are activated, regardless of signal strength. Such additional signals allow learning even the signals are low [13].

Fig. 1.4. Schematic diagram for a neuron (a) and an artificial neural network (b)

After the training epoch the adjusted weights assure to obtain the desired output and, being one of the main elements of an ANN structure, store the knowledge of the considered process [4, 5]. Two major types of functions are used as an activation function: sigmoid activation function (Figure 1.5):

ze1

1zf

, (1.1)

b)

a)

f


12

Fig. 1.5. Sigmoid activation function

and hyperbolic tangent activation function (Fig 1.6):

zz

zz

ee

ee

)zcosh(

)zsinh(ztanh)z(f

. (1.2)

Fig. 1.6. Hyperbolic tangent activation function

The activation function governs the behavior of the artificial neuron and transforms the logit into an output signal for the next node layer. In most cases (but not always) all the neurons within an ANN have the same type of activation function [12, 13]. Since a single neuron has a limited memorizing capacity, the neural network constitutes a group of interconnected neurons forming layers. Therefore we can distinguish input layer, hidden layers and the output layer (Figure 1.5).


13

The input layer is composed a neurons which get the input data. The output layer constitutes a layer of neurons which provide the answer or prediction from the model [13]. Hidden layers lie between the first layer (input layer) and the last (output layer) of neurons and are very helpful in extracting features of a considered data [12]. The main features of the ANN are as follows [23]:

the ANN don’t need of detailed information about the studied phenomena,

they have the ability to ignore redundant and excess data,

ANNs can use incomplete data sets,

they concentrate on more important inputs. Before a neural network becomes a useful modeling tool, it has to be prepared by means of the following main principal steps: Step 1: setting initial random values of weights and optimizing the ANN, Step 2: run the normalization process of input and output parameters, Step 3: run the learning (training) procedure. Initial values of weights are set randomly and uniformly distributed between 0 and 1. Because of the fact, that:

- the architecture of an artificial neural network,

- the number of hidden layers and the number of neurons in each of them,

- the activation function of a neurons, depend on the considered process (task/case) different types of ANNs can be distinguished: such as recurrent, Hopfield and feed-forward neural networks, etc. Moreover, in order to achieve the required accuracy of a neural network estimation, suitable architecture and the proper number of hidden neurons must be assumed. It means that the performance and accuracy of an ANN model highly depends on its structure and therefore the optimal architecture of the network should be found. There are no universal methodologies describing how to select an ANN structure optimal for the considered task. One can find only some tips how to build the best ANN, such as the one which pick up that often hidden layers have fewer neurons than the input layer to compress the input information [12]. However, usually the selection process of the best ANN architecture is run by trial and error [4, 5]. The feed-forward neural network is the ANN type that is widely used for engineering applications. Because of the fact, that usually the input and output parameters have different physical units and range sizes there is a necessity to bring all input and output values to the same numerical range (between 0 and 1) by the normalization process. It also allows avoiding any computational difficulty. Normalization of input and output signals can be performed using e.g. the following formula:

LOWLOWHIGHWW

WWW

MINIMUMMAXIMUM

MINIMUMACTUAL

NORM

(1.3)

where, e.g.: HIGH=0.9; LOW=0.1. The most popular and efficient learning method, used to train artificial neural networks with one or more hidden layers is the backpropagation learning algorithm. This technique performs a gradient descent procedure, i.s. the procedure by which the slope of loss function is calculated by taking a derivative [12, 13]. It constitutes the supervised learning, when the input pattern is repeatedly presented simultaneously with its corresponding output pattern, hence the training process is supervised by the “teacher” via the set of the training data (pattern). The input and the output pattern are the input and the output vectors of the data set, respectively which are used to train the ANN.


14

During the learning (training) epoch the neural network output (vector Y ) is compared

with the pattern (vector Y ) and the obtained difference (error of prediction) is squared and averaged over the number of data points [12, 13]. The obtained value is a mean squared error (MSE) computed as:

N

1i

2

YYN

1MSE (1.4)

which constitutes the measure of the weights modification rate during training (learning) epoch. The example of learning process is given in Figure 1.7.

Fig. 1.7. The change of a mean squared error during a learning process

Two very important factors influencing the learning process are: the learning rate and momentum. The learning rate controls the speed rate of the weights’ modification (i.e. it is multiplied by the error of prediction) whereas the momentum defines the inertia of the learning process, i.e. the inertia of the weights’ modification during the learning stage, leading to the improvement in the stability of the learning process [15-17]. Too high values of learning rate leads to the divergence, whereas too low numbers might cause to stop calculations in a local, instead of global optimum. A local and global optimum corresponds to the local and global minimum in the error surface, respectively. The lowest point of the lowest valey in the error surface is known as the global minimum whereas the nadirs of all other valleys are known as local minima [12, 13, 19]. Talking about learning optimization the loss function is used. It is a function which gauges how well error is minimized at each step during learning process [12, 13]. Higher values of the momentum allow to reach faster convergence, expressed in MSE lowering as well as to prevent from getting stuck in a local optimum. However too high momentum leads to unstable and diverge optimization. The learning process stops when the criterion of the mean squared error is fulfilled and the network has good ability to generalize the obtained knowledge, i.e. to predict accurately of the validation data set. The Lee and Park’s algorithm is an example of the backpropagation learning algorithm, where momentum and learning rate change simultaneously during training phase. To train the network well, sufficient number of input parameters must be used, covering the whole range of variables. After the training epoch, neural networks can be used to achieve adequate response, i.e. output data via non-iterative calculations with a low processing time and small memory resources,


15

as an answer to a new stimuli, not previously presented to the network (not seen by the ANN) [23]

1.3. Genetic algorithms

Another technique for AI methods is genetic algorithms (GA). The main domains of using these methods are the search and optimization issues. They base on the concept of using mechanisms which resemble of the evolution process to determine the optimum solution. Being a part of the Evolutionary Computations the GA techniques belong to Computational Intelligence methods, together with the ANN and FL methods. They provide useful and cost effective tools which constitute an alternative to other, often time consuming optimization procedures. Basing on the genetic processes of biological organisms they mimic the processes of natural populations evolving according to the principles of natural selection and survival of the most fitted individuals described by Charles Darwin [24, 25]. Many works are devoted to genetic algorithms [24 – 31]. A basic genetic algorithm is composed of three stages: 1) reproduction, 2) crossover and 3) mutation (Figure 1.8) [25]. Similar to the natural behavior GAs work with a set of individuals making so called populations. Hence, each of the individuals, previously reproduced in a population, constitutes a possible solution of a given problem and each of them have assigned so called fitness score describing how good a solution of the problem is [24]. Only the highly fitted individuals have the possibility to reproduce with other individuals by a crossover mechanism. Such produced new set of individuals make new offspring population sharing some features taken from each parent individuals. In such mechanism the less fitted members of the population are not selected for the next reproduction process. Since these individuals are not considered during the further calculations, they die. This mechanism assure, that the whole new population of possible solutions is made of the best fitted individuals.

Fig. 1.8. The main steps of a basic genetic algorithm

A potential solution may be represented by a so called chromosome, i.e. a set of parameters known as genes joined together to form a string of values [24]. Crossover and mutation are the two main separate mechanisms, called genetic operators, used for reproduction selected parents. During crossover, chromosomes of the two taken individuals are cut at a randomly chosen position (single-point crossover), generating head segments and tail segments. Then tail segments are swapped over, producing new chromosomes. The crossover process of two 5-bit numbers, as an example, is given in Figure 1.9.


16

Fig. 1.9. The scheme of a single-point crossover

The likelihood of crossover being applied is typically between 0.6 and 1.0, so the crossover is not applied for all pairs of individuals. In such situations offspring are produced by duplicating the parents [24]. Mutation is the second basic operator applied for recombination of chromosomes of each child. It constitutes in altering of a randomly selected gene with a small probability, usually 0.001 [24, 25]. The example of the first 5-bit number from Figure 1.9 is given in Figure 1.10 [24].

Fig. 1.10. The scheme of a single mutation

Mutation plays a secondary role and protects against possible loss of desirable features of individuals that may happen during reproduction and crossover [25].

1.4. Fuzzy Logic methods

Another AI method of data handling is Fuzzy Logic (FL) approach. The method allows formalizing an empirical problem using the experience rather and the strict knowledge of the process. The main features of the FL approach are listed in Figure 1.11.

Fig. 1.11. The main features of the FL methods


17

The method applies linguistic variables and fuzzy sets to describe the behavior of a considered system allowing to deal with imprecise, vague and uncertain information, as well as qualitative judgment applied to parameters quantitative in nature. The main parts of a FL model are: fuzzifier, fuzzy rule base, the inference engine and defuzzifier (Figure 1.12).

Fig. 1.12. Main parts of a FL model

Thus Fuzzification, Inference and Defuzzification are the three main stages which make the method (Figures 1.13) [32, 33].

Fig. 1.13. The structure of a general fuzzy system

To perform a FL model, input parameters should be covered by the fuzzy sets, where a numeric value of a parameter corresponds to a membership degree μ from the range between 0 and 1. The membership function μ* of a fuzzy set is a function which maps each crisp element of a numerical domain into an interval [0, 1] [33]:

]1,0[X:* (1.5)

Thus, a membership function μ* of a fuzzy set assigns each input element x a degree of membership μ, which informs to what extent this variable belongs to the fuzzy set [33]. The conversion of crisp input values (i.e. a vectors of numeric input variables) into fuzzy sets proceeds during the fuzzification stage (Figure 1.14). During the fuzzification process a vector of crisp inputs is transformed into a vector of membership degrees, i.e. fuzzy sets µ, which can be expressed by the Zadeh’s notation (1.6):


18

n

n

2

2

1

1

x

x

x

x

x

x (1.6)

where, µ is a degree of membership of elements x in fuzzy set.

Fig. 1.14. Fuzzification and membership functions for input parameters

As an example, five input parameters (x1 , x2, x3, x4, x5) from Figure 1.14 correspond to five linguistic variables, with values divided into the overlapping tringle linguistic terms: very low (VL), low (L), average (A), high (H), very high (VH) were applied in the FL model (Figure 1.15). Therefore the notation (1.5) can be expressed by the formula (1.7):

VH

VH

H

H

A

A

L

L

VL

VL (1.7)

where: VL, L, A, H, VH are overlapping linguistic terms: very low, low, average, high and very high, respectively.


19

Fig. 1.15. Linguistic variables

Besides triangular (given in Figs. 1.14 and 1.15) other types of linguistic terms can be also used, such as trapezoidal, rectangular, gaussian, bell, sigmoidal [34]. The IF-THEN fuzzy rule base is applied during the inference stage and the fuzzy outputs are generated (Figure 1.16). In order to properly design of an IF-THEN rule base we use knowledge to deduce or infer a conclusion from facts and experience [35, 36].

Fig. 1.16. An IF-THEN fuzzy rule base

Two main methods of deductive inference for fuzzy systems can be distinguished: Mamdani [37] and Sugeno [38, 39] techniques [35, 36]. In Mamdani models a fuzzy system with two inputs (antecedents) x1 and x2 and a single output (consequent) y can be described by the set of linguistic IF-THEN rules in the form (1.8):

IF x1 is μ1 and x2 is μ2 THEN y is O , (1.8)

where O is a fuzzy set [35, 36]. In the Sugeno systems, sometimes also called the TSK models (Takagi, Sugeno, and Kang) with two inputs x1, x2 and a single output y a typical IF-THEN rule has the form (1.9):

IF x1 is μ1 and x2 is μ2 THEN y is f (x1, x2), (1.9)

where f (x1, x2) is a crisp function of the inputs x1 and x2 [35, 36]. As the results of inference within each rules of the rule base the membership functions set of conclusions are generated which should be further applied to produce one final membership function of the system. Such process is called an accumulation procedure. There are many accumulation techniques due to multiplicity of operators used in fuzzy logic approach [33].


20

The last operation is the defuzzification process, occurring in defuzzifier, which produces crisp values, corresponding to the previously established fuzzy outputs and crisp inputs (Figure 1.17).

Fig. 1.17. The defuzzification stage

The literature review also provides many different functions in the defuzzifying process: max membership principle (also known as the height method), centroid method, weighted average method, mean max membership (also known as middle-of-maxima), the center of sums, center of largest area, and first (or last) of maxima methods [33, 35, 36]. The detailed description of the methods may be found elsewhere [33-39]. The developed FL model can be used to run a non-iterative procedure to study the effects of input parameters on the output value. After the input parameter is loaded the performed FL model should be called. The application of such the non-iterative method allows to determine a crisp output value (e.g. heat transfer coefficient) as an answer for the considered set of input data

1.5. Tools [How-to]

There are many different available tools which can be used to perform a model research. It is very difficult to compare all available sowtware. Some of them are free, others commercial software. The differences between them constitute also in functionality, flexibility, capabilities and user interface. The brief description of them can be found in [17] where, e.g. AANNS (Advanced Application Neural Network Simulator) and MATLAB with Fuzzy Logic Toolbox and Neural Networks Toolbox are listed and described. In this book the author concentrated on the very useful and rather cheaper applications. During this study two software were applied: Neuro Net, by Mic-Apps Limited and Qtfuzzylite, by Juan Rada-Vilela. The fact that, the two applications were employed here can be considered by readers as a tip, as it is a result of the intense author's analysis of the accessible tools. Generally speaking, these applications are cheap and effective at the same time. They are also easy in use but powerful tools. The presented software could stand a compromise between the price and its capability and flexibility as well as the ease of the use.

1.5.1. The Neuro Net application

Neuro Net is an application available on the app store dedicated for iPad. It requires iOS 8.0 or later operating system. Neuro Net is a useful tool and ease to use even for beginners.


21

Several help pages are also provided in the software (Figure 1.18). The help includes best practices to tune an ANN to any problem.

Fig. 1.18. A help page of the Neuro Net application

This tool allows designing an effective and fully ready-to-be parametrized neural network. The software enables also to define the properties of the neural network and use an advanced genetic algorithm to train the neural network. The applied backpropagation learning algorithm permits to fine tune the solution. This software permits to define complex neural networks with up to 20 neurons arranged in up to 4 layers. This means, that maximum 20 input and 80 total neurons can be employed. The software permits to visualize of multiple input effects. Different activation functions can be chosen. Neuro Net combines both genetic algorithm and back propagation methods to obtain best results. Genetic algorithm, back propagation and network settings can be saved and recalled independently. The following information, describing the model building process, are get from help pages of the NEURO NET application [40]. Managing and optimization an ANN is carried out in portrait whereas exploring the ANN – in landscape (Figure 1.19).


22

Fig. 1.19. The How to use Neuro Net tab

The graphical user interface (GUI) is also simple and easy to use. It allows to follow both network training and convergence live (Figure 1.20). Two methods can be used to train an ANN and adopt its properties to the training set. The first method (method I) constitutes in the use of genetic algorithm approach in GA SETTINGS tab together with the network definition from the NN SETTINGS tab (Figure 1.21). During this procedure after a genetic algorithm optimization, where the area of global optimum was identified, the BP learning algorithm allows improve the network response and refine the values [40].

Fig. 1.20. An example of a network adaptation tab of the Neuro Net application


23

Fig. 1.21. The two steps in the development of the model

During this method a genetic algorithm optimization is carried out allowing most likely to locate the global optimum. The second method (method II) concentrates only in conventional backpropagation learning algorithm to perform a gradient descent procedure, described in chapter 1.2), using the settings defined in the NN SETTINGS tab (Figure 1.22). This strategy, being a computationally intensive, might however result in a local optimum, especially for complex functions.

Fig. 1.22. The back propagation learning algorithm in the development of the model

The tabs: GA SETTINGS and NN SETTINGS are given in Figures 1.23 and 1.24, respectively. Method I and Method II can be combined efficiently to optimize the model response. The GA SETTINGS tabs and NN SETTINGS tabs are used to fix genetic algorithm parameters and to control the architecture of the artificial neural network. The detailed explanation of the symbols and parameters given in the tabs of the Neural Network tool can be found in Help tabs. However, some of the most important definitions are given here. The maximum iterations parameter specifies how many individuals will be created before stopping optimization and the population defines the number of individuals that constitute the working set. The number of individuals kept in memory as a reference is called a maximum storage. These individuals can be employed for creating new ones or when reseeding a new population, when the working population losts its diversity and should be refreshed by the introduction of new members, to find a global optimum. Another important parameters are: reseed fractions and screening offset. The first one is the fraction of the population being reseeded and specifies the fraction of the population being reseeded, whilst the second helps to control how close an individual need to be, to be screened out. The maximum number of reseed defines the maximum number of times a reseed which is


24

allowed during the calculations. In order to help avoiding calculating individuals that were too similar, the screening module can be set on.

