Multi-Objective Optimization : Techniques and Applications in Chemical Engineering

by Rangaiah, Gade Pandu

ISBN13: 9789812836519

ISBN10: 9812836519

Format: Hardcover

Pub. Date: 2009-03-31

Publisher(s): World Scientific Pub Co Inc

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Summary

Optimization has been playing a key role in the design, planning and operation of chemical and related processes for nearly half a century. Although process optimization for multiple objective was studied by several researchers back in the 1970s and 1980s, it has attracted active research in the last 10 years, spurred by the new and effective techniques for multi-objective optimization. In order to capture this renewed interest, this monograph presents the recent and ongoing research in multi-optimization techniques and their applications in chemical engineering.

Preface	p. v
Introduction	p. 1
Process Optimization	p. 1
Multi-Objective Optimization: Basics	p. 4
Multi-Objective Optimization: Methods	p. 8
Alkylation Process Optimization for Two Objectives	p. 13
Alkylation Process and its Model	p. 13
Multi-Objective Optimization Results and Discussion	p. 16
Scope and Organization of the Book	p. 18
References	p. 23
Exercises	p. 25
Multi-Objective Optimization Applications in Chemical Engineering	p. 27
Introduction	p. 28
Process Design and Operation	p. 29
Biotechnology and Food Industry	p. 30
Petroleum Refining and Petrochemicals	p. 40
Pharmaceuticals and Other Products/Processes	p. 41
Polymerization	p. 48
Conclusions	p. 48
References	p. 52
Multi-Objective Evolutionary Algorithms: A Review of the State-of-the-Art and some of their Applications in Chemical Engineering	p. 61
Introduction	p. 61
Basic Concepts	p. 62
Pareto Optimality	p. 63
The Early Days	p. 63
Modern MOEAs	p. 65
MOEAs in Chemical Engineering	p. 68
MOEAs Originated in Chemical Engineering	p. 68
Neighborhood and Archived Genetic Algorithm	p. 69
Criterion Selection MOEAs	p. 70
The Jumping Gene Operator	p. 72
Multi-Objective Differential Evolution	p. 73
Some Applications Using Well-Known MOEAs	p. 75
TYPE I: Optimization of an Industrial Nylon 6 Semi-Batch Reactor	p. 76
TYPE I: Optimization of an Industrial Ethylene Reactor	p. 76
TYPE II: Optimization of an Industrial Styrene Reactor	p. 77
TYPE II: Optimization of an Industrial Hydrocracking Unit	p. 76
TYPE III: Optimization of Semi-Batch Reactive Crystallization Process	p. 78
TYPE III: Optimization of Simulated Moving Bed Process	p. 79
TYPE IV: Biological and Bioinformatics Problems	p. 80
TYPE V: Optimization of a Waste Incineration Plant	p. 81
TYPE V: Chemical Process Systems Modelling	p. 81
Critical Remarks	p. 83
Additional Resources	p. 84
Future Research	p. 85
Conclusions	p. 85
Acknowledgements	p. 85
References	p. 86
Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations	p. 91
Introduction	p. 92
Genetic Algorithm (GA)	p. 93
Simple GA (SGA) for Single-Objective Problems	p. 93
Multi-Objective Elitist Non-Dominated Sorting GA (NSGA-II) and its JG Adaptations	p. 99
Simulated Annealing (SA)	p. 106
Simple Simulated Annealing (SSA) for Single-Objective Problems	p. 106
Multi-Objective Simulated Annealing (MOSA)	p. 107
Application of the Jumping Gene Adaptations of NSGA-II and MOSA to Three Benchmark Problems	p. 108
Results and Discussion (Metrics for the Comparison of Results)	p. 110
Some Recent Chemical Engineering Applications Using the JG Adaptations of NSGA-II and MOSA	p. 119
Conclusions	p. 120
Acknowledgements	p. 120
Appendix	p. 121
Nomenclature	p. 126
References	p. 127
Exercises	p. 129
Surrogate Assisted Evolutionary Algorithm for Multi-Objective Optimization	p. 131
Introduction	p. 132
Surrogate Assisted Evolutionary Algorithm	p. 134
Initialization	p. 135
Actual Solution Archive	p. 136
Selection	p. 136
Crossover and Mutation	p. 136
Ranking	p. 137
Reduction	p. 137
Building Surrogates	p. 138
Evaluation using Surrogates	p. 140
k-Means Clustering Algorithm	p. 140
Numerical Examples	p. 141
Zitzler-Deb-Thiele's (ZDT) Test Problems	p. 142
Osyczka and Kundu (OSY) Test Problem	p. 145
Tanaka (TNK) Test Problem	p. 146
Alkylation Process Optimization	p. 146
Conclusions	p. 147
References	p. 148
Exercises	p. 150
Why Use Interactive Multi-Objective Optimization in Chemical Process Design?	p. 153
Introduction	p. 154
Concepts, Basic Methods and Some Shortcomings	p. 155
Concepts	p. 155
Some Basic Methods	p. 158
Interactive Multi-Objective Optimization	p. 161
Reference Point Approaches	p. 163
Classification-Based Methods	p. 164
Some Other Interactive Methods	p. 170
Interactive Approaches in Chemical Process Design	p. 171
