World Scientific Publishing Co. Pte. Ltd. 2009. - 435 p.
Optimization is essential for reducing material and energy requirements as well as the harmful environmental impact of chemical processes. It leads to better design and operation of chemical processes as well as to sustainable processes. Many applications of optimization involve several objectives, some of which are conflicting. Multi-objective optimization (MOO) is required to solve the resulting problems in these applications. Hence MOO has attracted the attention of several researchers, particularly in the last ten years.
The book will be useful to researchers in academic and research institutions, to engineers and managers in process industries, and to graduates and senior-level undergraduates. Researchers and engineers can use it for applying MOO to their processes whereas students can utilize it as a supplementary text in optimization courses. Each of the chapters in the book can be read and understood with little reference to other chapters. However, readers are encouraged to go through the introduction chapter first. Many chapters contain several exercises at the end, which can be used for assignments and projects. Some of these and the applications discussed within the chapters can be used as projects in optimization courses at both undergraduate and postgraduate levels.
Contents
Preface
Introduction
Gade Pandu Rangaiah
Process Optimization
Multi-Objective Optimization: Basics
Multi-Objective Optimization: Methods
Alkylation Process Optimization for Two Objectives
Alkylation Process and its Model
Multi-Objective Optimization Results and Discussion
Scope and Organization of the Book
References
Exercises
Multi-Objective Optimization Applications in Chemical Engineering
Masuduzzaman and Gade Pandu Rangaiah
Introduction
Process Design and Operation
Biotechnology and Food Industry
Petroleum Refining and Petrochemicals
Pharmaceuticals and Other Products/Processes
Polymerization
Conclusions
References
Multi-Objective Evolutionary Algorithms: A Review of the State-of-the-Art and some of their Applications in Chemical Engineering
Antonio L?pez Jaimes and Carlos A Coello Coello
Introduction
Basic Concepts
Pareto Optimality
The Early Days
Mode MOEAs
MOEAs in Chemical Engineering
MOEAs Originated in Chemical Engineering
Neighborhood and Archived Genetic Algorithm
Criterion Selection MOEAs
The Jumping Gene Operator
Multi-Objective Differential Evolution
Some Applications Using Well-Known MOEAs
TYPE I: Optimization of an Industrial Nylon
Semi-Batch Reactor
TYPE I: Optimization of an Industrial Ethylene Reactor
TYPE II: Optimization of an Industrial Styrene Reactor
TYPE II: Optimization of an Industrial Hydrocracking
Unit
TYPE III: Optimization of Semi-Batch Reactive
Crystallization Process
TYPE III: Optimization of Simulated Moving Bed
Process
TYPE IV: Biological and Bioinformatics Problems
TYPE V: Optimization of a Waste Incineration Plant
TYPE V: Chemical Process Systems Modelling
Critical Remarks
Additional Resources
Future Research
Conclusions
Acknowledgements
References
Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations
Manojkumar Ramteke and Santosh K Gupta
Introduction
Genetic Algorithm (GA)
Simple GA (SGA) for Single-Objective Problems
Multi-Objective Elitist Non-Dominated Sorting GA
(NSGA-II) and its JG Adaptations
Simulated Annealing (SA)
Simple Simulated Annealing (SSA) for
Single-Objective Problems
Multi-Objective Simulated Annealing (MOSA)
Application of the Jumping Gene Adaptations of NSGA-II and MOSA to Three Benchmark Problems
Results and Discussion (Metrics for the Comparison of Results)
Some Recent Chemical Engineering Applications Using the JG Adaptations of NSGA-II and MOSA
Conclusions
Acknowledgements
Appendix
Nomenclature
References
Exercises
Surrogate Assisted Evolutionary Algorithm for Multi-Objective Optimization
Tapabrata Ray, Amitay Isaacs and Warren Smith
Introduction
Surrogate Assisted Evolutionary Algorithm
Initialization
Actual Solution Archive
Selection
Crossover and Mutation
Ranking
Reduction
Building Surrogates
Evaluation using Surrogates
k-Means Clustering Algorithm
Numerical Examples
Zitzler-Deb-Thiele’s (ZDT) Test Problems
Osyczka and Kundu (OSY) Test Problem
Tanaka (TNK) Test Problem
Alkylation Process Optimization
Conclusions
References
Exercises
Why Use Interactive Multi-Objective Optimization in Chemical Process Design?