Fig. 1.23. A sample GA SETTINGS tab of the Neuro Net application

Fig. 1.24. A sample NN SETTINGS tab of the Neuro Net application


25

Mutation factor determines to which extent a selected individual can be mutated. The so called GC factor controls how different individuals are combined together whereas the K nearest neighbor parameter determines the number of closest individuals being considered before screening or rejection. In order to help the exploration process, the diversity within the population in the early stages should be maintained. Therefore the initial crowding factor is suggested to be equal 1 or close to 1. On the other hand, the elimination of diversity helps to keep convergence at the end of the optimization process. For that reason the final crowding factor, which controls the diversity in the population at the end of the optimization should be equal 0 or close to 0. Some of the most important parameters which are listed and should be determined in NN SETTINGS tab are as follows. Number of neurons in hidden layers allows to be set from 1 to 20 hidden perceptrons in each hidden layer. As it is pointed out in the Help tab the problem have to be very complex to use more than 5 hidden neurons. Number of hidden layer defines how many hidden layers are content in the network. Most problems can be solved using only one hidden layer, however complex cases need 2 or more layers. The learning rate and momentum were described in details in chapter 1.2. Back propagation maximum iteration determines how many iterations are performed using the back propagation learning algorithm. A type of neurons parameter defines the activation function of the perceptrons. Total 6 types of neuron are available, where the sigmoid ones are the most common used. A 7-th option, called free, allows the Genetic Algorithm chose different neuron types for each neuron. Therefore this option is available for the method I during Genetic Algorithms usage. The tool enables saving up to 5 different NN and GA settings. The Neuro Net application provides also a threshold for neurons activation parameter, which is an additional activation value. The threshold can be static or optimized for each neuron with the Genetic Algorithm. The learning procedure can also proceed without the use of threshold parameter. The NEURAL NETWORK INPUT tab permits to specify the learning set to train the ANN. The INPUT and OUTPUT fields allow to enter or paste in training data (Figure 1.25).

Fig. 1.25. A sample NEURAL NETWORK INPUT tab of the Neuro Net application


26

In order to explore the artificial neural network response to any input combination, the EXPLORE NETWORK OUTPUT tab should be used (Figure 1.26). This tab permits to test the network. To allow for predictions outside the training zone the bounds of the training function are extended by 20% each way. Any input can be entered at the left hand side of the screen whilst the corresponding output is reported at the top right hand corner of the screen (Figure 1.26).

Fig. 1.26. A sample EXPLORE NETWORK OUTPUT tab of the Neuro Net application

To explore the artificial neural network detailed properties, the EXPLORE NETWORK DETAILS tab can be used (Figure 1.27). The current settings of each neuron can be inspected. Moreover this tab lets to visualize multiple effects of input stimuli.

Fig. 1.27. A sample EXPLORE NETWORK DETAILS tab of the Neuro Net application


27

The PROJECT MANAGEMENT tab enables managing Projects. The SAVE an LOAD options can be applied to save a new project (or replace an existing one) and to Load a previously developed and stored in memory Project (Figure 1.28).

Fig. 1.28. A sample PROJECT MANAGEMENT tab of the Neuro Net application

Concluding, the Neuro Net is an application which allows building powerful multi-input, single-output networks for, optimization of complex functions, conducting of complex simulations, optimization or exploring design domains [40, 41].

1.5.2. The Qtfuzzylite application

The Qtfuzzylite application, developed by Juan Rada-Vilela from New Zeeland is an application for easy design and operate fuzzy logic controllers [42]. It is available on Windows (64-bit and 32-bit), Linux 64-bit, Mac, Java/Android platforms [42]. The description given in this chapter refers to version 5.0b1408 for Mac. Being an intuitive tool the application allows utilize multiple rule blocks within a single engine and write inference rules naturally [40, 42]. The user interface is divided into three main areas (Figure 1.29): The process of building up a fuzzy logic model was briefly explained in chapter 1.4. To describe in details how to use Qtfuzzylite application firstly the user should determine input and output variables (Figure 1.30).


28

Fig. 1.29. The user interface of Qtfuzzylite: 1. The Input Variable area allows to define and create inputs,

2. The Output Variable area is designated to determine outputs, 3. The Rule Block area provides the possibilities to define a rule base system.

Fig. 1.30. Sample input and output variables


29

During this process different linguistic terms can be used, including Basic (4): triangle, trapezoid, rectangle, discrete, Extended (9): bell, cosine, gaussian, pi-shape, sigmoid difference, sigmoid product, spike, Edges (5): binary, concave, ramp, sigmoid, s-shape, z-shape, Functions (3): constant, linear, function. Each term can be modified to adjust it to the model. Both Mamdani and Sugeno inference techniques, described in chapter 1.4 are provided by the software. One of the many functionalities of the application is possibility to create multiple copies of the previously performed terms used during developing of a model. When the linguistic inputs and outputs are defined, the next step is building-up the rules, according to notations given in (1.8) and (1.9). For simple fuzzy systems the rules can be written by hand (Figure 1.31). The numbers on the right hand-side of a rule indicate the instantaneous strength in which each rule is activated.

Fig. 1.31. A sample rule base

For more complex models the Rule Block Editor can be employed the Qtfuzzylite application is equipped with (Figure 1.32). Finally the inputs must be activated and the FL model should be tested. Moving the sliders under each input variable allowing to change the input values one can observe how is the response of the output for a given input data set change. The current crisp numeric values of inputs and outputs can be observed from the centered boxes above each parameter (Figure 1.33).


30

Fig. 1.32. The use of Rule Block Editor

Fig. 1.33. Testing of a FL model


31

Another functionality of the Qtfuzzylite application is the possibility to minimize the windows with linguistic variables (Figure 1.34). Such option allows to see and control more variables at the same time, without necessity to scroll the windows during building and testing of a model.

Fig. 1.34. The workbench with minimized widows of linguistic variables

In Qtfuzzylite we can also import and export code of the performed system in different formats (Figures 1.35). As an example we may e.g. export the code of a system to Fuzzy Inference System (FIS) format (Figure 1.36). This is the one format which can be also applied in Matlab and Octave software [42]. By the use of codes one can easily modify parameters describing the elements of the model, e.g. the type and shape of the linguistic terms of the input or output variables. The setting of the fuzzy engine can be also modified. Different accumulation and defuzzification methods are provided in the Qtfuzzylite (Figures 1.37 and 1.38, respectively).


32

a)

b)

Fig. 1.35. The import and export menus of Qtfuzyylite


33

Fig. 1.36. The export of the code to Fuzzy Inference System

Fig. 1.37. Different accumulation methods


34

Fig. 1.38. Different defuzzification methods

In order to illustrate the obtained results in a clear way some maps of the output variable based on different possible values of the input parameters can be generated. These maps as examples, are

given in Figure 1.39. Qtfuzzylite allows to perform models mapping the areas of operating conditions in regions where the real measurements are sometimes unavailable. Thus, this tool can be used to build models for optimization purposes. It also constitutes an alternative approach for the other methods of data handling, considering the complexity of numerical and analytical methods as well as high costs of empirical experiments.

Fig. 1.39.. Maps of the dependences of input and output variables

Due to high flexibility the Qtfuzzylite application can be easily use to describe variety processes or objects. Some examples as well as further details of Qtfuzzylite may be found in tutorials, and documentation available online [42].


35

1.6. Conclusions

The presented AI methods allow describing processes (e.g. gaseous emissions, heat transfer and adsorption) including the operation in different devices (industrial boilers, adsorption chillers, gasifiers). Each of the above described techniques constitutes an alternative approach, comparing to the other methods of data handling, considering the complexity of numerical and analytical methods and high costs of empirical experiments. Modeling by the use of AI techniques allows mapping the areas of operating conditions in regions where the real measurements are sometimes unavailable. The AI models gives quick and accurate results for any input pattern of the device operating parameters, under different conditions. The AI approach allows also developing easy to use tools, useful for modeling of the reality

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[40] https://itunes.apple.com/us/app/neuro-net/id571408140?mt=8 (20.01.2018) [41] https://www.fuzzylite.com/qt/ (20.01.2018). [42] Krzywanski J., Grabowska K., Herman F., Pyrka P., Sosnowski M., Prauzner T., Nowak W.

Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks, Energy Conversion and Management, 153:2017, 313–322.

https://itunes.apple.com/us/app/neuro-net/id571408140?mt=8

https://www.fuzzylite.com/qt/

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2. FUZZY LOGIC MODELING OF HEAT TRANSFER COEFFICIENT FOR WING WALLS IN A LARGE-SCALE CFBC

Nomenclature:

e voidage, -


T temperature, oC U flue gas velocity, m s−1 z the distance from the gas distributor, m

Subscripts b bed d desired (target) p predicted by the model

Acronyms CFB Circulating Fluidized Bed CFBC Circulating Fluidized Bed Combustor FL Fuzzy Logic MCR Maximum Continuous Rating

2.1. Introduction

Since the heat transfer processes which occur in the furnace of a circulating fluidized bed combustor (CFBC) is affected by set of complex factors [1-5] many different methods can be found to describe the heat transfer coefficient. Detailed measurements in the furnace, multiple equations, correlations and models described in the literature, can be listed as the most common approaches, used to set the heat transfer coefficient from the bed to a heating surface [5-9]. Usually heat transfer coefficients are determined during laborious, time consuming and expensive experiments. On the other hand, numerous correlations exist which also allow estimating heat transfer coefficients in the furnace. However, since there are limited experimental data in literature on the overall heat transfer coefficient in CFB boilers, the correlations, which can be found in the literature, are usually valid only for limited ranges of operating parameters and sometimes differ from one another. This limits their accuracy and generality. The models, which can be found in literature need some additional data, e.g. to adjust parameters they use. The algorithms usually base on the solutions of complex differential equations and are time consuming to obtain accurate results. Moreover, the adjusting of parameters could not be determined immediately, especially for different operating conditions. All models are no better than the hydrodynamic model on which they are based [1]. An alternative method for the above, time consuming and expensive technics of data handling is the fuzzy logic (FL) approach [10 - 16]. The application of FL techniques to study of heat transfer processes in CFB boilers is very limited in literature. Moreover, the application of fuzzy logic method is a better suited technique of data handling in the considered case, as the total number of the desired data, necessary to develop the model, is limited. Additionally, the proposed FL approach allows mapping the operating conditions where the real measurements are unavailable. Finally, allowing to deal with imprecise, vague and uncertain information, the method permits formalizing problem using the experience rather and the strict knowledge of the process. This issue is very important, as the data employed to develop the model, are difficult to acquire, since they come from a large-scale object. Therefore this chapter deals with a fuzzy logic approach to evaluate the overall heat transfer coefficient for wing walls in a large-scale CFB boiler.

2. Fuzzy logic modeling of heat transfer coefficient for wing walls in a large-scale cfbc

39

2.2. Materials and Methods

2.2.1. An object of investigations

The study was performed for the 235 MWe (670 t/h) circulating fluidized bed (CFB) boiler, operated in PGE GiEK SA Turow Power Station in Poland (Figure 2.1). In a circulating fluidized bed boilers the heat is generated due to combustion of solid fuels, e.g. coal, biomass, solid wastes via so called heating surfaces. The CFB boilers are outstanding in fuel and load flexibility as well as low pollutants emissions [1]. During their operation most of the particles, including solid fuels, are blown upward, whereas big ones remain fluidized in the lower part of the combustion chamber. Since most of the entrained particles are recycled to the dense region after their capture by the cyclone system, the name circulating fluidized bed is used for such equipment [17-19]. The considered object is a commercial CFB boiler with single furnace and natural circulation and cross-section dimensions of 21.2 x 5.2 m above the air distributor and 21.2 x 9.9 m at height of 6.7 m above the grid level. The height of the furnace is 48 m. The boiler is equipped with two cyclones of 10 m in diameter. The axis of outlet windows is located at the level of 38 m above the distributor [11]. Membrane walls (water walls), superheater I (SH I, omega superheater) and superheater II (SH II, wing walls) constitute three different kinds of heating surfaces incorporated in the combustion chamber of the boiler (Figure 2.1).

Fig. 2.1. General arrangement of heating surfaces in the 670 t/h CFB boiler

The superheater II (SH II, wing walls) is considered in this study. The objective of such heating surfaces is to generate steam as well as to extract the additional amount of heat, necessary to maintain the temperature of furnace exit gas at the level of about 850 oC. This transferring surface is hung in the upper part of the combustion chamber, i.e. in the fast bed, above the secondary air injection level of CFB riser. The wing walls extend from the front wall toward the opposite wall with furnace exit. Therefore the hydrodynamic conditions, they operate in, may by different from those on membrane-walls (water walls). The wing walls have the surface of 532 m2 and are made of tubes with the diameter of 44.5 mm each, formed in the 14 tube bundles and with 1064 mm pitch. Each bundle is composed of 28 L-shaped tubes [11].


40

2.2.2. Fuzzy logic modeling

The fuzzy-logic (FL) approach constitutes one of the main representatives of the artificial intelligence methods [11-13, 20-24]. This technique applies linguistic variables and fuzzy sets to describe the behavior of a system allowing to deal with imprecise, vague and uncertain information and formalizing an empirical problem using the experience rather than the strict knowledge of the theory [13, 25 - 28]. The detailed description of the fuzzy logic methods can be found in section 1.4. The data of the heat transfer coefficient for wing walls (SUPERHEATER II) obtained during experiments as well as modeling campaigns, carried out on the 670 t/h CFBC, operating in PGE Turow Power Plant in Poland in 2005 [3, 29] were applied for validation of the developed FL model (Figure 2.2).

Fig. 2.2. Wing walls (SUPERHEATER II) in combustion chamber of a CFB boiler

The Qtfuzzylite software which is a fuzzy logic control application was applied in the study [11, 30]. The presented model combines experience in modeling of heat transfer coefficient in CFB boilers [11, 12]. The fallowing five input parameters were assumed to develop the model: the distance from the gas distributor to the considered cross section z (i.e. the height above gas distributor in the furnace), the temperature T and voidage e of the bed, flue gas velocity U and the MCR of the boiler. Maximum continuous rating (MCR) of the boiler is considered as the actual share of the maximum load of the boiler. The overall heat transfer coefficient for wing walls constitutes the output parameter [11, 12]. The input and output values are shown in Table 2.1. Table 2.1. Parameters of entry conditions

Parameter Values

The distance from the gas distributor, m 36 – 45

Temperature, oC 800 – 900

Voidage - 0.93 – 0.99

Flue gas velocity m s-1 5 – 7

Maximum Continuous Rating, % 40 – 100

Heat transfer coefficient, W m-2K-1 144 – 206


41

Thus, five above listed input parameters (i.e.: z, T, e, U and MCR) corresponding to five linguistic variables with values divided into the overlapping sigmoid linguistic terms: very low (VL), low (L), average (A), high (H), very high (VH) were applied in the FL model (Figure 2.3).

Fig. 2.3 Membership sigmoid functions for input parameters: z, t, e, U, MCR, respectively (the abscissa and the ordinate correspond to the values of the parameters from

Table 2.1 and to the values of the membership functions, respectively)

To describe the output data, i.e. overall heat transfer coefficient for wing walls, five constant linguistic terms, i.e.: very low (VL), low (L), average (A), high (H), very high (VH), given in Figure 2.4 are applied.


42

Fig. 2.4. Membership functions for the heat transfer coefficient as the output parameter (the abscissa and the ordinate correspond to the values of the overall heat transfer coefficient from Table 2.1 and to the values of membership functions, respectively)

As it was previously mentioned a numeric value of a considered input parameter is assigned to a value of a membership function (from the range between 0 and 1). During the fuzzification process a vector of crisp inputs is transformed into vectors of membership degrees, i.e. fuzzy sets [28]. So, the membership function embodies the mathematical representation of membership in a set, according to the Zadeh’s notation (equation 1.6) [32]. The set of fuzzy rules are formulated according to the Table 2.2. The Takagi-Sugeno inference engine is used in the model to determine the fuzzy output variable [31, 32]. The weighted average method was employed during the defuzzification stage, to produce a crisp output value, as it constitute one of the most frequently used and computationally efficient methods.

Table 2.2. The fuzzy rule base

Heat transfer coefficient, W m-2K-1 VH* H A L VL

The height above gas distributor in furnace, m VL L A H VH

Temperature, oC VH H A L VL

Voidage, - VL L A H VH

Flue gas velocity, m s-1 VH H A L VL

Maximum Continuous Rating, % VH H A L VL

* VH stands for: very high, H – high, A – average, L – low, VL – very low


43

The defuzzified output o was expressed by the following expression [32]:

o

ooo

(2.1)

where o is the centroid of each membership function (i.e. points for which the membership degree is equal ½ [32]).

2.3. Results and discussion

The predicted results are located within the range of ± 4% compared to the desired data (Figure 2.5).

