Contents -- Preface -- Chapter 1 Introduction Gade Pandu Rangaiah -- 1.1 Process Optimization -- 1.2 Multi-Objective Optimization: Basics -- 1.3 Multi-Objective Optimization: Methods -- 1.4 Alkylation Process Optimization for Two Objectives -- 1.4.1 Alkylation Process and its Model -- 1.4.2 Multi-Objective Optimization Results and Discussion -- 1.5 Scope and Organization of the Book -- References -- Exercises -- Chapter 2 Multi-Objective Optimization Applications in Chemical Engineering Masuduzzaman and Gade Pandu Rangaiah -- Abstract -- 2.1 Introduction -- 2.2 Process Design and Operation -- 2.3 Biotechnology and Food Industry -- 2.4 Petroleum Refining and Petrochemicals -- 2.5 Pharmaceuticals and Other Products/Processes -- 2.6 Polymerization -- 2.7 Conclusions -- References -- 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 and Carlos A. Coello Coello -- Abstract -- 3.1 Introduction -- 3.2 Basic Concepts -- 3.2.1 Pareto Optimality -- 3.3 The Early Days -- 3.4 Modern MOEAs -- 3.5 MOEAs in Chemical Engineering -- 3.6 MOEAs Originated in Chemical Engineering -- 3.6.1 Neighborhood and Archived Genetic Algorithm -- 3.6.2 Criterion Selection MOEAs -- 3.6.3 The Jumping Gene Operator -- 3.6.4 Multi-Objective Differential Evolution -- 3.7 Some Applications Using Well-Known MOEAs -- 3.7.1 TYPE I: Optimization of an Industrial Nylon 6 Semi-Batch Reactor -- 3.7.2 TYPE I: Optimization of an Industrial Ethylene Reactor -- 3.7.3 TYPE II: Optimization of an Industrial Styrene Reactor -- 3.7.4 TYPE II: Optimization of an Industrial Hydrocracking Unit -- 3.7.5 TYPE III: Optimization of Semi-Batch Reactive Crystallization Process -- 3.7.6 TYPE III: Optimization of Simulated Moving Bed Process -- 3.7.7 TYPE IV: Biological and Bioinformatics Problems.

3.7.8 TYPE V: Optimization of a Waste Incineration Plant -- 3.7.9 TYPE V: Chemical Process Systems Modelling -- 3.8 Critical Remarks -- 3.9 Additional Resources -- 3.10 Future Research -- 3.11 Conclusions -- Acknowledgements -- References -- Chapter 4 Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations Manojkumar Ramteke and Santosh K. Gupta -- Abstract -- 4.1 Introduction -- 4.2 Genetic Algorithm (GA) -- 4.2.1 Simple GA (SGA) for Single-Objective Problems -- 4.2.2 Multi-Objective Elitist Non-Dominated Sorting GA (NSGA-II) and its JG Adaptations -- 4.2.2.1 Jumping Genes/Transposons (Stryer, 2000) -- 4.2.2.2 (Variable-Length) Binary-Coded NSGA-II-JG (Kasat and Gupta, 2003) -- 4.2.2.3 (Fixed-Length) NSGA-II-aJG -- 4.2.2.4 NSGA-II-mJG ('modified' JG) -- 4.2.2.5 NSGA-II-saJG ('specific adapted' JG) -- 4.2.2.6 NSGA-II-sJG ('specific' JG) -- 4.3 Simulated Annealing (SA) -- 4.3.1 Simple Simulated Annealing (SSA) for Single-Objective Problems -- 4.3.2 Multi-Objective Simulated Annealing (MOSA) -- 4.4 Application of the Jumping Gene Adaptations of NSGA-II and MOSA to Three Benchmark Problems -- 4.5 Results and Discussion (Metrics for the Comparison of Results) -- 4.6 Some Recent Chemical Engineering Applications Using the JG Adaptations of NSGA-II and MOSA -- 4.7 Conclusions -- Acknowledgements -- Appendix -- Nomenclature -- References -- Exercises -- Chapter 5 Surrogate Assisted Evolutionary Algorithm for Multi-Objective Optimization Tapabrata Ray, Amitay Isaacs and Warren Smith -- Abstract -- 5.1 Introduction -- 5.2 Surrogate Assisted Evolutionary Algorithm -- 5.2.1 Initialization -- 5.2.2 Actual Solution Archive -- 5.2.3 Selection -- 5.2.4 Crossover and Mutation -- 5.2.5 Ranking -- 5.2.6 Reduction -- 5.2.7 Building Surrogates -- 5.2.8 Evaluation using Surrogates -- 5.2.9 k-Means Clustering Algorithm.

