MULTI-OBJECT OPTIMIZA (2ND ED): Techniques and Applications in Chemical Engineering
588
MULTI-OBJECT OPTIMIZA (2ND ED): Techniques and Applications in Chemical Engineering
588eBook
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Product Details
| ISBN-13: | 9789813148246 |
|---|---|
| Publisher: | World Scientific Publishing Company, Incorporated |
| Publication date: | 12/22/2016 |
| Series: | ADVANCES IN PROCESS SYSTEMS ENGINEERING , #5 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 588 |
| File size: | 34 MB |
| Note: | This product may take a few minutes to download. |
Table of Contents
Preface v
Chapter 1 Introduction Gade Pandu Rangaiah 1
1.1 Process Optimization 1
1.2 Multi-Objective Optimization: Basics 4
1.3 Multi-Objective Optimization: Methods 8
1.4 Alkylation Process Optimization for Two Objectives 13
1.4.1 Alkylation Process and its Model 13
1.4.2 Multi-Objective Optimization Results and Discussion 16
1.5 Scope and Organization of the Book 18
References 23
Exercises 25
Chapter 2 Multi-Objective Optimization Applications in Chemical Engineering Masuduzzaman Gade Pandu Rangaiah 27
2.1 Introduction 28
2.2 Process Design and Operation 29
2.3 Biotechnology and Food Industry 30
2.4 Petroleum Refining and Petrochemicals 40
2.5 Pharmaceuticals and Other Products/Processes 41
2.6 Polymerization 48
2.7 Conclusions 48
References 52
Chapter 3 Multi-Objective Evolutionary Algorithms: A Review of the State-of-the-Art and some of their Applications in Chemical Engineering Antonio López Jaimes Carlos A. Coello Coello 61
3.1 Introduction 61
3.2 Basic Concepts 62
3.2.1 Pareto Optimality 63
3.3 The Early Days 63
3.4 Modern MOEAs 65
3.5 MOEAs in Chemical Engineering 68
3.6 MOEAs Originated in Chemical Engineering 68
3.6.1 Neighborhood and Archived Genetic Algorithm 69
3.6.2 Criterion Selection MOEAs 70
3.6.3 The Jumping Gene Operator 72
3.6.4 Multi-Objective Differential Evolution 73
3.7 Some Applications Using Well-Known MOEAs 75
3.7.1 TYPE I: Optimization of an Industrial Nylon 6 Semi-Batch Reactor 76
3.7.2 TYPE I: Optimization of an Industrial Ethylene Reactor 76
3.7.3 TYPE II: Optimization of an Industrial Styrene Reactor 77
3.7.4 TYPE II: Optimization of an IndustrialHydrocracking Unit 76
3.7.5 TYPE III: Optimization of Semi-Batch Reactive Crystallization Process 78
3.7.6 TYPE III: Optimization of Simulated Moving Bed Process 79
3.7.7 TYPE IV: Biological and Bioinformatics Problems 80
3.7.8 TYPE V: Optimization of a Waste Incineration Plant 81
3.7.9 TYPE V: Chemical Process Systems Modelling 81
3.8 Critical Remarks 83
3.9 Additional Resources 84
3.10 Future Research 85
3.11 Conclusions 85
Acknowledgements 85
References 86
Chapter 4 Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations Manojkumar Ramteke Santosh K. Gupta 91
4.1 Introduction 92
4.2 Genetic Algorithm (GA) 93
4.2.1 Simple GA (SGA) for Single-Objective Problems 93
4.2.2 Multi-Objective Elitist Non-Dominated Sorting GA (NSGA-II) and its JG Adaptations 99
4.3 Simulated Annealing (SA) 106
4.3.1 Simple Simulated Annealing (SSA) for Single-Objective Problems 106
4.3.2 Multi-Objective Simulated Annealing (MOSA) 107
4.4 Application of the Jumping Gene Adaptations of NSGA-II and MOSA to Three Benchmark Problems 108