Fig. 2.5. Comparison of overall heat transfer coefficient values desired and predicted by the model; input parameters (according to Table 2.1): z = 36 – 45m, T = 800-900 oC, e = 0.93 – 0.99, U = 5 – 7 m s-1, MCR = 40 – 100 %

The results shown in Figure 2.5 correspond to different values of input parameters, among others heights above the gas distributor. The approach is undertaken since the comparison between desired and calculated data is regarded as the most difficult and demanding type of model's validation procedure [23]. The comparison between some of the predicted and desired values heat transfer coefficients is also given in Table 2.3. As it can be seen the relative error for some of the data, given by formula 2.2:

%100

d

pd

h

hhErr (2.2)

is lower than 1%. Such performed FL model allows to run a non-iterative procedure to study the effects of operating parameters on the local heat transfer coefficient for wing walls in the large-scale 235 MWe (670 t/h) CFB combustor. The flow chart of the model application is presented in Figure 2.6. After the input parameter is loaded the performed FL model should be called. As the result of such non-iterative method the local heat transfer coefficient for wing walls can be determined.

- 4%

+4%


44

Table 2.3 The heat transfer coefficient desired and predicted by the model

Z m

T oC

e -

U m s-1

MCR %

hd

Wm-2K-1 hp

Wm-2K-1 |Err|

%

40.0 827 0.985 5.0 100 168.12 165.97 1.28

45.0 800 0.990 5.0 40.0 146.74 144.10 1.80

40.0 850 0.985 6.0 100 173.54 174.96 0.82

40.0 850 0.985 5.0 40.0 150.79 156.67 3.90

42.8 825 0.975 5.5 55.0 162.44 160.16 1.40

40.0 850 0.985 5.0 100 168.49 168.87 0.23

36.0 850 0.985 5.0 100 168.93 174.78 3.46

39.5 850 0.985 5.0 100 168.54 169.79 0.74

42.0 850 0.985 5.0 100 168.27 166.29 1.18

45.0 850 0.985 5.0 100 167.94 162.64 3.16

Fig. 2.6. Application of the FL model for the prediction of heat transfer coefficient for wing walls

Therefore the developed FL model may be applied as a submodel or a separate module in engineering calculations, capable to predict local heat transfer coefficient for wing walls in CFB units. In order to study the impact of a specific operating input variable on the local heat transfer coefficient values for wing walls, other input parameters should be fixed as the dependence can be established only for the specified conditions. Quadratic polynomial turned out to be a function that provides the best possible approximation of the obtained results


45

2.3.1. The effect of bed temperature and voidage

The influence of bed temperature and voidage on the local heat transfer coefficient from the bed to wing walls is shown in Figs 2.7 and 2.8, respectively.

Fig. 2.7. The effect of bed temperature on the heat transfer coefficient for wing walls; U = 6 m s-1, e = 0.95

Heat transfer coefficient increases with the increase in bed temperature. It is the result of the increase both in the radiation and the thermal conductivity of gas in higher temperatures, since the radiation plays significant role at higher temperature and voidage [1, 2].

z = 37 m

z = 40 m

z = 43 m


46

Fig. 2.8. The effect of bed voidage on the heat transfer coefficient for wing walls; U = 6 m s-1, T = 850 oC

The suspension density is considered to be one of the most important operating factors influencing the heat transfer coefficient to the wing walls. It is related to the suspension density as the presence of solids reduces the thickness of the thermal boundary layers on the wall [1]. The observations made for wing walls, reported by Dutta and Basu [39] revealed that heat transfer coefficient without solids was lower than the one with solids. It is the result of the absence of particle convection and the flatter boundary layer over the wing wall with the presence of solids [39]. Moreover, because of the fact, that the contribution of heat transfer through the particles is higher than that across the fluid boundary layer the heat transfer coefficient increases with the

z = 37 m

z = 40 m

z = 43 m


47

suspension density and decreases with the increase in bed voidage. The observed behavior corresponds to the fluid dynamics of the circulating fluidized bed and the decrease in average bed voidage with the height of a considered cross-section area above the grid [1, 2, 33 – 38]. As the result the local heat transfer coefficient increases on the way down the wing walls, what is predicted by the FL model (Figs. 2.7 and 2.8).

2.3.2. The effect of gas velocity

The influence of gas velocity on the heat transfer coefficient for wing walls is shown in Figure 2.9.

Fig. 2.9. The effect of bed gas velocity on the heat transfer coefficient for wing walls; e = 0.95, T = 850 oC

z = 37 m

z = 40 m

z = 43 m


48

The increase in gas velocity leads to the increase in local heat transfer coefficient from the bed to walls. As it was explained by Duta and Basu [39] such behaviour is the result of the increase in the Reynolds number and therefore the convective heat transfer coefficient. Basu and Nag [1] pointed out, that gas velocity has an influence on the heat transfer coefficient only for dilute beds and such conditions are considered in this case. They also stated that the secondary air does not have much effect on the heat transfer coefficient in the upper part of the combustion chamber, what is in coincidence with the observations reported by Andersson and Leckner [40]. The authors underlined, that in commercial boilers primary air velocity has the main effect on the heat transfer coefficient in the furnace as more solid is being transported to the upper part of the combustion chamber for higher primary air velocities. These observations are also in agreement with other findings, given in [41 - 47].

2.3.3. The effect of the MCR

The effect of the MCR on the local heat transfer coefficient from the bed to wing walls is shown in Figure 2.10.

Fig. 2.10. The effect of the load of the boiler; e = 0.95, T = 850 oC

The overall heat transfer coefficient from the bed to wing walls increases with the value of MCR, i.e. the actual load of the boiler. It is the result of varying hydrodynamics conditions for different loads of the boiler. The solids behavior in the furnace for low and for high load corresponds to different solid circulating rate in these conditions. Since mixing processes tend to improve for higher MCR levels and solid circulating rate increases in such conditions the increase in the load results in the increase in the overall heat transfer coefficients from bed to the transferring surface [1]. As the voidage and the gas velocity are fixed during the study, the increase in the solid circulating rate can be drawn out by the particles with smaller diameter. The observations are also in coincidence with the observations reported in [1, 42].

2.3.4. Overall heat transfer coefficient for real conditions

As it was previously underlined, in order to study the effect of a specific operating input variable on the local heat transfer coefficient for wing walls, other input parameters were fixed. However, due to complex processes and dependences between input and output parameters, occurring during a CFB boiler’s operation, the modification of a selected operating parameter usually cause the change of the others, e.g. for a given MCR, the parameters: T, U, e, change simultaneously with the height z, above the gas distributor.


49

Therefore, an interesting issue seems to be the observation how the heat transfer coefficient from bed to wing walls alters in such real conditions. The calculations are performed for three different loads, i.e. 40%, 70% and 100%, when the increase of the height from 36 to 45 m above the grid accompany the increase in the bed voidage (from 0.96 to 0.99) and the decrease in the bed temperature (from 895 to 820 oC), as well as the flue gas velocity (from 5.50 to 5.22 m s–1). The calculated local transfer coefficient for wing walls is given in Figure 2.11. The obtained results are in accordance with the observations reported in [1, 2, 39]. Dutta and Basu [39] underlined, that the heat transfer coefficient decreases with the length of the surface.

Fig. 2.11. The local heat transfer coefficient for wing walls along the combustion chamber


50

This behaviour could be explained by the longer time of clusters residence on the transferring surface, which approaches thermal equilibrium with the surface while they move along it. In such a way thermal driving force, i.e. the temperature differential across the gas film, is reduced. As the result, the heat transfer coefficient on the lowest area of the surface is higher than that on the area above it [39]. Such built-up the two FL models, i.e. the first previously performed for membrane-walls (water walls) [11] and the one developed in this work for wing walls may be finally applied to compare the results at given elevation. The calculations were completed for MCR = 100 % and the other input parameters varying in the above listed ranges from Figure 2.11. The comparison is shown in Figure 2.12.

Fig. 2.12. Comparison on local heat transfer coefficient for membrane-walls [11] and wing walls along the combustion chamber

The heat transfer is higher on the membrane-walls (water walls) than on the wing walls (SH II) at a given elevation. Similar behaviour was also observed in [39]. The presented approach and the performed FL model allow mapping the areas of operating conditions in regions where the real measurements are sometimes unavailable. Thus, the optimization purposes can be the main area where the model can be applied for. The FL model can serve as an easy to run tool which can help in matching the best operating conditions with respect to achieve the highest overall heat transfer coefficient from the fluidized bed to the wing walls in the furnace of a large-scale CFB boiler. The technique also constitutes an alternative approach for the other methods of data handling, considering the complexity of numerical and analytical methods, as well as high costs and time consuming empirical experiments.

2.4. Conclusions

The FL model for the prediction of the overall heat transfer coefficient from bed to wing walls is proposed. The model is based on the fuzzy-logic techniques, which constitute one of the main representatives of the artificial intelligence methods. The heat transfer coefficient increases on the way down the wing walls. The increase in gas velocity leads to the increase in heat transfer coefficient from the bed to walls. The overall heat transfer coefficient from the bed to wing walls increases with the load of the boiler. It has been shown, that the developed model allows evaluating heat transfer coefficient from bed to wing walls in real conditions. The applied approach gives quick and accurate results


51

as an answer to the input data sets. The local heat transfer coefficients evaluated using the developed model are in good agreement with the desired data. The FL model constitutes an easy to use optimization tool, helpful in the further research of the local heat transfer coefficients for wing walls in the combustion chambers of the large-scale CFB boilers

2.5. References

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[23] J. Krzywanski, T. Czakiert, A. Blaszczuk, R. Rajczyk, W. Muskala, W. Nowak, A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach. Part 1. The mathematical model of SO2 emissions in air-firing, oxygen-enriched and oxycombustion CFB conditions, Fuel Processing Technology 137 (2015) 66-74.

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[35] W. Muskała, J. Krzywański, T. Czakiert, W. Nowak, The Research of CFB boiler operation for oxygen–enhanced dried lignite combustion, Rynek Energii, 1 (92) (2011) 172–176.

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3. NEUROCOMPUTING APPROACH FOR HYDROGEN PRODUCTION VIA CaO SORPTION ENHANCED GASIFICATION OF SAWDAST IN FLUIDIZED BED

Nomenclature:

Bi, L bias neuron i on a layer L, - CaO/C CaO to carbon mole ratio, - ER equivalence ratio of air,- CH2 hydrogen concentration in syngas, % H2O/C H2O to carbon mole ratio, - T reaction temperature, oC Wi,L,k weight between a neuron i on a layer L and a neuron k on a layer L+1, -

Greek symbols δ the relative error, %

Subscripts calc calculated exp measured i, k numbers of neurons, i = 0 - 4, k = 0-2 L number of a layer, L = 0 - 3,

Acronyms ANN Artificial Neural Networks, GHG Greenhouse gas.

3.1. Introduction

Taking advantage of the photosynthesis of green plants, biomass is considered as a clean, renewable and CO2 neutral source of energy [1-3]. It is suggested to become the fuel which will provide 13 % of the total energy consumption by 2020 [4]. Solid fuels (e.g. charcoal, biochar), liquid fuels (e.g. biodiesel, vegetable oil, ethanol) and gaseous fuels (e.g. biogas, syngas) are produced from biomass due to its bulkiness, low energy density and inconvenient form with respect to transport. Two major routes of biomass conversion may be distinguished: biochemical (digestion or fermentation) or thermochemical conversion (e.g. combustion, torrefaction, liquefaction, gasification) [1]. Gasification is listed as one of the most economical in long term method of bioenergy conversion, offering a wide range of applications for syngas and hydrogen production [5]. Hydrogen is a clean energy carrier considered as one of the most promising energy source with great potential to replace of fossil fuels [6]. It has the highest energy density of all fuels and energy carriers and when combusted it does not emit any pollutants [7]. Moreover, hydrogen is not only employed as an energy carrier. It is also widely used in fertilizer, food, glass, electronics and metallurgical and cosmetics industry [7]. The hydrogen production is nowadays mostly based on fossil fuels, such as natural gas, coal and oil, and leading to produce CO2 cannot be treated as a “zero carbon” fuel [4]. Only 4% of hydrogen is generated from renewable sources [7]. Biomass is considered as one of the promising renewable sources of for H2 production and its thermochemical gasification is now one of the most important applications for its production [1, 5, 6, 8, and 9]. Two major typologies of gasifiers can be distinguished: fixed and fluidized bed. Particular attention should be paid to gasifiers with fluidized bed as they provide good mixing conditions of fuel and bed material [10]. However undesirable CO2 and tar formation are the main obstacles in

3. Neurocomputing approach for hydrogen production via cao sorption enhanced gasification of sawdast in fluidized bed

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biomass gasification technologies, leading to the deterioration of system efficiency [2, 6]. A common tar reduction method constitutes in the use of cheap and abundant CaO-contained minerals, such as limestone or dolomite. It can be supplied either inside of the reaction chamber (primary methods) or downstream (secondary methods) [2, 6]. The introducing of this catalyst into the bed leads to tar reduction, in situ CO2 capture and increase in hydrogen output [2, 6, 10]. The above literature review confirms the beliew, that the experiments belongs to the basic cognitive methods in this field, as most of discussed in literature results are usually obtained via expensive and laborious measurements on real objects. However big challenge, high costs, time-consuming and limitations make the experiments sometimes an insufficient method of data mining. An alternative method of data handling constitutes a mathematical modeling approach. Few studies, using a computer-based models, refer to hydrogen production from biomass gasification in the presence of CaO. A thermodynamic model for exergy based calculations was developed in [8]. Several parameters, such as: relative irreversibility, productivity lack, exergetic factor, and improvement potential as well as energy and exergy eficiencies were included during calculations [8]. A model for the gasification of biomass in an atmospheric fluidized bed gasifier using the Aspen Plus simulator was shown in [11]. The increase in temperature improved both hydrogen production and carbon conversion efficiency. Stanjan software (v 3.93L) was employed to develop a thermodynamic equilibrium model to predict the chemical composition of the products from wood gasification in fixed bed [12]. The H2 production decreases with the increase in equivalence ratio. A semi-kinetic model on the basis of an Aspen Plus was described in [5]. The influence of some critical parameters, such as gasification temperature, equivalence ratio and steam/biomass ratio on hydrogen yield and CO2 absorption ratio have been studied [5]. This chapter introduces artificial neural networks approach to study the effect of critical operating variables, i.e. CaO to carbon mole ratio (CaO/C), H2O to carbon mole ratio (H2O/C), reaction temperature (T) and equivalence ratio of air (ER) on H2 production from biomass gasification in fluidized bed reactor. The developed model allows predicting hydrogen concentration in syngas produced from sawdust as a typical biomass resource


The considered biomass gasification process belongs to the thermochemical techniques of H2 productions [1, 2]. It is a novel process of hydrogen production since the employed CaO sorption enhances biomass anaerobic gasification. By this attractive method the produced syngas contains high H2 concentration [2]. The CaO sorbent, supplied directly into the gasifier, serves as a biomass tar reduction catalyst as well as a heat carrier for endothermic gasification reactions and sorbent for in situ CO2 capture [2, 13]. The experimental work reported here was carried out at State Key Laboratory of Clean Energy Utilization, Department of Energy Engineering, Zhejiang University, Hangzhou, China in [2]. The considered lab-scale facility consists of bubbling fluidized bed gasifier made of stainless steel tube with inner diameter of 0.04 m and of the 2 m height (Figure 3.1). The test rig was also equipped with six electric heaters to warm up the gasifier. The system was supplied by the gasifying agent (steam, oxygen and their mixture) sawdust and CaO. The detailed description of the facility can be also found elsewhere [2].


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Fig. 3.1. The schematic diagram of the lab-scale facility [2]

The fraction 0.15 – 0.25 mm of sawdust and CaO was employed during the tests. The specific surface area and pore volume of CaO were 12-18 m2/g and 0.08-01. cm3/g, respectively [2]. The proximate and ultimate analyses of sawdust are given in Table 3.1.

Table 3.1. Properties of sawdust [2]

Lower heating value LHV, MJ kg–1 21.06

Proximate analysis (as received), wt. %

Moisture 14.85

Volatile matter 68.41

Ash 0.45

Fixed carbon 16.3

Ultimate analysis (dry ash-free), wt. %

C 50.90

H 3.76

O 45.03

N 0.266

S 0.047

As it was mentioned the CaO sorption enhanced anaerobic gasification of sawdust in a bubbling fluidized bed is nowadays considered as one of the most effective way of H2 production. The experimental results reported in [2] were applied to develop and validate the model. The Neuro Net application was used to predict the hydrogen concentration in syngas CH2 during the CaO sorption enhanced anaerobic gasification of biomass. Reasons behind choosing


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the software are described in chapter 1.5. The tool allows to use an artificial neural network (ANN), previously trained for the considered process. The method II of the Neuro Net application was used. This method was described in details in chapter 1.5.1. The model research has been carried out during the internships in Zhejiang University in Hangzhou, China, in April 2017r. The following values: CaO/C mole ratio, H2O/C mole ratio, Reaction temperature T, and Equivalence ratio of air ER are assumed as input parameters. Theirs range of values are given in Table 3.2. The H2 concentration in syngas constitutes the output parameter. Such selected input and output parameters, corresponding to the wide range of operating parameters made the input-output data set. This set was uploaded and used to develop the model.