Kaisa Miettinen and Jussi Hakanen
Introduction
Concepts, Basic Methods and Some Shortcomings
Concepts
Some Basic Methods
Interactive Multi-Objective Optimization
Reference Point Approaches
Classification-Based Methods
Some Other Interactive Methods
Interactive Approaches in Chemical Process Design
Applications of Interactive Approaches
Simulated Moving Bed Processes
Water Allocation Problem
Heat Recovery System Design
Conclusions
References
Exercises
Net Flow and Rough Sets: Two Methods for Ranking the Pareto Domain
Jules Thibault
Introduction
Problem Formulation and Solution Procedure
Net Flow Method
Rough Set Method
Application: Production of Gluconic Acid
Definition of the Case Study
Net Flow Method
Rough Set Method
Conclusions
Acknowledgements
Nomenclature
References
Exercises
Multi-Objective Optimization of Multi-Stage Gas-Phase Refrigeration Systems
Nipen M Shah, Gade Pandu Rangaiah and Andrew F A Hoadley
Introduction
Multi-Stage Gas-Phase Refrigeration Processes
Gas-Phase Refrigeration
Dual Independent Expander Refrigeration Process for LNG
Significance of _Tmin
Multi-Objective Optimization
Case Studies
Nitrogen Cooling using N Refrigerant
Liquefaction of Natural Gas using the Dual Independent Expander Process
Discussion
Conclusions
Acknowledgements
Nomenclature
References
Exercises
Feed Optimization for Fluidized Catalytic Cracking using a Multi-Objective Evolutionary Algorithm
Kay Chen Tan, Ko Poh Phang and Ying Jie Yang
Introduction
Feed Optimization for Fluidized Catalytic Cracking
Process Description
Challenges in the Feed Optimization
The Mathematical Model of FCC Feed Optimization
Evolutionary Multi-Objective Optimization
Experimental Results
Decision Making and Economic Evaluation
Fuel Gas Consumption of Reactor CC
High Pressure (HP) Steam Consumption of Reactor CC
Rate of Exothermic Reaction or Energy Gain
Summary of the Cost Analysis
Conclusions
References
Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives
Elaine Su-Qin Lee, Gade Pandu Rangaiah and Naveen Agrawal
Introduction
Williams-Otto Process Optimization for Multiple Economic Objectives
Process Model
Objectives for Optimization
Multi-Objective Optimization
LDPE Plant Optimization for Multiple Economic Objectives
Process Model and Objectives
Multi-Objective Optimization
Optimizing an Industrial Ecosystem for Economic and Environmental Objectives
Model of an IE with Six Plants
Objectives, Results and Discussion
Conclusions
Nomenclature
References
Exercises
Multi-Objective Emergency Response Optimization Around Chemical Plants
Paraskevi S Georgiadou, Ioannis A Papazoglou, Chris T Kiranoudis and Nikolaos C Markatos
Introduction
Multi-Objective Emergency Response Optimization
Decision Space
Consequence Space
Determination of the Pareto Optimal Set of Solutions
General Structure of the Model
Consequence Assessment
Assessment of the Health Consequences on the Population
Socioeconomic Impacts
A MOEA for the Emergency Response Optimization
Representation of the Problem
General Structure of the MOEA
Initialization
Fitness Assignment
Environmental Selection
Termination
Mating Selection
Variation
Case Studies
Conclusions
Acknowledgements
References
Array Informatics using Multi-Objective Genetic Algorithms: From Gene Expressions to Gene Networks
Sanjeev Garg
Introduction
Biological Background
Interpreting the Scanned Image
Preprocessing of Microarray Data
Gene Expression Profiling and Gene Network Analysis
Gene Expression Profiling
Gene Network Analysis
Need for Newer Techniques?
Role of Multi-Objective Optimization
Model for Gene Expression Profiling
Implementation Details
Seed Population based NSGA-II
Model for Gene Network Analysis
Results and Discussion
Conclusions
Acknowledgments
References
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
Introduction
Central Carbon Metabolism of Escherichia coli
Formulation of the MOO Problem
Procedure used for Solving the MIMOO Problem
Optimization of Gene Knockouts
Optimization of Gene Manipulation
Conclusions
Nomenclature
References
Index
Optimization is essential for reducing material and energy requirements as well as the harmful environmental impact of chemical processes. It leads to better design and operation of chemical processes as well as to sustainable processes. Many applications of optimization involve several objectives, some of which are conflicting. Multi-objective optimization (MOO) is required to solve the resulting problems in these applications. Hence MOO has attracted the attention of several researchers, particularly in the last ten years.
The book will be useful to researchers in academic and research institutions, to engineers and managers in process industries, and to graduates and senior-level undergraduates. Researchers and engineers can use it for applying MOO to their processes whereas students can utilize it as a supplementary text in optimization courses. Each of the chapters in the book can be read and understood with little reference to other chapters. However, readers are encouraged to go through the introduction chapter first. Many chapters contain several exercises at the end, which can be used for assignments and projects. Some of these and the applications discussed within the chapters can be used as projects in optimization courses at both undergraduate and postgraduate levels.