4.5 Results and Discussion (Metrics for the Comparison of Results) 110
4.6 Some Recent Chemical Engineering Applications Using the JG Adaptations of NSGA-II and MOSA 119
4.7 Conclusions 120
Acknowledgements 120
Appendix 121
Nomenclature 126
References 127
Exercises 129
Chapter 5 Surrogate Assisted Evolutionary Algorithm for Multi-Objective Optimization Tapabrata Ray Amitary Isaacs Warren Smith 131
5.1 Introduction 132
5.2 Surrogate Assisted Evolutionary Algorithm 134
5.2.1 Initialization 135
5.2.2 Actual Solution Archive 136
5.2.3 Selection 136
5.2.4 Crossover and Mutation 136
5.2.5 Ranking 137
5.2.6 Reduction 137
5.2.7 Building Surrogates 138
5.2.8 Evaluation using Surrogates 140
5.2.9 k-Means Clustering Algorithm 140
5.3 Numerical Examples 141
5.3.1 Zitzler-Deb-Thiele's (ZDT) Test Problems 142
5.3.2 Osyczka and Kundu (OSY) Test Problem 145
5.3.3 Tanaka (TNK) Test Problem 146
5.3.4 Alkylation Process Optimization 146
5.4 Conclusions 147
References 148
Exercises 150
Chapter 6 Why Use Interactive Multi-Objective Optimization in Chemical Process Design? Kaisa Miettinen Jussi Hakanen 153
6.1 Introduction 154
6.2 Concepts, Basic Methods and Some Shortcomings 155
6.2.1 Concepts 155
6.2.2 Some Basic Methods 158
6.3 Interactive Multi-Objective Optimization 161
6.3.1 Reference Point Approaches 163
6.3.2 Classification-Based Methods 164
6.3.3 Some Other Interactive Methods 170
6.4 Interactive Approaches in Chemical Process Design 171
6.5 Applications of Interactive Approaches 171
6.5.1 Simulated Moving Bed Processes 172
6.5.2 Water Allocation Problem 176
6.5.3 Heat Recovery System Design 178
6.6 Conclusions 181
References 182
Exercises 187
Chapter 7 Net Flow and Rough Sets: Two Methods for Ranking the Pareto Domain Jules Thibault 189
7.1 Introduction 190
7.2 Problem Formulation and Solution Procedure 193
7.3 Net Flow Method 196
7.4 Rough Set Method 203
7.5 Application: Production of Gluconic Acid 211
7.5.1 Definition of the Case Study 211
7.5.2 Net Flow Method 213
7.5.3 Rough Set Method 220
7.6 Conclusions 230
Acknowledgements 231
Nomenclature 231
References 232
Exercises 235
Chapter 8 Multi-Objective Optimization of Multi-Stage Gas-Phase Refrigeration Systems Nipen M. Shah Gade Pandu Rangaiah Andrew F. A. Hoadley 237
8.1 Introduction 238
8.2 Multi-Stage Gas-Phase Refrigeration Processes 241
8.2.1 Gas-Phase Refrigeration 241
8.2.2 Dual Independent Expander Refrigeration Processes for LNG 243
8.2.3 Significance of ΔTmin 245
8.3 Multi-Objective Optimization 246
8.4 Case Studies 247
8.4.1 Nitrogen Cooling using N2 Refrigerant 248
8.4.2 Liquefaction of Natural Gas using the Dual Independent Expander Process 256
8.4.3 Discussion 267
8.5 Conclusions 267
Acknowledgements 269
Nomenclature 269
References 270
Exercises 271
Chapter 9 Feed Optimization for Fluidized Catalytic Cracking using a Multi-Objective Evolutionary Algorithm Kay Chen Tan Ko Poh Phang Ying Jie Yang 277
9.1 Introduction 278
9.2 Feed Optimization for Fluidized Catalytic Cracking 279
9.2.1 Process Description 279
9.2.2 Challenges in the Feed Optimization 282
9.2.3 The Mathematical Model of FCC Feed Optimization 283
9.3 Evolutionary Multi-Objective Optimization 284