Table 3.2. The input parameters used in the study

Input Parameter Value

CaO/C mole ratio, - 0 – 2

H2O/C mole ratio, - 1.2 – 2.33

Reaction temperature T, oC 489 – 850

Equivalence ratio of air ER, - 0.0 – 0.32

The equivalence ratio is determined as the ratio of the amount of oxygen supplied in relation to the amount of air needed for stoichiometric combustion [6, 12]. The method constitutes in the application of the ANN approach with conventional, common used and very effective backpropagation learning algorithm [14]. Since the experimental results are limited the total number of learning data employed to develop model included 18 samples of which 5 were independent, validation data set, similar to the procedure given in [15-18]. The determined neural network’s parameters are described in the next sections.


Number of neurons in input and output layer of an ANN corresponds to the number of input and output parameters, respectively [16, 17]. Since the four input parameters are assumed to be, i.e.: CaO to carbon mole ratio (CaO/C), H2O to carbon mole ratio (H2O/C), reaction temperature (T) and equivalence ratio of air (ER) the number of neurons in input layer was also set to 4. The H2 concentration in syngas constitutes the output parameter. As the performance and accuracy of an ANN model are highly dependent on its structure, different ANN architectures have been tested during the study [15-17]. Multilayers perceptrons with various hidden layers and different neurons in each of them were considered during the study. The optimal network turned out to be [4-3-3-1] with two hidden layers and is composed of three sigmoid neurons (i.e. neurons with sigmoid activation function) in each hidden layer. The structure of the neural network is given in Figure 3.2.


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Fig. 3.2. The structure of the [4-3-3-1] type of neural network

The momentum back propagation learning method was applied in the model [18]. The learning rate and the momentum were set to 0.05 and 0.03, respectively. The structure and detailed description of the [4-3-3-1] type of ANN are given in table 3.3.

Table 3.3. The [4-3-3-1] type of ANN detailed properties

Weights, Wi, L, k*

W0, 0, 0 8.1570

W0, 0, 1 16.706

W0, 0, 2 5.7400

W1, 0, 0 -1.6230

W1, 0, 1 0.2730

W1, 0, 2 2.5260

W2, 0, 0 9.0920

W2, 0, 1 0.8420

W2, 0, 2 2.9180

W3, 0, 0 -1.1310

W3, 0, 1 -4.0210

W3, 0, 2 2.2670

W0, 1, 0 -9.4260

W0, 1, 1 -0.3080

W0, 1, 2 0.0180

W1, 1, 0 6.3950

W1, 1, 1 7.9010

W1, 1, 2 -0.1790

W2, 1, 0 -1.2400

W2, 1, 1 0.4150

W2, 1, 2 -0.0270

W0, 2, 0 -3.1080

W1, 2, 0 9.9660

W2, 2, 0 -0.4170


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Table 3.3(continued). The [4-3-3-1] type of ANN detailed properties

Neuron Bias, Bi, L*

B0, 1 - 10.844

B1, 1 14.350

B2, 1 3.0360

B0, 2 - 7.4480

B1, 2 - 9.5440

B2, 2 0.0910

B0, 3 -0.7780

* neurons on a layer and layers in the ANN in Figure 3.2 are numbered from top to bottom (i = 0 – 3), (k = 0 – 2) and from the left to the right (L = 0 – 3), respectively.

The Table 3.3 contains very important information useful to perform the artificial neural network. The data summarize and provide the necessary values describing the ANN construction, trained and developed for the considered process of H2 production.

3.3.1. Validation

The comparison between results obtained experimentally and calculated (generated by the model) is given in Figure 3.3. The data correspond to different input parameters, both known by the ANN, used during training stage (dependent data) as well as new, previously unseen by the ANN (independent data). Maximum relative error of calculations is lower than ± 10 % compared to the measured results.

Fig. 3.3. Comparison of the H2 concentration in syngas desired (measured) and predicted (calculated) by the model; (red points correspond to dependent whilst the blue ones – to independent, new data)

As it can be seen from Figure 3.3 good accuracy in the prediction by the model of H2 concentration in syngas was also obtained for the new, previously unseen by the ANN, independent data set. Some of the selected results, calculated by the model, are given in Table 3.4. As it can be seen the relative error for some of the results is even lower than 1 %.


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The knowledge of the process, acquired by the network during the training stage, is stored in the ANN structure [14, 16, 17]. Therefore all the factors describing the ANN performance, i.e. weights, activation function of neurons, the number of perceptrons in hidden layers and the type of ANN, should be considered as indicators which are the best tuned to the analyzed task, defining the whole neural network.

Table 3.4. The CH2, predicted by the [4-3-3-1] type ANN and obtained from measurements

CH2exp CH2calc δ

% %

Dat

a

no

t use

d f

or

trai

nin

g n

etw

ork

50.2 51.6 2.79

59.1 59.5 0.68

47.0 49.0 4.26

49.4 53.0 7.29

Dat

a

use

d f

or

trai

nin

g n

etw

ork

45.0 44.8 0.44

50.3 51.6 2.52

62.2 61.8 0.64

56.3 54.8 2.67

58.0 59.5 2.58

The approach, which consists in the use of artificial neural networks, is also known as the neurocomputing approach, where the ANNs have the ability to reproduce a process from training samples [14, 19] The developed model is a tool which is capable to predict H2 concentration in syngas via a non-iterative procedure, so it allows generate quick and accurate results as an answers corresponding to new stimuli (independent input data). The flow chart of the model application is shown in Figure 3.4. The calculations procedure is simple, since one needs only enter input parameters, i.e. CaO/C, H2O/C, T, and ER to determine the H2 concentration in syngas.

Fig. 3.4. Application of the model for the prediction of H2 concentration in syngas

The developed model is also capable to study the effect of the operating parameters on the H2 production. Similar to the previous studies, in order to evaluate the influence of a specific input variable on the hydrogen concentration in syngas, other input parameters should be fixed, as the dependence can be established only for the specified conditions [15-18].


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3.3.2. Influence of operating parameters on hydrogen production

Since the Neuro Net tools allows extending the bounds of the input parameters by 20 % each way to make predictions outside of the training zone, the study of the influence of input parameters on H2 output can be carried out for the ranges from Table 3.5.

Table 3.5. The extended ranges of input parameters


CaO/C mole ratio, - 0 – 2.4

H2O/C mole ratio, - 1.0 – 2.8


Equivalence ratio of air ER, - 0.0 – 0.38

The relationship between input parameters, i.e. CaO/C, H2O/C, T and ER should be considered taking into account important reactions which occur during CaO sorption enhanced biomass gasification. They all can be found in [2].

3.3.2.1. Effect of CaO/C

The influence of CaO to carbon mole ratio (CaO/C) is shown in Figure 3.5. The increase in the CaO/C mole ratio leads to the increase in hydrogen production. This behaviour can be explained by absorption of CO2 with CaO via the following reaction of carbonation:

32CaCOCOCaO (3.1)

lowering partial pressure of CO2 and moving in forward direction the water gas shift reaction [2]:

222HCOOHCO (3.2).

Fig. 3.5. Effect of CaO/C on H2 production (H2/C = 1.2, T = 740 oC, ER = 0)

The presence of CaO also promotes the hydrocarbons reforming reactions (3.3) and (3.4):


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224H3COOHCH (3.3)

22ba

H2/baaCOOaHHC (3.4)

leading to the increase in H2 concentration in syngas [2]. Additional reason for the increase in hydrogen output can be attributed to the enhancement of tar reforming (cracking) reaction (3.5) by the presence of CaO sorbent [2, 20, 21]:

...nshydrocarboCOCOHOHTars22

CaO

2 (3.5)

promoting the hydrogen yield. These conclusions are also in coincidence with the observations reported by Hang et al. [22]. The authors pointed out that CaO may act as a catalyst in the reduction reactions of such tar species, as: toluene or phenol. However all these mechanisms have their own edges and the increase of CaO/C above 2 has little effect on H2 generation.

3.3.2.2. Effect of H2O/C

The influence of H2O to carbon mole ratio (H2O/C) is depicted in Figure 3.6. The increase in H2O/C mole ratio leads to the increase in hydrogen production. To explain this relationship it is worth to bring back the observations of Manovic et al. [23]. The authors reported that steam hydration would promote the carbonation reactivity of CaO sorbents.

Fig. 3.6. Effect of H2O/C on H2 production (CaO/C = 1, T = 740 oC, ER =0)

In such conditions more CO2 can be absorbed via the reaction (3.1) what further shifts the thermodynamic equilibrium of water gas shift reaction (3.2) and then the water gas reactions (3.6) and (3.7):

22mnH)2/)2m2n((COOH)y1(OCH (3.6)

22HCOOHC (3.7)

towards the forward direction to produce more H2 [2]. However Hang et al. [2] pointed out that taking into account the ideal global gasification reaction (3.8):


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232mnH)2/)4m2n((CaCOCaOOH)y2(OCH (3.8)

a H2O/C ratio of 1.3 would be sufficient for complete of sawdust conversion. In the considered real conditions it happens for higher levels of H2O/C (Figure 3.6) and further increase in H2O to carbon mole ratio have a limited impact on the H2 yield.

3.3.2.3. Effect of temperature

The influence of temperature on hydrogen production is given in Figure 3.7. The H2 concentration in syngas first increases and then decreases with the increase in reaction temperature with maximum at 838 oC.

Fig. 3.7. Effect of temperatures on H2 production (CaO/C = 1, H2O/C = 1.7, ER =0)

The increase in H2 yield with temperature is mainly caused by the promotion of the reforming reactions (3.3) – (3.5) as well as the acceleration of water gas shift reaction (3.2) [2, 24]. However, besides the above reactions, the thermodynamic equilibrium between carbonation (3.1) and calcination reaction (3.9):

23COCaOCaCO (3.9)

has also significant impact on the H2 output. For temperatures higher than the equilibrium value the carbonation is inhibited and the dominant reaction is calcination (3.9) leading to the increase in CO2 yield and moving in backward direction the water gas shift reaction (3.2) lowering the H2 production. Moreover, such caused increase in the CO concentration leads to move also in backward direction the water gas reactions (3.6) and (3.7), resulting in additional decrease in H2 generation. Therefore the maximum in H2 production can be considered as the result of the compromise between the biomass gasification efficiency and CaO carbonation [2].

3.3.2.4. Effect of the equivalence ratio of air (ER)

The impact of equivalence ratio of air (ER) on H2 concentration is rather small (Figure 3,8). The H2 output slightly decreases with the increase in the ER. To explain the observed dependence it is necessary point out that the gasification agent constitutes a mixture of O2 and steam for ER > 0 whilst for ER = 0 the gas is only composed of steam. When O2 is absent in the supplied gas the char combustion processes are inhibited, leading to the decrease in CO2 formation. Lower partial pressures of CO2 move the water gas shift


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reaction (3.2) in forward direction, leading to both: the increase in H2 concentration and significant consumption of CO. Such conditions shift the equilibrium of water gas reactions (3.6) and (3.7) towards forward direction to produce more H2 [2].

Fig. 3.8. Effect of the equivalence ratio of air on H2 production (CaO/C = 1, H2O/C = 2, T = 850 oC)

On the other hand the increase in ER leads to both enhanced consumption of combustible gases including H2 and production more H2O and CO2 as well as to the dilution of the product gas by the excess air [6]. Therefore, the increase in ER causes the decrease in H2 concentration in the syngas.

3.3.3. The best strategy for hydrogen production

Taking into account the observed trends in the H2 yield behavior the effects of input parameters can be described as it was shown in Figure 3.9. The optimization issues of hydrogen generation can be performed by the selection of the best suited input factors [25, 26]. The highest value of the H2 concentration in syngas, which may be obtained for the considered range of input operational parameters, is equal 64 %.

Parameter (the abscissa) H2 production (the ordinate)

CaO/C mole ratio, -

H2O/C mole ratio, -

Reaction temperature T, oC

Equivalence ratio of air ER, -

Fig. 3.9. Effect of increase in input parameters on H2 production


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3.4. Conclusions

This section deals with a very attractive method of the hydrogen production. Artificial neural network approach was used to develop a non-iterative model allowing to conduct optimization study of the hydrogen production process via CaO sorption enhanced gasification of sawdust in a bubbling fluidized bed. The H2 concentration in syngas, evaluated by the model, is in a good agreement with the experimental results. Maximum relative error between measured and calculated data is lower than ± 10%. The increase in the CaO/C and H2O/C mole ratios leads to the increase in hydrogen production. The H2 concentration in syngas first increases and then decreases with the increase in reaction temperature with maximum at 838 oC. The H2 output slightly decreases with the increase in the ER. The highest value of the calculated H2 concentration in syngas, which can be reached for the considered range of input operational parameters, is equal 64 %. The developed model constitutes an easy-to-use and powerful optimization tool which allows estimating the hydrogen output from the presented environment-friendly hydrogen production method.

3.5. References

[1] P. Basu, Biomass gasification and pyrolysis: practical design and theory. Academic press, (2013).

[2] L. Han, Q. Wang, Y. Yang, C. Yu, M. Fang, Z. Luo, Hydrogen production via CaO sorption enhanced anaerobic gasification of sawdust in a bubbling fluidized bed. International journal of hydrogen energy, 36, (2011) 4820-4829.

[3] J. Krzywanski, R. Rajczyk, M. Bednarek, M. Wesolowska, W. Nowak, Gas emissions from large scale circulating fluidized bed boilers burning lignite and biomass, Fuel Processing Technology, 116, (2013) 27 - 34.

[4] G. Nahar, D. Mote, V. Dupont, Hydrogen production from reforming of biogas: Review of technological advances and an Indian perspective. Renewable and Sustainable Energy Reviews, 76, (2017) 1032-1052.

[5] M. H. Doranehgard, H. Samadyar, M. Mesbah, P. Haratipour, S. Samiezade, High-purity hydrogen production with in situ CO2 capture based on biomass gasification. Fuel, 202 (2017) 29-35.

[6] J. Udomsirichakorn, P. A. Salam, Review of hydrogen-enriched gas production from steam gasification of biomass: the prospect of CaO-based chemical looping gasification. Renewable and Sustainable Energy Reviews, 30, (2014) 565-579.

[7] P. Parthasarathy, K. S. Narayanan, Hydrogen production from steam gasification of biomass: influence of process parameters on hydrogen yield–a review. Renewable Energy, 66 (2014) 570-579.

[8] Y. Kalinci, A. Hepbasli, I. Dincer, Biomass-based hydrogen production: a review and analysis. International journal of hydrogen energy, 34, (2009) 8799-8817.

[9] M. R. Mahishi, D.Y. Goswami, An experimental study of hydrogen production by gasification of biomass in the presence of a CO2 sorbent. International Journal of Hydrogen Energy, 32 (2007) 2803-2808.

[10] E. Bocci, M. Sisinni, M. Moneti, L. Vecchione, A. Di Carlo, M. Villarini, State of art of small scale biomass gasification power systems: a review of the different typologies. Energy Procedia, 45, (2014) 247-256.

[11] M.B. Nikoo, N. Mahinpey, Simulation of biomass gasification in fluidized bed reactor using ASPEN PLUS. Biomass and Bioenergy, 32 (2008) 1245–54.


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[12] M. R. Mahishi, D. Y. Goswami, Thermodynamic optimization of biomass gasifier for hydrogen production. International Journal of Hydrogen Energy, 32, (2007) 3831-3840.

[13] L. Han, Q. Wang, Q. Ma, C. Yu, Z. Luo, K. Cen, Influence of CaO additives on wheat-straw pyrolysis as determined by TG-FTIR analysis. Journal of Analytical and Applied Pyrolysis, 88 (2010) 199-206.

[14] J. Krzywański, W. Nowak, Neurocomputing approach for the prediction of NOx emissions from CFBC in air-fired and oxygen-enriched atmospheres, Journal of Power Technologies, 97 (2017) 75-84.

[15] J. Krzywanski, W. Nowak Artificial Intelligence Treatment of SO2 Emissions from CFBC in Air and Oxygen-Enriched Conditions, J. Energy Eng. 142 (2016) Issue 1, Article Number 04015017.

[16] J. Krzywanski, T. Czakiert, A. Blaszczuk, R. Rajczyk, W. Muskala, W. Nowak, A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach, Part 1. The mathematical model of SO2 emissions in air-firing, oxygen-enriched and oxycombustion CFB conditions, Fuel Processing Technology 137 (2015) 66 - 74.

[17] J. Krzywanski, T. Czakiert, A. Blaszczuk, R. Rajczyk, W. Muskala, W. Nowak, A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach Part 2. SO2 emissions from large- and pilot-scale CFB boilers in O2/N2, O2/CO2 and O2/RFG combustion atmospheres, Fuel Processing Technology 139 (2015) 73–85.

[18] M. Nalecz, Biocybernetyka i inżynieria biomedyczna 2000. Tom 6 Sieci Neuronowe. Warsaw: Exit; 1999.

[19] R.R. Jensen, S. Karki, H. Salehfar, Artificial neural network-based estimation of mercury speciation in combustion flue gases, Fuel Process. Technol., 85 (2004) 451-462.