Applications of Interactive Approaches	p. 171
Simulated Moving Bed Processes	p. 172
Water Allocation Problem	p. 176
Heat Recovery System Design	p. 178
Conclusions	p. 181
References	p. 182
Exercises	p. 187
Net Flow and Rough Sets: Two Methods for Ranking the Pareto Domain	p. 189
Introduction	p. 190
Problem Formulation and Solution Procedure	p. 193
Net Flow Method	p. 196
Rough Set Method	p. 203
Application: Production of Gluconic Acid	p. 211
Definition of the Case Study	p. 211
Net Flow Method	p. 213
Rough Set Method	p. 220
Conclusions	p. 230
Acknowledgements	p. 231
Nomenclature	p. 231
References	p. 232
Exercises	p. 235
Multi-Objective Optimization of Multi-Stage Gas-Phase Refrigeration Systems	p. 237
Introduction	p. 238
Multi-Stage Gas-Phase Refrigeration Processes	p. 241
Gas-Phase Refrigeration	p. 241
Dual Independent Expander Refrigeration Processes for LNG	p. 243
Significance of ¿Tmin	p. 245
Multi-Objective Optimization	p. 246
Case Studies	p. 247
Nitrogen Cooling using N2 Refrigerant	p. 248
Liquefaction of Natural Gas using the Dual Independent Expander Process	p. 256
Discussion	p. 267
Conclusions	p. 267
Acknowledgements	p. 269
Nomenclature	p. 269
References	p. 270
Exercises	p. 271
Feed Optimization for Fluidized Catalytic Cracking using a Multi-Objective Evolutionary Algorithm	p. 277
Introduction	p. 278
Feed Optimization for Fluidized Catalytic Cracking	p. 279
Process Description	p. 279
Challenges in the Feed Optimization	p. 282
The Mathematical Model of FCC Feed Optimization	p. 283
Evolutionary Multi-Objective Optimization	p. 284
Experimental Results	p. 288
Decision Making and Economic Evaluation	p. 292
Fuel Gas Consumption of Reactor 72CC	p. 293
High Pressure (HP) Steam Consumption of Reactor 72CC	p. 295
Rate of Exothermic Reaction or Energy Gain	p. 296
Summary of the Cost Analysis	p. 297
Conclusions	p. 298
References	p. 298
Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives	p. 301
Introduction	p. 302
Williams-Otto Process Optimization for Multiple Economic Objectives	p. 304
Process Model	p. 305
Objectives for Optimization	p. 308
Multi-Objective Optimization	p. 309
LDPE Plant Optimization for Multiple Economic Objectives	p. 314
Process Model and Objectives	p. 314
Multi-Objective Optimization	p. 317
Optimizing an Industrial Ecosystem for Economic and Environmental Objectives	p. 320
Model of an IE with Six Plants	p. 322
Objectives, Results and Discussion	p. 325
Conclusions	p. 334
Nomenclature	p. 335
References	p. 335
Exercises	p. 336
Multi-Objective Emergency Response Optimization Around Chemical Plants	p. 339
Introduction	p. 340
Multi-Objective Emergency Response Optimization	p. 342
Decision Space	p. 342
Consequence Space	p. 343
Determination of the Pareto Optimal Set of Solutions	p. 343
General Structure of the Model	p. 345
Consequence Assessment	p. 345
Assessment of the Health Consequences on the Population	p. 345
Socioeconomic Impacts	p. 349
A MOEA for the Emergency Response Optimization	p. 349
Representation of the Problem	p. 349
General Structure of the MOEA	p. 349
Initialization	p. 350
"Fitness" Assignment	p. 350
Environmental Selection	p. 352
Termination	p. 352
Mating Selection	p. 352
Variation	p. 353
Case Studies	p. 353
Conclusions	p. 358
Acknowledgements	p. 359
References	p. 359
Array Informatics using Multi-Objective Genetic Algorithms: From Gene Expressions to Gene Networks	p. 363
Introduction	p. 364
Biological Background	p. 364
Interpreting the Scanned Image	p. 367
Preprocessing of Microarray Data	p. 368
Gene Expression Profiling and Gene Network Analysis	p. 369
Gene Expression Profiling	p. 370
Gene Network Analysis	p. 371
Need for Newer Techniques?	p. 377
Role of Multi-Objective Optimization	p. 378
Model for Gene Expression Profiling	p. 378
Implementation Details	p. 380
Seed Population based NSGA-II	p. 381
Model for Gene Network Analysis	p. 382
Results and Discussion	p. 386
Conclusions	p. 395
Acknowledgments	p. 396
References	p. 396
Optimization of a Multi-Product Microbial Cell Factory for Multiple Objectives - A Paradigm for Metabolic Pathway Recipe	p. 401
Introduction	p. 402
Central Carbon Metabolism of Escherichia coli	p. 405
Formulation of the MOO Problem	p. 408
Procedure used for Solving the MIMOO Problem	p. 410
Optimization of Gene Knockouts	p. 413
Optimization of Gene Manipulation	p. 415
Conclusions	p. 422
Nomenclature	p. 424
References	p. 426
Index	p. 429
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Multi-Objective Optimization : Techniques and Applications in Chemical Engineering

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