9.4 Experimental Results 288
9.5 Decision Making and Economic Evaluation 292
9.5.1 Fuel Gas Consumption of Reactor 72CC 293
9.5.2 High Pressure (HP) Steam Consumption of Reactor 72CC 295
9.5.3 Rate of Exothermic Reaction or Energy Gain 296
9.5.4 Summary of the Cost Analysis 297
9.6 Conclusions 298
References 298
Chapter 10 Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives Elaine Su-Quin Lee Gade Pandu Rangaiah Naveen Agrawal 301
10.1 Introduction 302
10.2 Williams-Otto Process Optimization for Multiple Economic Objectives 304
10.2.1 Process Model 305
10.2.2 Objectives for Optimization 308
10.2.3 Multi-Objective Optimization 309
10.3 LDPE Plant Optimization for Multiple Economic Objectives 314
10.3.1 Process Model and Objectives 314
10.3.2 Multi-Objective Optimization 317
10.4 Optimizing an Industrial Ecosystem for Economic and Environmental Objectives 320
10.4.1 Model of an IE with Six Plants 322
10.4.2 Objectives, Results and Discussion 325
10.5 Conclusions 334
Nomenclature 335
References 335
Exercises 336
Chapter 11 Multi-Objective Emergency Response Optimization Around Chemical Plants Paraskevi S. Georgiadou Ioannis A. Papazoglou Chris T. Kiranoudis Nikolaos C. Markatos 339
11.1 Introduction 340
11.2 Multi-Objective Emergency Response Optimization 342
11.2.1 Decision Space 342
11.2.2 Consequence Space 343
11.2.3 Determination of the Pareto Optimal Set of Solutions 343
11.2.4 General Structure of the Model 345
11.3 Consequence Assessment 345
11.3.1 Assessment of the Health Consequences on the Population 345
11.3.2 Socioeconomic Impacts 349
11.4 A MOEA for the Emergency Response Optimization 349
11.4.1 Representation of the Problem 349
11.4.2 General Structure of the MOEA 349
11.4.3 Initialization 350
11.4.4 "Fitness" Assignment 350
11.4.5 Environmental Selection 352
11.4.6 Termination 352
11.4.7 Mating Selection 352
11.4.8 Variation 353
11.5 Case Studies 353
11.6 Conclusions 358
Acknowledgements 359
References 359
Chapter 12 Array Informatics using Multi-Objective Genetic Algorithms: From Gene Expressions to Gene Networks Sanjeev Garg 363
12.1 Introduction 364
12.1.1 Biological Background 364
12.1.2 Interpreting the Scanned Image 367
12.1.3 Preprocessing of Microarray Data 368
12.2 Gene Expression Profiling and Gene Network Analysis 369
12.2.1 Gene Expression Profiling 370
12.2.2 Gene Network Analysis 371
12.2.3 Need for Newer Techniques? 377
12.3 Role of Multi-Objective Optimization 378
12.3.1 Model for Gene Expression Profiling 378
12.3.2 Implementation Details 380
12.3.3 Seed Population based NSGA-II 381
12.3.4 Model for Gene Network Analysis 382
12.4 Results and Discussion 386
12.5 Conclusions 395
Acknowledgments 396
References 396
Chapter 13 Optimization of a Multi-Product Microbial Cell Factory for Multiple Objectives - A Paradigm for Metabolic Pathway Recipe Fook Choon Lee Gade Pandu Rangaiah Dong-Yup Lee 401
13.1 Introduction 402
13.2 Central Carbon Metabolism of Escherichia coli 405
13.3 Formulation of the MOO Problem 408
13.4 Procedure used for Solving the MIMOO Problem 410
13.5 Optimization of Gene Knockouts 413
13.6 Optimization of Gene Manipulation 415
13.7 Conclusions 422
Nomenclature 424
References 426
Index 429