[20] G. Xu, , T. Murakami, T. Suda, S. Kusama, T. Fujimori, Distinctive effects of CaO additive on atmospheric gasification of biomass at different temperatures. Ind. Eng. Chem. Res., 44 (2005) 5864-5868.

[21] N. H. Florin, A. T. Harris, Mechanistic study of enhanced H2 synthesis in biomass gasifiers

with in‐ situ CO2 capture using CaO. AIChE journal, 54 (2008) 1096-1109. [22] L. Han, Q. Wang, Q. Ma, C. Yu, Z. Luo, K. Cen Influence of CaO additives on wheat-

straw pyrolysis as determined by TG-FTIR analysis. Journal of Analytical and Applied Pyrolysis, 88, (2010) 199-206.

[23] V. Manovic, D. Lu, E. J. Anthony, Steam hydration of sorbents from a dual fluidized bed CO2 looping cycle reactor. Fuel, 87 (2008) 3344-3352.

[24] T. Hanaoka T., T. Yoshida , S. Fujimoto , K. Kamei, M. Harada, Y. Suzuki, H. Hatano, S. Yokoyama, T. Minowa, Hydrogen production from woody biomass by steam gasification using a CO2 sorbent. Biomass and Bioenergy, 28 (2005) 63-68.

[25] Lewandowski W. M. Proekologiczne źródła energii odnawialnej.Kompendium (Pro-ecological renewable energy sources. A compendium) PWN, 2017.

[26] Chmielniak T. Technologie energetyczne (Energy technologies). WNT 2013.

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4. FUZZY LOGIC TREATMENT FOR NOx EMISSIONS FROM CALCIUM LOOPING PROCESS IN FLUIDIZED BEDS

Nomenclature:

C_CO&CO2 total concentration of CO and CO2 in flue gas from absorber C_O2 oxygen concentration in flue gas from regenerator, % CR_N conversion of fuel-N to NOx in regenerator, [-] F fuzzy set FC fixed carbon, wt. % N nitrogen content, wt. % VM volatile matter content, wt. %

Subscripts d desired (measured) p predicted (calculated)

Acronyms AI Artificial Intelligence BFB Bubbling Fluidized Bed CaL Calcium Looping CFB Circulating Fluidized Bed CFBC Circulating Fluidized Bed Combustor daf. dry ash-free-basis DFB Dual Fluidized Bed FB Fluidized Bed FL Fuzzy Logic PG Primary Gas SG Secondary Gas

4.1. Introduction

Apart from oxy-fuel combustion, and chemical looping combustion calcium looping (CaL) process is regarded as one of the promising post-combustion CO2 capture technology [1 - 9]. It is a dry absorption/desorption process carried out in two reactors, i.e. in absorber (carbonator) and regenerator (calcinator) as it is based on a reversible reaction for absorption of CO2 by solid absorbent CaO (Eq. (4.1)) and decomposition of CaCO3 (Eq. (4.2)) [4]:

32 CaCOCOCaO (4.1)

23 COCaOCaCO (4.2)

The reaction of carbonation (4.1) proceeds in the carbonator whilst the CaCO3 decomposition (4.2) is conducted in the regenerator. To recirculate solid absorbent the solid transportation line connects the absorber to the regenerator [3, 4]. The reaction temperature in the absorber with CaO carbonated to CaCO3 is fixed at approximately 873 K. The CaCO3 is then transported to the regenerator with temperature maintained at approximately 1223 K, where CaCO3 is regenerated to CaO, via the reaction (4.2) and high purity CO2 is recovered. The obtained mixture of CO2 produced in regenerator and CO2-rich flue gas from oxyfuel combustion can be compressed then for geological storage [3, 4, 8, 10]. To avoid hot-spot formation and assure effectively heat and solids supply remove from the reactors, the existing systems usually employ fluidized bed reactors for both the carbonator

4. Fuzzy logic treatment for nox emissions from calcium looping process in fluidized beds

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and regenerator [3, 4]. Fast fluidized bed (circulating fluidized bed or riser) is often applied for regenerator whereas bubbling, turbulent fluidized bed or fast fluidized beds are usually used for the carbonator [4]. The dry absorption/desorption CaL using dual-fluidized bed (DFB) solid circulating system, being regarded as an energy efficient and low cost, post-combustion CO2 capture technology, can also work as a multi-functional process for not only CO2 capture but also for NOx abatement in flue gas from air-blown combustors [5]. On the other hand the CaL process is designed for oxycombustion of coal, as a practical fuel, to supply the necessary heat for endothermic reaction (4.2). Fuel is burned in pure oxygen (oxyfuel combustion) so the flue gas consists mainly of CO2 and H2O, thus it would be suitable for geological storage [4]. However, since the fuel NOx in flue gas, can be converted to nitric acid, the evaluation of NOx formation in the regenerator is also of concern when considering the CaL process [3-6]. The emission of NOx (i.e., NO + NO2) is affected by complex factors and is the result of competing formation and destruction mechanisms [9]. Among them are: volatile and nitrogen content, primary/secondary gas ratio, local temperature and oxygen partial pressure in the furnace, the presence of calcined limestone in the combustion chamber and gas residence time in the furnace as well as the geometry of the system [11-42]. Many works also deal with the fuel-N to NOx emissions from solid fuels combustion in CFB units under oxygen-enriched atmospheres can [16, 17, 19, 24-27, 36-39, 41, 43]. The authors underlined, that staged combustion is a useful method for the decrease of NOx emission [12, 28]. They also pointed out, that the complex influence of the limestone addition on NOx formation. For high-volatile fuels (e.g. Minto coal - 33.1% of VM) calcined limestone surface acted as a catalyst for oxidation of volatile nitrogen whereas for coals with low volatile content (e.g. 10% for petroleum coke) the presence of calcined limestone behaved as a catalyst for NO reduction by CO [42]. These results are consistent with the opinion that low rank coals can yield more NOx than higher rank ones [20, 23, 42]. The observations reported by Feng et al. [20] revealed that during combustion of coal with 20.9% VM of in CFB combustor the NOx concentrations increased with the increase in Ca/S ratio as calcined limestone surface acted as a catalyst for volatile nitrogen oxidation [42]. The above literature review reveals, that many of the results, dealing with NOx formation, were obtained via measurements on the existing objects. However a big challenge, as well as the high costs and time-consuming as well as limitations make the experiments often an insufficient method of data mining [11, 44-46]. An alternative can be mathematical modeling. Different in details or/and sophistication models of solid fuels combustion in fluidized beds can be found in literature. Some of them refer to solid fuels combustion in air-fired and oxy-fuel conditions [15, 22, 35, 47 - 67] and others to calcium looping processes [68-82]. A review of the existing CFBC models can be found in [15]. The author discussed two approaches to the performance modeling, i.e. furnace approach and system approach. The furnace approach describes the details of what goes on in the furnace whereas the system approach takes an interest in system integration. Three levels of details or/and sophistication can be distinguished in the furnace approach: level I: 1-D, plug flow/stirred tank, using simple mass and energy balance; level II: core-annulus, 1.5-D with broad consideration of combustion and other related processes; level III: 3-D model based on Navier-Stokes Equation with detailed consideration of chemical kinetics and individual physical processes [11]. A review of semi-empirical, macroscopic models for fluid dynamics of circulating fluidized bed boilers was provided by Pallares and Johnsson [68]. The time-consuming issue of CFD methods was pointed out by Zhou et al. [61]. The Euler-Euler modeling approach, employed in simulations of coal combustion under oxycombustion O2/CO2 and O2/RFG mode, conducted on the platform of FLUENT 12 software with three processors parallel for 70 s with the number of meshes nearly 8500 cost nearly 60 days [44, 61].


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Several models of calcium looping (CaL) processes can be also found in the open literature. Some of them consider the whole CaL system (full-loop), others only carbonator or calciner [68-82]. A review of the existing data with respect of scaling and hydrodynamic similarity issues of dual fluidized bed (DFB) was given in [69]. A 1D dynamic calcium looping models were developed and discussed in [70, 71], whereas a 1.7 MW pilot plant calciner with 1-D and 3-D approaches can be found in [72]. A simplified kinetic and attrition models empirically oriented for 10 kWth dual fluidized calcium looping facility were performed by Glykeria Duelli (Varela) et al. [74]. Ströhle et al. developed a 1D CFB model using ASPEN PLUSTM package [75]. Atsonios et al. [76] reported an advanced methodology, referred to as Numerical Tools Combination Methodology for modeling of CaL in DFB system. A new approach in modeling of carbonator was presented by Ortiz et al. [78]. The model was based on lab-scale multicyclic CaO conversion results in which calcination is carried out under high CO2 partial pressure simulating real conditions. A model of carbonator, based on the Kunii–Levenspiel theory for circulating fluidized bed was developed by Romano [80]. Cordero and Alonso [81] proposed a Random Pore model. Lasheras et al. [82] performed a carbonate looping process simulation using a 1D fluidized bed model for the carbonator. As the nature of the industrial processes is often non-linear and sometimes extremely complex, models usually include some empirical parameters to provide necessary data in the cases where up-to-date modeling is unsatisfactory [11, 44, 67]. It happens e.g. when adjusting parameters of the model, which could not be determined immediately, especially for different operating conditions [11, 44]. Models are sometimes time consuming. Time required to conduct the numerical test can be fairly long to acquire accurate predictions, in spite of the fact that they usually use some simplified assumptions to obtain a tractable solution, making the models simpler and easier [11, 44, 61]. The algorithms are complicated and, as usual, are based on the solution of complex differential equations. These features are listed among the main shortcomings of the programmed computing approach [44, 45]. Another estimation method in engineering analysis and predictions encompasses the use of the artificial intelligence (AI) technics. Apart from artificial neural networks and evolutionary computations, fuzzy-logic (FL) constitutes one of the main and promising representatives of the artificial intelligence approach [11, 33, 34, 44 – 46, 83-91]. The above literature review shows that CO2 capture process is the main focus of the papers dealing with the calcium looping issues. However the CaL technology, being a multifunctional process, can also serve as a method for NOx abatement from flue gas of air-blown combustors [3-5]. Because of the complexity of the conversion of fuel-N to NOx, the discussion about the dominant mechanism over the NOx formation is still open, especially in oxy-fuel conditions in CaL systems. The fuzzy logic approach was employed to predict the NOx (i.e., NO + NO2) emissions from a CaL DFB system. The results of the FL applications for CaL DFB are discussed in this section and to the best author’s knowledge it is the first work in open literature dealing with the use of FL methods in modeling gaseous pollutant emissions from calcium looping systems by the AI approach.


4.2.1. An object of investigations

The experimental work reported here was carried out in a CaL DFB facility at Department of Chemistry and Chemical Engineering, Niigata University in Japan. The experimental setup is given


70

in Figure 4.1. The laboratory-scale twin fluidized bed CO2 separation reactor consisted of a fast fluidized bed calcinator (regenerator) and a bubbling fluidized bed carbonator (absorber). Because of the vigorous solid mixing, fast fluidized bed is regarded as a suitable process for oxyfuel combustion and calcium looping at high temperatures, to suppress hot-spot formation. The height and inner diameter of the regenerator were 1.93 cm and 2.2 cm, respectively. Primary gas was fed through the grid whereas the secondary gas was supplied at 0.63 m above the bottom gas distributor [3, 4]. The regenerator operated in oxygen-enriched conditions using the mixture of air and oxygen. The ratio of primary gas to total gas was maintained at 0.5. The overall oxygen concentration in the combustion chamber of the regenerator was 30% by volume. To obtain such conditions oxygen O2 concentration in primary gas was kept at 51%. Air was used as a secondary gas. The superficial gas velocity above the secondary gas was fixed at 2.75 m/s at the temperature in the combustion chamber of the regenerator [3, 4].

Fig. 4.1. Experimental system

To achieve the necessary heat recovery, bubbling fluidized bed was employed for the absorber. The height of bubbling fluidized bed absorber was 0.6 m, whereas an overflow tube was installed at 0.3 m above the gas distributor to maintain the bed height constant [3, 4]. The inner diameter of the absorber was 9.3 cm. Electric heaters were applied to maintain temperatures 1223 ±10 K in the regenerator and 873 ±10 K in the absorber, respectively. Three kinds of coal, with different volatile matter content, were used during the study, i.e. high-volatile bituminous (HBV), medium-volatile bituminous (MVB) and semi-anthracite (SA). The fuel particle size was 297-1000 µm [3]. Fuel analyses are given in Table 4.1. The schematic diagram and the detailed description of experimental apparatus as well as the methodology of measurements can be also found elsewhere [3]. The data of conversion of fuel-N to NOx in regenerator reported in [3], obtained during experiments carried out on the CaL DFB facility at Department of Chemistry and Chemical Engineering, Niigata University in Japan are used for the validation of the developed FL model.


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Table 4.1. Analysis of fuels [3]

Hig

h-v

ola

tile

bit

um

ino

us

co

al

(HV

B)

Med

ium

-vo

lati

le

bit

um

ino

us

co

al

(MV

B)

Sem

i-an

tracit

e

(SA

)

Proximate Analysis/wt.%

Fixed Carbon FCa 41.2 56.2 70.7

Volatile Matter VMa 39.2 26.3 15.5

Ash Aa 14.3 15.0 10.9

Moisture Wa 5.3 2.5 2.9

Ultimate Analysis/daf.%/

Carbon C 78.1 85.9 89.7

Hydrogen H 6.3 4.9 4.2

Oxygen O 13.4 7.0 3.6

Nitrogen N 1.3 1.7 1.9

Sulfur S 1.0 0.5 0.7

4.2.2. Application of fuzzy logic method and calculating conditions

Fuzzy-logic (FL) constitutes one of the main representatives of the artificial intelligence techniques [92 – 95]. As it was underlined in the section 1.4. the FL approach enables qualitative judgment applied to parameters quantitative in nature and also allows dealing with imprecise, vague and uncertain information [96 - 99]. The detailed description of this method can be found in chapter 1.4. The commercial Qtfuzzylite fuzzy logic control application (http://www.fuzzylite.com) is used to predict the NOx emissions from the considered CaL DFB system. The design of a FL model consists in modeling of inputs and the output expressed as linguistic variables and composing a fuzzy rule base to describe the behavior of a process, an object or a system [84]. Different types of linguistic terms are available in qtfuzzylite software: triangular, trapezoidal, rectangular, discrete, gaussian, bell, cosine, pi shaped, sigmoidal, s shaped, z shaped, constant, linear, and etc. [84]. The following five input parameters are assumed: the volatile matter content VM in the fuel, the oxygen concentration C_O2 in flue gas from the regenerator, nitrogen N and fixed carbon FC content in the fuel and the total carbon mono- and dioxide concentration C_CO&CO2 in flue gas from absorber, whereas the conversion of fuel-N to NOx in regenerator constitutes the output parameter CR_N. These input and output data are given in Table 4.2. Such prepared set of input parameters allows the model to be more flexible and insensitive for the errors of measurements. In spite of the fact, that the total sum of VM (daf.) and FC (daf.) gives 100% the use of these two parameters makes the model more complete and compehensive. The input parameters (VM, C_O2, N, FC and C_CO&CO2) are linguistic variables with values divided into the three overlapping triangle linguistic terms: low (L), medium (M), high (H).

http://www.fuzzylite.com/


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Table 4.2. The operating parameters used in the study


The volatile mater content VM , wt. % 15.5 – 39.2

Oxygen concentration in flue gas from the regenerator O2, vol. % 2.424 – 3.833

Nitrogen content Ndaf, wt. % 1.3 – 1.9

Fixed carbon content FC, wt. % 41.4 – 70.7

The carbon mono -and dioxide concentration in flue gas from absorber, CO&CO2 vol.%

0.951 – 3.750

Conversion of fuel-N to NOx in Regenerator [-] 0.026 – 0.055

However five constant linguistic terms, i.e.: very low (VL), low (L), medium (M), high (H), and very high (VH), are applied to describe the conversion of fuel-N to NOx in regenerator CR_N i.e. the output parameter. Such approach was undertaken due to the non-linear nature of the NOx formation and destruction mechanisms in such systems. The input and output variables are depicted in Figs. 4.2 and 4.3, respectively.

Fig. 4.2. Membership functions for the fuzzy sets for the input parameters: VM, C_O2, N, FC and C_CO&CO2, respectively (the abscissa and the ordinate correspond to the values of the parameters from Table 4.2 and to the

values of the membership functions, respectively)


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To improve the accuracy of the model the output parameter CR_N are scaled by multiplying it by 103. The set of fuzzy rules are formulated according to the Table 4.3. As it can be seen the rule base is unsymmetrical what indicates that the considered issue of the conversion of fuel-N to NOx in regenerator is highly complex and non-lineal.