5.3 Numerical Examples -- 5.3.1 Zitzler-Deb-Thiele's (ZDT) Test Problems -- 5.3.2 Osyczka and Kundu (OSY) Test Problem -- 5.3.3 Tanaka (TNK) Test Problem -- 5.3.4 Alkylation Process Optimization -- 5.4 Conclusions -- References -- Exercises -- Chapter 6 Why Use Interactive Multi-Objective Optimization in Chemical Process Design? Kaisa Miettinen and Jussi Hakanen -- 6.1 Introduction -- 6.2 Concepts, Basic Methods and Some Shortcomings -- 6.2.1 Concepts -- 6.2.2 Some Basic Methods -- 6.3 Interactive Multi-Objective Optimization -- 6.3.1 Reference Point Approaches -- 6.3.2 Classification-Based Methods -- 6.3.3 Some Other Interactive Methods -- 6.4 Interactive Approaches in Chemical Process Design -- 6.5 Applications of Interactive Approaches -- 6.5.1 Simulated Moving Bed Processes -- 6.5.2 Water Allocation Problem -- 6.5.3 Heat Recovery System Design -- 6.6 Conclusions -- References -- Exercises -- Chapter 7 Net Flow and Rough Sets: Two Methods for Ranking the Pareto Domain Jules Thibault -- Abstract -- 7.1 Introduction -- 7.2 Problem Formulation and Solution Procedure -- 7.3 Net Flow Method -- 7.4 Rough Set Method -- 7.5 Application: Production of Gluconic Acid -- 7.5.1 Definition of the Case Study -- 7.5.2 Net Flow Method -- 7.5.3 Rough Set Method -- 7.6 Conclusions -- Acknowledgements -- Nomenclature -- References -- Exercises -- Chapter 8 Multi-Objective Optimization of Multi-Stage Gas-Phase Refrigeration Systems Nipen M. Shah, Gade Pandu Rangaiah and Andrew F. A. Hoadley -- Abstract -- 8.1 Introduction -- 8.2 Multi-Stage Gas-Phase Refrigeration Processes -- 8.2.1 Gas-Phase Refrigeration -- 8.2.2 Dual Independent Expander Refrigeration Process for LNG -- 8.2.3 Significance of Tmin -- 8.3 Multi-Objective Optimization -- 8.4 Case Studies -- 8.4.1 Nitrogen Cooling using N2 Refrigerant.

8.4.2 Liquefaction of Natural Gas using the Dual Independent Expander Process -- 8.4.3 Discussion -- 8.5 Conclusions -- Acknowledgements -- Nomenclature -- References -- Exercises -- Chapter 9 Feed Optimization for Fluidized Catalytic Cracking using a Multi-Objective Evolutionary Algorithm Kay Chen Tan, Ko Poh Phang and Ying Jie Yang -- Abstract -- 9.1 Introduction -- 9.2 Feed Optimization for Fluidized Catalytic Cracking -- 9.2.1 Process Description -- 9.2.2 Challenges in the Feed Optimization -- 9.2.3 The Mathematical Model of FCC Feed Optimization -- 9.3 Evolutionary Multi-Objective Optimization -- 9.4 Experimental Results -- 9.5 Decision Making and Economic Evaluation -- 9.5.1 Fuel Gas Consumption of Reactor 72CC -- 9.5.2 High Pressure (HP) Steam Consumption of Reactor 72CC -- 9.5.3 Rate of Exothermic Reaction or Energy Gain -- 9.5.4 Summary of the Cost Analysis -- 9.6 Conclusions -- References -- Chapter 10 Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives Elaine Su-Qin Lee, Gade Pandu Rangaiah and Naveen Agrawal -- Abstract -- 10.1 Introduction -- 10.2 Williams-Otto Process Optimization for Multiple Economic Objectives -- 10.2.1 Process Model -- 10.2.2 Objectives for Optimization -- 10.2.3 Multi-Objective Optimization -- 10.3 LDPE Plant Optimization for Multiple Economic Objectives -- 10.3.1 Process Model and Objectives -- 10.3.2 Multi-Objective Optimization -- 10.4 Optimizing an Industrial Ecosystem for Economic and Environmental Objectives -- 10.4.1 Model of an IE with Six Plants -- 10.4.2 Objectives, Results and Discussion -- 10.5 Conclusions -- Nomenclature -- References -- Exercises -- Chapter 11 Multi-Objective Emergency Response Optimization Around Chemical Plants Paraskevi S. Georgiadou, Ioannis A. Papazoglou, Chris T. Kiranoudis and Nikolaos C. Markatos -- Abstract -- 11.1 Introduction.

11.2 Multi-Objective Emergency Response Optimization -- 11.2.1 Decision Space -- 11.2.2 Consequence Space -- 11.2.3 Determination of the Pareto Optimal Set of Solutions -- 11.2.4 General Structure of the Model -- 11.3 Consequence Assessment -- 11.3.1 Assessment of the Health Consequences on the Population -- 11.3.2 Socioeconomic Impacts -- 11.4 A MOEA for the Emergency Response Optimization -- 11.4.1 Representation of the Problem -- 11.4.2 General Structure of the MOEA -- 11.4.3 Initialization -- 11.4.4 "Fitness" Assignment -- 11.4.5 Environmental Selection -- 11.4.6 Termination -- 11.4.7 Mating Selection -- 11.4.8 Variation -- 11.5 Case Studies -- 11.6 Conclusions -- Acknowledgements -- References -- Chapter 12 Array Informatics using Multi-Objective Genetic Algorithms: From Gene Expressions to Gene Networks Sanjeev Garg -- Abstract -- 12.1 Introduction -- 12.1.1 Biological Background -- 12.1.2 Interpreting the Scanned Image -- 12.1.3 Preprocessing of Microarray Data -- 12.2 Gene Expression Profiling and Gene Network Analysis -- 12.2.1 Gene Expression Profiling -- 12.2.2 Gene Network Analysis -- 12.2.3 Need for Newer Techniques? -- 12.3 Role of Multi-Objective Optimization -- 12.3.1 Model for Gene Expression Profiling -- 12.3.2 Implementation Details -- 12.3.3 Seed Population based NSGA-II -- 12.3.4 Model for Gene Network Analysis -- 12.4 Results and Discussion -- 12.5 Conclusions -- Acknowledgments -- References -- 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 and Dong-Yup Lee -- Abstract -- 13.1 Introduction -- 13.2 Central Carbon Metabolism of Escherichia coli -- 13.3 Formulation of the MOO Problem -- 13.4 Procedure used for Solving the MIMOO Problem -- 13.5 Optimization of Gene Knockouts.

13.6 Optimization of Gene Manipulation.