| 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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