Fig. 4.3.Membership functions for the conversion of fuel-N to NOx CR_N in regenerator as the output parameter (the abscissa and the ordinate correspond to the values of the Conversion of fuel-N

to NOx in Regenerator from Table 4.2 and to the values of membership functions, respectively)


NOx VH* H M L VL

VM H M L

O2 H M L

N H M L

FC L M H

CO&CO2 L M H

* VH stands for: very high, H – high, M – medium, L – low, VL – very low

The qtfuzzylite also allows using different methods of fuzzy operator’s activation and accumulation as well as defuzzification techniques, allowing for different behavior for the application’s fuzzy engine [84]. The Takagi-Sugeno inference engine is utilized to determine the fuzzy output variable [92, 100]. To produce a crisp output value the weighted average method is applied during the defuzziffication stage, since it is one of the most computationally efficient methods [84, 92]. The defuzzified output o can be expressed by the expression (2.1) [92]: Such developed FL model constitutes an effective tool for making quick calculations, giving accurate results for the conversion rates of fuel-N to NOx in regenerator.

4.3. Results and Discussion

The FL model was successfully validated against experimental results. The comparison between desired and calculated by the FL model data is given in Figure 4.4.


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Fig. 4.4. Comparison of conversion of fuel-N to NOx in regenerator, desired and predicted by the model

The calculated results are located within the range of ± 10% compared to the desired data. The data shown in Figure 4.4 take into account the three considered kinds of coal and correspond to different input parameters, among others different oxygen concentrations in flue gas from the regenerator. The approach is undertaken since the comparison between desired and calculated data is regarded as the most difficult and demanding type of model's validation procedure [44, 45]. Such performed FL model can be used to run a non-iterative procedure to study the influence of the operating parameters on the nitrogen conversion rate of fuel-N to NOx in the regenerator of CaL DFB unit. The flow chart of the model application is given in Figure 4.5. The main stage of the calculating procedure is expressed by the green box where the FL model is called. In order to study the impact of a specific operating input variable on the CR_N other input parameters should be fixed as the dependence can be established only for the specified conditions. However one should keep in mind, that such inputs, as: VM, N, FC are strictly connected with fuel properties.

Fig. 4.5. The flow chart of the developed FL model

4.3.1. Effect of oxygen concentration in flue gas from the regenerator and total CO and CO2 concentrations in flue gas from absorber

The oxygen concentration in flue gas is one of the most important factors influencing the NOx formation. The effect of O2 content in flue gas from the regenerator on the conversion of fuel-N to NOx in the regenerator CR_N is presented in Figure 4.6.


75

Fig. 4.6. The effect of O2 concentration in flue gas from the regenerator; C_CO&CO2 = 3 %

The calculations are performed for coal of three considered kinds and the total CO and CO2 concentrations in flue gas from absorber 3%. As expected the CR_N increase with the increase in O2 concentration in flue gas from regenerator. Such results are in coincidence with other findings, described in [3, 21, 22]. Higher oxygen concentrations in the reactor make the combustion of char and volatile matter more enhanced, leading to the increase in NOx emissions. Conversion rate CR_N can be also expressed as a function of total concentration of CO and CO2 in the gas from absorber irrespective of the coal type and flue gas oxygen concentration [3]. This dependency can be also determined by the performed FL model. The effect of total concentration of CO and CO2 in flue gas from absorber on the conversion of fuel-N to NOx in regenerator is given in Figure 4.7.

Fig. 4.7. The effect of total concentration of CO and CO2 in flue gas from absorber on conversion of fuel-N to NOx in the regenerator; C_O2 = 3.5 %


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The existence of CO and CO2 in the absorber indicates that a certain amount of char was transported from the calciner to carbonator [5]. Simulations are performed for coal of three considered kinds and the O2 concentration in flue gas from regenerator 3.5 %. Such chosen O2 concentration in regenerator is a typical value in regenerator and is considered to be realistic to reduce the concentration of unreacted O2 in the regenerator flue gas suppressing the emissions of unburned CO as well as hydrocarbons under low excess O2 conditions. These non–condensable gases are not favorable during the compression of produced CO2 prior to geological storage [6]. Since char is a reductant of NO as well as it also acts as a catalyst in the NO reduction by reducing gases mechanism, such as CO, the increase in total concentration of CO and CO2 in flue gas from absorber leads to decrease in the conversion rate of fuel-N to NOx in regenerator. Similar trends has been reported in others studies dealing with NOx emissions from fluidized bed systems [3, 18, 21, 22]. The complex processes that occur in the CaL system cause that the modification of one operating parameter usually leads to the change of the others. For example, the change in O2 concentration in flue gas from regenerator has an impact on the total concentration of CO and CO2. Hence it seems interesting to study how the conversion of fuel-N to NOx in regenerator alters in such real conditions. Such simulation results are given in Figure 4.8.

Fig. 4.8. The conversion of fuel-N to NOx in the regenerator at real conditions

The obtained set of data corresponds to real operating conditions from Table 4.2 and are in accordance with the observations reported in [3]. As it can be seen form Figure 4.8 the conversion rate of fuel-N to NOx in the regenerator depends on fuel type.

4.3.2. The effect of volatile matter, nitrogen and fixed carbon content in fuel

The influence of fuel type on conversion of fuel-N to NOx in the regenerator is depicted in Figure 4.9. The CR_N decreases with the decrease volatile-matter content of fuel, irrespective of the nitrogen content in coal. The decreases in conversion of fuel-N to NOx in the regenerator accompany the increase in nitrogen content in the fuel and the increase in FC. This trend also confirms the opinion that lower rank coals can yield more NOx than higher rank ones [23]. Although the char from high-volatile coals is highly porous and reactive as well as the intrinsic oxidation rate is also higher than the one from low-volatile coals the former coals yields less char than the latter ones [3].


77

Fig. 4.9. The effect of fuel type on conversion of fuel-N to NOx in the regenerator (C_O2 = 3.5 %, C_CO&CO2= 3 %)

It is well known, that char plays pivotal role in NOx abatement mechanisms as a reductant and also a catalyst of NO reduction by reducing gases such as CO, via the reactions (4.3) and (4.4) [6]:

CONCcharNO 22/1 (4.3)

222/1 CONCONO (4.4)

Therefore the effect of volatile matter is more enhanced by the simultaneous decrease in fixed carbon content in three kinds of coal. Similar observations were also discussed by other researches [13, 14, 18, 20, 21, 23, 33].

4.4. Conclusions

The first results of simulations with the use of FL modeling approach for the prediction of conversion rate of fuel-N to NOx in regenerator from CaL DFB system are discussed in this chapter. It has been shown, that the approach gives quick and accurate results as an answer to the input data sets. The conversion rate of fuel-N to NOx in regenerator, evaluated using the developed model, is in a good agreement with the desired data. Maximum relative error of the proposed method is less than 10%. The obtained results revealed that the increase in oxygen concentration in flue gas from the regenerator and the total concentration of CO and CO2 in flue gas from absorber leads to an increase and a decrease in NOx emissions, respectively. The conversion rate of fuel-N to NOx in regenerator decreases with the decrease volatile-matter content of fuel, irrespective of the nitrogen content in coal. These results confirm the opinion that lower rank coals can yield more NOx than higher rank ones. The FL model constitutes an easy to use and useful tool. The model can be also applied as a submodel or a separate module in engineering calculations, capable to predict the conversion rate of fuel-N to NOx in regenerator of the CaL DFB system. Hence, it may also be employed for optimization purposes with the respect of lowering NOx emissions from the CaL DFB unit


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5. A COMPUTATIONAL STUDY OF CaSO4 DECOMPOSITION DURING COAL PYROLYSIS BY GENETIC ALGORYTHMS

AND NEUROCOMPUTING APPROACH

Nomenclature:

A ash content, wt. % Bi, L bias of neuron i on a layer L, - C carbon content, wt. % d particle diameter, m Ht holding time, s M moisture content, wt. % N nitrogen content, wt. % O oxygen content, wt. % S sulfate sulfur, wt. % So organic sulfur, wt. % Sp pyritic sulfur, wt. % St total sulfur, wt. % T Temperature, oC VM volatile matter content, wt. %

Greek symbols δ the relative error, %

Subscripts Exp measured (desired) Calc calculated (predicted) i, j numbers of neurons, i = 0 - 4, j = 0-1 L number of a layer, L = 0 - 3, ad air dried basis,

Acronyms ANN Artificial Neural Network, daf. dry ash-free-basis. GA Genetic algorithms, OC Oxygen carrier.

5.1. Introduction

The observed climate change resulted in an intensive development of new technologies for carbon dioxide capture technologies. They can be grouped into four main categories [1, 2]:

1) pre-combustion capture (a kind of fuel reforming), 2) oxy-fuel combustion (nitrogen in the air is removed before combustion), 3) post-combustion (CO2 separation process from flue gas), 4) chemical-looping combustion (CLC).

Chemical-looping combustion (CLC) belongs to the cheapest and most promising combustion technologies of fossil fuels for CO2 separation from flue gas [3-5]. It is based on combination of reduction and oxidation of solid oxygen carriers (OC), by fuel and air, respectively [1] (Figure 5.1). These reduction and oxidation processes occur in a separate gas streams. The main features of the CLC process it, that this technology is considered to be a nearly zero-emission technology, with a very low efficiency penalty and low CO2 capture cost, due to concentrated CO2 stream

5. A computational study of caso4 decomposition during coal pyrolysis by genetic algorythms and neurocomouting approach

85

ready for utilization and/or storage [2]. Moreover, since the process does not use air for combustion, the NOx production is limited and the flue gas mainly consists of CO2 and H2O [2].

Fig. 5.1. The idea of a chemical-looping combustion process

Different oxygen carriers can be used in the CLC systems [6]. There can be: Ni-, Fe-, Cu-, Mn- and Co- based oxygen carriers [5, 7]. Recently an interest on low cost materials, such as natural minerals or industrial waste products, as oxygen-carriers has been shown by some researches [5]. Several research groups in China and USA use CaSO4 as an oxygen carrier [5, 6]. The CaSO4 from natural anhydrite mines and industrial desulfurization processes is a low cost oxygen-carrier, having much higher oxygen transport capacity than all above listed metal-based oxides [5, 9]. Furthermore, CaSO4 is more environmentally benign than metal oxide based oxygen-carrier [9]. The main reactions which occur in a CLC system using the CaSO4 oxygen carrier are as follows [5, 6, 9]:

in the fuel reactor:

CaSCO2C2CaSO24 (5.1)

OH4CaSH4CaSO224

(5.2)

24CO4CaSCO4CaSO (5.3)

OH2COCaSCHCaSO2244

(5.4)

in the air reactor:

42CaSOO2CaS (5.5)

However, using CaSO4 as an oxygen-carrier, accompanies some difficulties.


86

Firstly, low reaction rate of CaSO4 with fuel cause, that the overall system should be operated under high temperature, leading to sintering of the OC and losing its reactivity [9]. Secondly, side-reactions with sulfur dioxide release and CaO formation (5.6-5.12):

2224SOOHCaOHCaSO (5.6)

224SOCOCaOCOCaSO (5.7)

222SOH3CaOOH3CaS (5.8)

22SOCO3CaOCO3CaS (5.9)

24SO4CaO4CaSCaSO3 (5.10)

22SOCaOO5.1CaS (5.11)

224O5.0SOCaOCaSO (5.12)

limiting practical application of the CaSO4 as an oxygen carrier [5, 9]. Moreover, these reactions may occur either in fuel-reactor or in air-reactor. The CaO production leads to the deterioration of oxygen transport capacity of oxygen-carrier and therefore the additional fresh CaSO4 particles should be supplied to replace the spent solids. Lastly, the existence of sulfur species brings corrosion problems of the system as well as difficulties for carbon dioxide capture and transportation [9]. Adanez et al. [5] underlined, that lowering operational temperature to 900-950 oC for fuel-reactor and 1050-1150 oC for air-reactor minimizes the SO2 release [5, 10]. Jia et al. [11] determined other than temperature important parameters influencing the CaSO4 decomposition rate. The authors studied the mechanism of CaSO4 decomposition during coal pyrolysis in a fixed bed reactor in the temperature range of 500-800 oC. Their comprehensive study revealed that the inherent minerals of coal, such as Fe2O3, Al2O3 and SiO2, could promote CaSO4 decomposition. They also observed that the CaSO4 decomposition rate decreased with the increase in coal particle size [11]. Since CaSO4 is a promising oxygen-carrier the study of its decomposition under reducing atmospheres is of practical significance. The purpose of this chapter is to give a model research of the CaSO4 decomposition mechanism during coal pyrolysis and to study the influence of operational conditions on this process. The performed model allows estimating the CaSO4 decomposition rate under wide range of operating conditions. The necessary training samples to develop the model are taken from the experiments previously carried out in Zhejiang University in Hangzhou, China and reported in [11], similar to the methods described in [13-16].


The experimental results, used for further validation of the developed model, were reported in [11]. Two lignites: Xiaolongtan (XLT), Hulunbeier (HLBR) and two bituminous coals: Shenmu (SM), Shanxi (SX) were used during the study. Additionally the demineralized coal (DEM for short) was also applied. Powdered CaSO4 (97 % purity) was mechanically mixed with coals with different proportions of CaSO4/coal: 10%, 50% and 100% by weight. The proximate and ultimate analysis of the fuels is shown in Table 5.1.


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Table 5.1. Main properties of samples [11]

Coal Ultimate analysis/ wt.% Elemental analysis/ wt.%, ad

M A VM C H O N

XLT SM

HLB SX

2.68 2.93 3.33 1.65

18.19 20.76 9.45 12.35

44.02 26.81 37.01 29.68

48.72 59.63 61.03 68.31

4.64 3.45 3.60 4.21

22.30 14.43 21.55 12.40

1.24 0.82 0.63 0.63

The sulfur forms contained in the coals used during the study are given in Table 5.2. The sulfate sulfur of coals was mainly made of FeSO4, Fe2(SO4)3 and CaSO4. Since the amount of FeSO4 and Fe2(SO4)3 of the coal was lower than 0.83%, the content of CaSO4 in the mixture can be considered as the content of sulfate sulfur S, especially when considering the fact, that much more quantity (10%, 50% and 100%) of CaSO4 was added during tests [11]. Table 5.2. Sulfur forms in coals, wt. %, ad [11]

Coal S So Sp St

XLT SM

HLB SX

0.83 0.02

0 0.01

1.36 0.27 0.22 0.30

0.32 0.39 0.19 0.17

2.51 0.68 0.41 0.48

The ash compositions of the raw coals are given in Table 5.3. The knowledge of the ash composition is very important as the inherent mineral matter of char or coal could promote CaSO4 decomposition [11]. Table 5.3. Ash compositions in raw coals, wt. %, [11]

Coal SiO2 Fe2O3 Al2O3 CaO MgO K2O Na2O SO3 Others

by.diff

XLT SM

HLB SX

33.47 33.76 53.28 49.02

8.09 5.45 11.83 6.88

12.04 47.27 11.20 17.87

19.11 6.46 11.87 14.59

3.42 0.95 6.83 4.99

0.76 0.32 0.30 1.13

0.16 0.15 0.92 0.46

20.01 4.07 5.42 4.07

2.94 1.57

- 0.96

Horizontal tube furnace was used during experiments. Schematic diagram of the facility are given in Figure 5.2.

Fig. 5.2. Schematic diagram of the experimental setup [11]

During each test the amount of 8 g coal with a specific proportion of CaSO4 was uniformly spread in a quartz container and placed in the center of the furnace [11]. Tests were conducted under nitrogen protection. Different series of experiments were carried out to study the influence of operating parameters on the CaSO4 decomposition (see Table 5.4) [11].


88

Coal samples were heated at 20 oC/min to a desired temperature from the range 500 oC – 800 oC and held for 30 min. The time keeping at a specific temperature is called the holding time [11]. Water and tar ware captured by a thimble filter, whilst the gas was collected in a gas bag. The char residue in the quartz container was taken out of the tube and weighted. Table 5.4. Main properties of samples [11]

Samples Temperature Particle diameter Holding time

XLT + 10% CaSO4

XLT + 50% CaSO4

XLT + 100% CaSO4

DEM-XLT+100 % CaSO4

XLT + 100% CaSO4

SM + 100% CaSO4

HLB + 100% CaSO4

SX + 100% CaSO4

XLT + 100% CaSO4

XLT + 100% CaSO4

XLT + 100% CaSO4

XLT + 100% CaSO4

XLT + 100% CaSO4

XLT + 100% CaSO4

XLT + 100% CaSO4

XLT + 100% CaSO4

XLT + 100% CaSO4

500 – 800

500 – 800

500 – 800

500 – 800

800

800

800

800

800

800

800

800

800

800

800

800

0 – 0.15

0 – 0.15

0 – 0.15

0 – 0.15

0.075 – 0.15

0.075 – 0.15

0.075 – 0.15

0.075 – 0.15

0 – 0.15

0 – 0.15

0 – 0.15

0 – 0.15

0 – 0.075

0.075 – 0.15

0.15 – 0.42

0.42 – 0.84

30

30

30

30

30

30

30

30

0

10

20

30

30

30

30

30

The CaSO4 decomposition rate was calculated by the formula [11]:

%100WWS

WS1.%)wt(rateiondecompositCaSO

4CaSOcoalcoal

charchar

4

(5.13)

where: Schar, Scoal are calcium sulfate content of the char and coal, Wchar, Wcoal, 4CaSOW are the weight

of the char, coal and CaSO4 added to the sample. Further details of the experiments can be also found elsewhere [11].


5.3.1. Genetic algorithm and artificial neural networks approach

The presented model was performed by the use of the Neuro Net application. This tool allows managing and optimizing artificial neural networks (ANN) by genetic algorithms (GA). The whole process of the building-up the model can be divided into two main stages according to the method I described in chapter 1.5.1. The use of genetic algorithms constitutes the first stage. The main features describing the ANN’s architecture are defined during this stage as the NN operation is highly dependent on its architecture. During this stage the GA optimization is performed to locate the global optimum, so the parameters defining genetic algorithm settings should be thoroughly selected. The definition of a local and global optimum can be found in chapter 1.5.1.


89

The maximum iterations and the population size were set to 1000 and 35, respectively. The number of individuals kept in memory as a reference (so called a maximum storage) was equal 350. Another important parameters are reseed fractions and screening offset. These two parameters were set to be 1. The maximum number of reseed parameter, was fixed to 2. Mutation factor was set to 5. The GC factor and the K nearest neighbor parameter were set to 0.25 and 2, respectively. Therefore the initial crowding factor was set to 0.9. On the other hand, the the final crowding factor, was fixed to 0.1. The second stage of model’s development procedure constitutes in the use of backpropagation learning algorithm to perform a highly trained ANN. Such approach turned out to be very effective, since the model can be successfully developed using limited number of experimental results. The total number of learning data included 24 samples of which 6 were independent, validation data set. The following four critical operating variables are considered as input parameters: Temperature (T), CaSO4/coal ratio (CaSO4), inherent sulfate sulfur content (S), inherent minerals of coal content (Fe2O3, Al2O3, SiO2), average particles diameter of coal (d) and holding time (Ht). It is interesting, that such defined set of input parameters allows model to take into consideration of different types of coal used in the experiment. The sensitivity analysis was performed, where the influence of a selected input variable on the model’s response was evaluated. The determined set of input parameters, including inherent sulfate sulfur and minerals content makes the model more complete and comprehensive. The input parameters, taken into account in the study, are given in Table 5.5.

Table 5.5. The input parameters used for training and testing the model



CaSO4, wt. % 10 – 100

S, wt. % 0 – 0.83

Fe2O3, wt. % 5.45 – 11.83

Al2O3, wt. % 11.2 – 47.27

SiO2, wt. % 33.47 – 53.28

d, mm 0 – 0.84

Ht, min 0 – 30

The CaSO4 decomposition constitutes the output parameter. Such defined input and output values determine the number of input and output neurons in the developed ANN. Hence the input and output layers consist of 8 and 1 perceptrons, respectively. Since the performance and accuracy of an ANN model highly depends on its structure, different ANN architectures have been tested during the study. Multilayers perceptrons with different hidden layers and different neurons in each of them were considered during the study. The optimal network turned out to be [8-3-3-1] with two hidden layers and is composed of three sigmoid neurons in each hidden layer. The structure of the ANN is given in Figure 5.3.


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Fig. 5.3. The Optimal [8-3-3-1] type of artificial neural network

The backpropagation learning algorithm, described in section 1.2, was used to develop the model [17]. The learning rate and the momentum were set to 0.07 and 0.5, respectively. Back propagation max iterations parameter was equal 2500. The sigmoid activation function, basing on the results of preliminary calculations and previous experience was employed. It is also one of the activation functions of neurons widely used in artificial neural networks [15-17]. A screenshot of the learning process is given in Figure 5.4.

Fig. 5.4. The behavior of mean squared error during the learning process

The details of the [8-3-3-1] type of ANN can be found in Table 5.6.


91


Weights, Wi, L, j*

W0, 0, 0 4,446

W0, 0, 1 -0.477

W0, 0, 2 -6,149

W1, 0, 0 1.017

W1, 0, 1 1.804

W1, 0, 2 0.522

W2, 0, 0 -1,029

W2, 0, 1 -1,841

W2, 0, 2 -2,780

W3, 0, 0 0,748

W3, 0, 1 -0,991

W3, 0, 2 -0,784

W4, 0, 0 0,651

W4, 0, 1 -0,984

W4, 0, 2 -0,561

W5, 0, 0 1,677

W5, 0, 1 -0,177

W5, 0, 2 -0,730

W6, 0, 0 -4,021

W6, 0, 1 0,286

W6, 0, 2 -1,529

W7, 0, 0 -2,602

W7, 0, 1 -0,399

W7, 0, 2 -2,709

W0, 1, 0 -0,995

W0, 1, 1 0,088

W0, 1, 2 -2,258

W1, 1, 0 1,119

W1, 1, 1 -0,680

W1, 1, 2 2,123

W2, 1, 0 1,307

W2, 1, 1 -0,492

W2, 1, 2 2,405

W0, 2, 0 -0.886

W1, 2, 0 1.554

W2, 2, 0 -2.662


92

Table 5.6(continued). The [8-3-3-1] type of ANN detailed properties

Neuron Bias, Bi, L*

B0, 1 - 1.461

B1, 1 3.363

B2, 1 4.024

B0, 2 0.263

B1, 2 0.756

B2, 2 -0.336

B0, 3 1.826

* a weight between a neuron i on a layer L and a neuron j on a layer L+1; neurons on a layer and layers in the ANN in Figure 5.3 are numbered from top to bottom (i = 0 – 7) and from the left to the right (L = 0 – 2), respectively

The Table 5.6 contains crucial data set, indispensable for the proper designing of the optimal [8-3-3-1] type of NN. These values describe the ANN structure developed for the considered task, i.e. the reproducing the CaSO4 decomposition mechanism.

5.3.2. Validation of the model

The developed model was successfully validated against measured data, both training and the new ones, unseen before by the developed NN (Figure 5.5).

Fig. 5.5. Comparison of the CaSO4 decomposition measured (from experiment) and calculated (predicted) by the model. Red symbols refer to training whereas

the blue ones apply to the new, previously unseen, independent data

Such approach was undertaken as the comparison between experimental and calculated data is regarded as the most difficult types of model's validation procedures [16]. The CaSO4 decomposition values generated by the model are in good accuracy with the measured ones both training and the new, unseen before by the model data. Maximum relative error is lower than ± 15%. Such obtained results prove a good generalization ability of the developed network.


93

Table 5.7 summarizes the values of the CaSO4 decomposition, predicted by the optimal [8- 3-3-1] type of ANN and measured during experiments.

Table 5.7. The selected CaSO4 decompositions, measured and predicted by the [8-3-3-1] type of ANN

CaSO2 decomposition Exp

CaSO2 decomposition Calc

δ

%

Dat

a

no

t use

d f

or

trai

nin

g

net

wo

rk

11.30 12.16 7.61

94.00 83.40 11.28

3.00 3.14 4.67

71.30 80.57 13.00

84.70 93.46 10.34

Dat

a

use

d f

or

trai

nin

g n

etw

ork

20.70 20.50 0.97

3.30 3.65 10.61

37.60 37.52 0.213

93.30 92.88 0.45

34.00 34.13 0.38

The relative errors for some of the predicted by the model results are lower than 1 %. Because of the fact, that the knowledge of the process, acquired by the network during the training stage, is stored in ANN structure, i.e. weights and the architecture of the ANN, all the parameters defining the network, i.e. activation function of neurons, the number of perceptrons in hidden layers and the type of ANN should be considered as the most suitable for the analyzed task. Such developed ANN model has the ability to successfully reproduce the process of the CaSO4 decomposition. It also makes a tool which allows obtaining quick and accurate results as an answer to new independent input data (stimuli). The new model can run via a non-iterative procedure to study the influence of the operating parameters on the CaSO4 decomposition. The flow chart of the ANN model application, developed in this study, is given in Figure 5.6.

Fig. 5.6. Flow chart of the ANN model for the prediction of CaSO4 decomposition

Taking into account the above shown procedure for the prediction of CaSO4

decomposition one only needs to enter the required by the model eight input parameters. Thus, the whole calculations procedure is quite simple as it belongs to the non-iterative methods.


94

Since the ANNs have the ability to reproduce a process from training samples the knowledge about the phenomenon allows the model to generate the correct CaSO4 decomposition for the given set of input parameters. Thus, such a tool can be employed for studying the influence of a specific input variable on the CaSO4 decomposition rates.

5.3.3. Influence of operating parameters on CaSO4 decomposition rate

Since the bounds of the input parameters can be extended by 20% each way in the Neuro Net tool, to allow for predictions outside of the training zone, the study of the influence of the input parameters can be performed for the ranges from Table 5.8.

Table 5.8. The input parameters used for the study



CaSO4, wt. % 8 – 100

S, wt. % 0 – 0.996

Fe2O3, wt. % 4.36 – 14.2

Al2O3, wt. % 8.96 – 56.72

SiO2, wt. % 26.78 – 63.94

d, mm 0 –1.0

Ht, min 0 – 36

5.3.3.1. Effect of temperature and CaSO4/coal ratio

The influence of the temperature on the CaSO4 decomposition rate was studied for XLT lignite, particles size: 0.075 - 0.15 mm, Ht = 30 min. The results, shown in Figure 5.7 are similar to the data obtained experimentally as the process of CaSO4 decomposition strongly depends on the temperature [11].


95

Fig. 5.7. Effect of temperature and CaSO4/coal ratio on CaSO4 decomposition

The small amount of sulfate sulfur decomposed below 600 oC might be attributed to the decomposition of FeSO4 and Fe2(SO4)3, since the amount of FeSO4 and Fe2(SO4)3 of the coal was lower than 0.83% and they could be decomposed at much lower temperatures than CaSO4 [18]. As the temperature increases the CaSO4 decomposition rate shows a fast increase for all CaSO4/coal ratios (Figure 5.7). However the increase in CaSO4/coal ratio leads to decrease in CaSO4 decomposition. The results obtained by Jia et al. [11] revealed that CaSO4 decomposition could be greatly promoted by the presence of coal during pyrolysis. The authors underlined, that the presence of reducing agents, such as pyrolyzed gas, tar and the carbon of the coal can play a role in the decomposition process [11]. The authors proposed two mechanisms, i.e. solid-solid reaction mechanism (5.14) and gas-solid reactions mechanism (5.15) and (5.16):

24CO2CaSCaSOC2 (5.14)

CO2COC2 (5.15)

24CO4CaSCaSOCO4 (5.16)

On the other hansd the observed behavior can be also explained considering the ratio of reducing agents and the sum of FeSO4 and Fe2(SO4)3 to total sulfate sulfur with the increase in the CaSO4 content. Since this ratio decreases with the total CaSO4 content, the decomposition rate decreases with the increase in the proportion of CaSO4/coal. Taking into account the above, the authors [11] concluded, that the pyrolyzed gas and tar play only a little role in the CaSO4 decomposition and the solid-solid mechanism is the predominant cause for the CaSO4 decomposition.

5.3.3.2. Effect of inherent minerals content

The influence of Fe2O3, Al2O3 and SiO2 content in XLT coal is shown in Figs. 5.8 – 5.10. The results were obtained for T= 650 oC , CaSO4/coal ratio = 50 %, particles size: 0.075 - 0.15 mm, Ht = 20 min.


96

Fig. 5.8. Effect of Fe2O3 content on CaSO4 decomposition

Fig. 5.9. Effect of Al2O3 content on CaSO4 decomposition

Fig. 5.10. Effect of SiO2 content on CaSO4 decomposition


97

As we can see from Figure 5.9 the dependence of the CaSO4 decomposition rate is a nearly linear function of the Al2O3 inherent content in the considered range of input parameters. As expected, the increase in the content of inherent minerals, i.e. Fe2O3, Al2O3 and SiO2 will promote the CaSO4 decomposition rate. According to observations reported in [19] the Fe2O3 as a porous, catalytic support, could promote the reaction between carbon and calcium sulfate providing higher surface area as a porous support [11]. Jia et al. [11] supposed, that the inherent minerals could enlarge surface area, thereby facilitating CaSO4 decomposition. Moreover, the experiments with the demineralized coal revealed that inherent minerals could greatly enhance CaSO4 decomposition in elevated temperatures [11]. They also observed that the solid-solid reaction between carbon and CaSO4 hardly took place in the absence of inherent minerals in temperatures below 800 oC whilst for a not demineralized coal this reaction proceeded at temperatures 600 -700 oC. The authors concluded that minerals could lower the initial reaction temperature and raise CaSO4 decomposition rate [11].

5.3.3.3. Effect of coal type

The influence of coal type on CaSO4 decomposition rate is given in Figure 5.11. The results are obtained for T = 800 oC, CaSO4/coal ratio = 100 particles size: 0.075 - 0.15 mm, Ht = 30 min.

Fig. 5.11. Effect of coal type on CaSO4 decomposition

The reported difference in the CaSO4 decomposition rate can be considered as a result of complex effects of inherent minerals, such as Fe2O3, Al2O3 and SiO2. Mihara et al [20] confirmed, that the addition of Fe2O3, SiO2 and Al2O3 cause the reduction of CaSO4 decomposition temperature. This was due to the formation of composite oxides (calcium ferrite, calcium silicate, or calcium aluminate) via the reaction of CaSO4 with these minerals [20]. Moreover, the comparison of the data from Table 5.3 and the obtained results given in Figure 5.11, leads to the conclusion, that CaSO4 decomposition rate is in good accordance with the Fe2O3 content in ash. Since the HLBE and SM contain the highest and the lowest Fe2O3 in the ash, the CaSO4 decomposition rates for these coals are also the highest and the lowest, respectively. Finally the reported in Figure 5.11 results correspond to the rank of considered coals. It is well known that low rank coals have higher reactivity than high rank coals since the C-C bounds could be easily decomposed for low rank coals [11, 21, 22]. As the result the CaSO4 ecomposition rate, due to the reaction carbon of coal, is favored for lignite coals XLT and HLBE [11].


98

5.3.3.4. Effect of coal particle size

The The influence of particle size on the CaSO4 decomposition rate is given in Figure 5.12. The results are obtained for Xiaolongtan (XLT) lignite, T= 800 oC, CaSO4/coal ratio = 100 % and Ht = 30 min.

Fig. 5.12. Effect of particle size on CaSO4 decomposition

Figure 5.12 clearly depicts, that the increase in the particle diameter leads the decrease in the CaSO4 decomposition rate. Such behavior goes in accordance with the previous conclusions that the solid-solid reaction mechanism is the predominant cause for the CaSO4 decomposition process. Since the contact area strongly influences the rate of such mechanism and the increase in particle size causes the decrease in the contact area between CaSO4 and coal, the CaSO4 decomposition rate lowers for the coarse particles of coal. Hence particle size is considered as one of the important variables in coal pyrolysis processes [11].

5.3.3.5. Effect of holding time

The influence of holding time on the CaSO4 decomposition rate is shown in Figure 5.13. The calculations are performed for XLT lignite. As expected the increase in holding time leads to the increase in the CaSO4 decomposition rate.

Fig. 5.13. Effect of particles size on CaSO4 decomposition


99

The higher CaSO4 decomposition calculated value for 20 min can be attributed to the method’s error. However, the observed error (10.3%) is located within the rage of maximum relative error of the method (± 15%). Similar to the observations underlined in [11], most of the CaSO4 could be decomposed in 20 min.

5.4. Conclusions

The presented study allowed developing a model of CaSO4 decomposition process during coal pyrolysis. The developed model allows estimating the CaSO4 decomposition rate which significantly increases with the temperature. As the temperature increases the CaSO4 decomposition rate shows a fast increase for all CaSO4/coal ratios. However the increase in CaSO4/coal ratio leads to decrease in CaSO4 decomposition. The inherent minerals content enhances the CaSO4 decomposition process. The CaSO4 decomposition rate, due to the reaction carbon of coal, is favored for lignite coals XLT and HLBE as low rank coals have higher reactivity than the high rank ones. The decrease in coal particle size and the increase in holding time lead to the increase in CaSO4 decomposition rate. The Neuro Net software is a suitable tool to develop the non-iterative AI models.

5.5. References

[1] Shimizu T. Calcium Looping process for post-combustion CO2 capture – concept, state- of-art, and future perspective, [in:] Majchrzak-Kucęba I., Wawrzyńczak D. (eds.) Advanced CO2 Capture Technologies for Clean Coal Energy Generation, Czestochowa University of Technology, ISBN 978-83-7193-655-5, Czestochowa, Poland, 2016.

[2] Skulimowska, A., Di Felice, L., Kamińska-Pietrzak, N., Celińska, A., Pławecka, M., Hercog, J., Kraz M., Aranda, A. (2017). Chemical looping with oxygen uncoupling (CLOU) and chemical looping combustion (CLC) using copper-enriched oxygen carriers supported on fly ash. Fuel Processing Technology, 168, 123-130.

[3] Krzywanski, J., Żyłka, A., Czakiert, T., Kulicki, K., Jankowska, S., Nowak, W. (2017). A 1.5D model of a complex geometry laboratory scale fuidized bed CLC equipment. Powder Technology, 316, 592-598.

[4] Ksepko E., Sciazko M., Babinski P. (2014). Studies on the redox reaction kinetics of Fe2O3-CuO/Al2O3 and Fe2O3/TiO2 oxygen carriers, Appl. Energy 115, 374–383.

[5] Adanez J., Abad A., Garcia-Labiano F., Gayan P., Luis F. (2012). Progress in chemical-looping combustion and reforming technologies, Prog. Energy Combust. Sci. 38, 215–282.

[6] Rakowski J., Bocian P., Celińska A., Świątkowski B., Golec T. (2016). Zastosowanie pętli chemicznych w energetyce (Application of chemical loops in power industry), Energetyka, 4, 208-213.

[7] Nandy, A., Loha, C., Gu, S., Sarkar, P., Karmakar, M. K., Chatterjee, P. K. (2016). Present status and overview of Chemical Looping Combustion technology. Renewable and Sustainable Energy Reviews, 59, 597-619.

[8] Jia, X., Wang, Q., Han, L., Cheng, L., Fang, M., Luo, Z., Cen, K. (2017). Sulfur transformation during the pyrolysis of coal with the addition of CaSO 4 in a fixed-bed reactor. Journal of Analytical and Applied Pyrolysis, 124, 319-326.

[9] Zhang, S., Xiao, R., Liu, J., Bhattacharya, S. (2013). Performance of Fe2O3/CaSO4 composite oxygen carrier on inhibition of sulfur release in calcium-based chemical looping combustion. International Journal of Greenhouse Gas Control, 17, 1-12.


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[10] Shen, L., Zheng, M., Xiao, J., Xiao, R. (2008). A mechanistic investigation of a calcium-based oxygen carrier for chemical looping combustion. Combustion and Flame, 154(3), 489-506.

[11] Jia, X., Wang, Q., Cen, K., Chen, L. (2016). An experimental study of CaSO4 decomposition during coal pyrolysis. Fuel, 163, 157-165.

[13] Kesgin U. Genetic algorithm and artificial neural network for engine optimization of efficiency and NOx emission. Fuel 2004;83:885–95.

[14] Jensen RR, Karki S, Salehfar H. Artificial neural network-based estimation of mercury speciation in combustion flue gases. Fuel Process Technol 2004;85: 451–62.

[15] Liukkonen M, Heikkinen M, Hiltunen T, Halikka E, Kuivalainen R, Hiltunen Y. Artificial neural networks for analysis of process states in fluidized bed combustion. Energy 2011;36:339–47.

[16] Krzywanski J., Czakiert T., Blaszczuk A., Rajczyk R., Muskala W., Nowak W., A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach, Part 1. The mathematical model of SO2 emissions in air-firing, oxygen-enriched and oxycombustion CFB conditions, Fuel Processing Technology (2015) 137, 66 - 74.

[17] Nalecz M. Biocybernetyka i inżynieria biomedyczna (Biocybernetics and biomedical engineering) 2000. Tom 6 Sieci Neuronowe. Warsaw: Exit; 1999 [in Polish].

[18] Yani S, Zhang DK. An experimental study of sulphate transformation during pyrolysis of an Australian lignite. Fuel Process Technol (2010) 91, 313–21.

[19] van der Merwe EM, Strydom CA, Potgieter JH. Thermogravimetric analysis of the reaction between carbon and CaSO4 center dot 2H(2)O, gypsum and phosphogypsum in an inert atmosphere. Thermochim Acta 1999;341:431–7.

[20] Mihara N, Kuchar D, Kojima Y, Matsuda H. Reductive decomposition of waste gypsum with SiO2, Al2O3 and Fe2O3 additives. J Mater Cycles Waste Manage (2007) 9, 21–6.

[21] Ye DP, Agnew JB, Zhang DK. Gasification of a South Australian low-rank coal with carbon dioxide and steam: kinetics and reactivity studies. Fuel (1998) 77,1209–19.

[22] Beamish BB, Shaw KJ, Rodgers KA, Newman J. Thermogravimetric determination of the carbon dioxide reactivity of char from some New Zealand coals and its association with the inorganic geochemistry of the parent coal. Fuel Process Technol (1998) 53, 243–53.

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CONCLUSIONS

The AI techniques presented in this book constitute practical applications and case studies where energy objects and processes are considered. The described methods are easy in use, in spite of the fact that a detailed, strict knowledge of the modeled system is not indispensable. It referes to artificial neural networks and genetic algorithms as well as fuzzy logic techniques. However the basic or general knowledge of the modeled system or its behaviour is advisable. It helps to choose the proper initial model parameters, including architecture, inputs selection and the general model design. The most suitable area of the AI application techniques are complex systems.

Providing an effecive modeling methods, the AI can be very useful in development comprehensive models, capable to predict the behaviour of such systems.

102

MODELING OF ENERGY SYSTEMS WITH FIXED AND MOVING POROUS MEDIA BY ARFTIFICIAL INTELLIGENCE METHODS

ABSTRACT

The book deals with the artificial intelligence methods in modeling of energy systems. Selected artificial intelligence techniques, i.e. artificial neural networks, fuzzy logic and genetic algorithms are applied in the study. A wide range of applications are considered in the monograph. Besides chapter one with an introduction to artificial intelligence methods heat transfer in a large-scale circulating fluidized bed boiler, hydrogen production via CaO sorption enhanced gasification of sawdust in a fluidized bed unit, NOx emissions from calcium looping in fluidized bed systems and the CaSO4 decomposition during coal pyrolysis are discussed in the book. The developed models constitute a series of easily-applicable and powerful tools allowing to describe the complex energy systems. Original models developed in the book have a high value from practical point of view and are consistent with the latest scientific achievements in the discussed subject.

MODELOWANIE UKŁADÓW ENERGETYCZNYCH ZE STACJONARNYMI I RUCHOMYMI WARSTWAMI POROWATYMI

PRZY UŻYCIU METOD SZTUCZNEJ INTELIGENCJI

STRESZCZENIE

W pracy przedstawiono wybrane metody sztucznej inteligencji w modelowaniu układów energetycznych. Wykorzystano: sztuczne sieci neuronowe, logikę rozmytą oraz algorytmy genetyczne. W opracowaniu uwzględniono szeroki zakres zagadnień. Po rozdziale pierwszym, stanowiącym wstęp do omawianych technik sztucznej inteligencji, kolejne rozdziały dotyczą: wymiany ciepła w komorze paleniskowej kotła z cyrkulacyjną warstwą fluidalną dużej skali, produkcji wodoru z biomasy w warstwie fluidalnej, emisję NOx w pętli wapniowej, realizowanej w warstwie fluidalnej, oraz rozkładu CaSO4 w procesie pirolizy węgla. Opracowane i przedstawione w książce modele stanowią zestaw prostych w użyciu a jednocześnie efektywnych narzędzi, pozwalających opisać zachowanie złożonych układów energetycznych. Modele te posiadają duże znaczenie praktyczne a przedstawiony w pracy materiał cechuje aktualność i zgodność z najnowszymi trendami nauki.

103

LIST OF FIGURES

Fig. 1.1. Modeling approach as an alternative method of data handling

Fig. 1.2. Some aspects for considering when modeling

Fig. 1.3. A flow chart of a non-iterative procedure

Fig. 1.4. Schematic diagram for a neuron (a) and an artificial neural network (b)

Fig. 1.5. Sigmoid activation function

Fig. 1.6. Hyperbolic tangent activation function

Fig. 1.7. The change of a mean squared error during a learning process

Fig. 1.8. The main steps of a basic genetic algorithm

Fig. 1.9. The scheme of a single-point crossover

Fig. 1.10. The scheme of a single mutation

Fig. 1.11. The main features of the FL methods

Fig. 1.12. Main parts of a FL model

Fig. 1.13. The structure of a general fuzzy system

Fig. 1.14. Fuzzification and membership functions for input parameters

Fig. 1.15. Linguistic variables

Fig. 1.16. The IF-THEN fuzzy rule base

Fig. 1.17. The defuzzification stage

Fig. 1.18. A help page of the Neuro Net application

Fig. 1.19. The How to use Neuro Net tab

Fig. 1.20. An example of a network adaptation tab of the Neuro Net application

Fig. 1.21. The two steps in the development of the model

Fig. 1.22. The back propagation learning algorithm in the development of the model

Fig. 1.23. A sample GA SETTINGS tab of the Neuro Net application

Fig. 1.24. A sample NN SETTINGS tab of the Neuro Net application

Fig. 1.25. A sample NEURAL NETWORK INPUT tab of the Neuro Net application

Fig. 1.26. A sample EXPLORE NETWORK OUTPUT tab of the Neuro Net application

Fig. 1.27. A sample EXPLORE NETWORK DETAILS tab of the Neuro Net application

Fig. 1.28. A sample PROJECT MANAGEMENT tab of the Neuro Net application

Fig. 1.29. The user interface of Qtfuzzylite

Fig. 1.30. Sample input and output variables

Fig. 1.31. A sample rule base

Fig. 1.32. The use of Rule Block Editor

Fig. 1.33. Testing of a FL model

Fig. 1.34. The workbench with minimized widows of linguistic variables

Fig. 1.35. The import and export menus of Qtfuzyylite

Fig. 1.36. The export of the code to Fuzzy Inference System

Fig. 1.37. Different accumulation methods

Fig. 1.38. Different defuzzification methods

Fig. 1.39.. Maps of the dependences of input and output variables

Fig. 2.1. General arrangement of heating surfaces in the 670 t/h CFB boiler

8. List of figures

104

Fig. 2.2. Wing walls (SUPERHEATER II) in combustion chamber of a CFB boiler

Fig. 2.3. Membership sigmoid functions for input parameters: z, t, e, U, MCR, respectively (the abscissa and the ordinate correspond to the values of the parameters from Table 2.1 and to the values of the membership functions, respectively)

Fig. 2.4. Membership functions for the heat transfer coefficient as the output parameter (the abscissa and the ordinate correspond to the values of the overall heat transfer coefficient from Table 2.1 and to the values of membership functions, respectively)

Fig. 2.5. Comparison of overall heat transfer coefficient values desired and predicted by the model; input parameters (according to Table 2.1): z = 36 – 45m, T = 800-900 oC, e = 0.93 – 0.99, U = 5 – 7 m s-1, MCR = 40 – 100 %

Fig. 2.6. Application of the FL model for the prediction of heat transfer coefficient for wing walls

Fig. 2.7. The effect of bed temperature on the heat transfer coefficient for wing walls; U = 6 m s-1, e = 0.95

Fig. 2.8. The effect of bed voidage on the heat transfer coefficient for wing walls; U = 6 m s-1, T = 850 oC

Fig. 2.9. The effect of bed gas velocity on the heat transfer coefficient for wing walls; e = 0.95, T = 850 oC

Fig. 2.10. The effect of the load of the boiler; e = 0.95, T = 850 oC

Fig. 2.11. The local heat transfer coefficient for wing walls along the combustion chamber

Fig. 2.12. Comparison on local heat transfer coefficient for membrane-walls and wing walls along the combustion chamber

Fig. 3.1. The schematic diagram of the lab-scale facility [2]

Fig. 3.2. The structure of the [4-3-3-1] type of neural network

Fig. 3.3. Comparison of the H2 concentration in syngas desired (measured) and predicted (calculated) by the model; (red points correspond to dependent whilst the blue – to independent, new data)

Fig. 3.4. Application of the model for the prediction of H2 concentration in syngas

Fig. 3.5. Effect of CaO/C on H2 production (H2/C =1.2, T = 740 oC, ER = 0)

Fig. 3.6. Effect of H2O/C on H2 production (CaO/C = 1, T = 740 oC, ER =0)

Fig. 3.7. Effect of temperatures on H2 production (CaO/C = 1, H2O/C = 1.7, ER =0)

Fig. 3.8. Effect of the equivalence ratio of air on H2 production (CaO/C = 1, H2O/C = 2, T = 850 oC)

Fig. 3.9. Effect of increase in input parameters on H2 production

Fig. 4.1. Experimental system

Fig. 4.2. Membership functions for the fuzzy sets for the input parameters: VM, C_O2, N, FC and C_CO&CO2, respectively (the abscissa and the ordinate correspond to the values of the parameters from Table 4.2 and to the values of the membership functions, respectively)

Fig. 4.3. Membership functions for the conversion of fuel-N to NOx CR_N in regenerator as the output parameter (the abscissa and the ordinate correspond to the values of the Conversion of fuel-N to NOx in Regenerator from Table 4.2 and to the values of membership functions, respectively)

Fig. 4.4. Comparison of conversion of fuel-N to NOx in regenerator, desired and predicted by the model

Fig. 4.5. The flow chart of the developed FL model

Fig. 4.6. The effect of O2 concentration in flue gas from the regenerator; C_CO&CO2 = 3 %

Fig. 4.7. The effect of total concentration of CO and CO2 in flue gas from absorber on conversion of fuel-N to NOx in the regenerator; C_O2 = 3.5 %

Fig. 4.8. The conversion of fuel-N to NOx in the regenerator at real conditions

Fig. 4.9. The effect of fuel type on conversion of fuel-N to NOx in the regenerator (C_O2 = 3.5 %, C_CO&CO2= 3 %)

Fig. 5.1. The idea of a chemical-looping combustion process

Fig. 5.2. Schematic diagram of the experimental setup [11]

8. List of figures

105

Fig. 5.3. The Optimal [8-3-3-1] type of artificial neural network

Fig. 5.4. The behavior of mean squared error during the learning process

Fig. 5.5. Comparison of the CaSO4 decomposition measured (from experiment) and calculated (predicted) by the model. Red symbols refer to training whereas the blue ones apply to the new, previously unseen, independent data

Fig. 5.6. Flow chart of the ANN model for the prediction of CaSO4 decomposition

Fig. 5.7. Effect of temperature and CaSO4 /coal ratio on CaSO4 decomposition

Fig. 5.8. Effect of Fe2O3 content on CaSO4 decomposition

Fig. 5.9. Effect of Al2O3 content on CaSO4 decomposition

Fig. 5.10. Effect of SiO2 content on CaSO4 decomposition

Fig. 5.11. Effect of coal type on CaSO4 decomposition

Fig. 5.12. Effect of particle size on CaSO4 decomposition

Fig. 5.13. Effect of particles size on CaSO4 decomposition.

106

LIST OF TABLES

Table 1.1. Main features of different methods of data handling

Table 2.1. Parameters of entry conditions


Table 2.3 The heat transfer coefficient desired and predicted by the model

Table 3.1. Properties of sawdust [2]

Table 3.2. The input parameters used in the study


Table 3.4. The CH2, predicted by the [4-3-3-1] type ANN and obtained from measurements

Table 3.5. The extended ranges of input parameters

Table 4.1. Analysis of fuels [3]

Table 4.2. The operating parameters used in the study



Table 5.2. Sulfur forms in coals, wt. %, ad [11]

Table 5.3. Ash compositions in raw coals, wt. %, [11]


Table 5.5. The input parameters used for training and testing the model


Table 5.7. The selected CaSO2 decompositions, measured and predicted by the [8-3-3-1] type of ANN

Table 5.8. The input parameters used for the study

107

LIST OF THE MOST IMPORTANT SYMBOLS AND ACRONYMS

Nomenclature:

A ash content, wt. %

Bi, L bias neuron i on a layer L, -

C carbon content, wt. %

CaO/C CaO to carbon mole ratio, -

CH2 hydrogen concentration in syngas, %

C_CO&CO2 total concentration of CO and CO2 in flue gas from absorber

C_O2 oxygen concentration in flue gas from regenerator, %

CR_N conversion of fuel-N to NOx in regenerator, [-]

d particle diameter, m

δ the relative error, %

e voidage, -

f activation function, -

F fuzzy set

FC fixed carbon, wt. %


H2O/C H2O to carbon mole ratio, -

Ht holding time, s

i input,

m load of the boiler

M moisture content, wt. %

μ degree of membership, -

N nitrogen content, wt. %

o output,

O oxygen content, wt. %

ρ density, kg m-3

S sulfate sulfur, wt. %

T temperature, oC

U velocity, m s−1

VM volatile matter content, wt. %

Wi,L,k weight between a neuron i on a layer L and a neuron k on a layer L+1, -

z the distance from the gas distributor, m

Acronyms

AI Artificial Intelligence

ANN Artificial Neural Networks

BFB Bubbling Fluidized Bed

BP Backpropagation

CaL Calcium Looping

CC Cooling Capacity

CFB Circulating Fluidized Bed

CFBC Circulating Fluidized Bed Combustor

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DFB Dual Fluidized Bed

FB Fluidized Bed

FL Fuzzy Logic

GA Genetic Algorithm

GHG Greenhouse gas.

GUI Graphical user interface

MCR Maximum Continuous Rating

MSE Mean Squared Error

PG Primary Gas

SG Secondary Gas

OC Oxygen carrier.

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BADANIA I ANALIZA NUMERYCZNA RAMY GOKARTA fileBADANIA I ANALIZA NUMERYCZNA RAMY GOKARTA