Contents
Preface
Introduction
Gade Pandu Rangaiah
Process Optimization
Multi-Objective Optimization: Basics
Multi-Objective Optimization: Methods
Alkylation Process Optimization for Two Objectives
Alkylation Process and its Model
Multi-Objective Optimization Results and Discussion
Scope and Organization of the Book
References
Exercises
Multi-Objective Optimization Applications in Chemical Engineering
Masuduzzaman and Gade Pandu Rangaiah
Introduction
Process Design and Operation
Biotechnology and Food Industry
Petroleum Refining and Petrochemicals
Pharmaceuticals and Other Products/Processes
Polymerization
Conclusions
References
Multi-Objective Evolutionary Algorithms: A Review of the State-of-the-Art and some of their Applications in Chemical Engineering
Antonio L?pez Jaimes and Carlos A Coello Coello
Introduction
Basic Concepts
Pareto Optimality
The Early Days
Mode MOEAs
MOEAs in Chemical Engineering
MOEAs Originated in Chemical Engineering
Neighborhood and Archived Genetic Algorithm
Criterion Selection MOEAs
The Jumping Gene Operator
Multi-Objective Differential Evolution
Some Applications Using Well-Known MOEAs
TYPE I: Optimization of an Industrial Nylon
Semi-Batch Reactor
TYPE I: Optimization of an Industrial Ethylene Reactor
TYPE II: Optimization of an Industrial Styrene Reactor
TYPE II: Optimization of an Industrial Hydrocracking
Unit
TYPE III: Optimization of Semi-Batch Reactive
Crystallization Process
TYPE III: Optimization of Simulated Moving Bed
Process
TYPE IV: Biological and Bioinformatics Problems
TYPE V: Optimization of a Waste Incineration Plant
TYPE V: Chemical Process Systems Modelling
Critical Remarks
Additional Resources
Future Research
Conclusions
Acknowledgements
References
Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations
Manojkumar Ramteke and Santosh K Gupta
Introduction
Genetic Algorithm (GA)
Simple GA (SGA) for Single-Objective Problems
Multi-Objective Elitist Non-Dominated Sorting GA
(NSGA-II) and its JG Adaptations
Simulated Annealing (SA)
Simple Simulated Annealing (SSA) for
Single-Objective Problems
Multi-Objective Simulated Annealing (MOSA)
Application of the Jumping Gene Adaptations of NSGA-II and MOSA to Three Benchmark Problems
Results and Discussion (Metrics for the Comparison of Results)
Some Recent Chemical Engineering Applications Using the JG Adaptations of NSGA-II and MOSA
Conclusions
Acknowledgements
Appendix
Nomenclature
References
Exercises
Surrogate Assisted Evolutionary Algorithm for Multi-Objective Optimization
Tapabrata Ray, Amitay Isaacs and Warren Smith
Introduction
Surrogate Assisted Evolutionary Algorithm
Initialization
Actual Solution Archive
Selection
Crossover and Mutation
Ranking
Reduction
Building Surrogates
Evaluation using Surrogates
k-Means Clustering Algorithm
Numerical Examples
Zitzler-Deb-Thiele’s (ZDT) Test Problems
Osyczka and Kundu (OSY) Test Problem
Tanaka (TNK) Test Problem
Alkylation Process Optimization
Conclusions
References
Exercises
Why Use Interactive Multi-Objective Optimization in Chemical Process Design?
Kaisa Miettinen and Jussi Hakanen
Introduction
Concepts, Basic Methods and Some Shortcomings
Concepts
Some Basic Methods
Interactive Multi-Objective Optimization
Reference Point Approaches
Classification-Based Methods
Some Other Interactive Methods
Interactive Approaches in Chemical Process Design
Applications of Interactive Approaches
Simulated Moving Bed Processes
Water Allocation Problem
Heat Recovery System Design
Conclusions
References
Exercises
Net Flow and Rough Sets: Two Methods for Ranking the Pareto Domain
Jules Thibault
Introduction
Problem Formulation and Solution Procedure
Net Flow Method
Rough Set Method
Application: Production of Gluconic Acid
Definition of the Case Study
Net Flow Method
Rough Set Method
Conclusions
Acknowledgements
Nomenclature
References
Exercises
Multi-Objective Optimization of Multi-Stage Gas-Phase Refrigeration Systems
Nipen M Shah, Gade Pandu Rangaiah and Andrew F A Hoadley
Introduction
Multi-Stage Gas-Phase Refrigeration Processes
Gas-Phase Refrigeration
Dual Independent Expander Refrigeration Process for LNG
Significance of _Tmin
Multi-Objective Optimization
Case Studies
Nitrogen Cooling using N Refrigerant
Liquefaction of Natural Gas using the Dual Independent Expander Process
Discussion
Conclusions
Acknowledgements
Nomenclature
References
Exercises
Feed Optimization for Fluidized Catalytic Cracking using a Multi-Objective Evolutionary Algorithm
Kay Chen Tan, Ko Poh Phang and Ying Jie Yang
Introduction
Feed Optimization for Fluidized Catalytic Cracking
Process Description
Challenges in the Feed Optimization
The Mathematical Model of FCC Feed Optimization
Evolutionary Multi-Objective Optimization
Experimental Results
Decision Making and Economic Evaluation
Fuel Gas Consumption of Reactor CC
High Pressure (HP) Steam Consumption of Reactor CC
Rate of Exothermic Reaction or Energy Gain
Summary of the Cost Analysis
Conclusions
References
Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives
Elaine Su-Qin Lee, Gade Pandu Rangaiah and Naveen Agrawal
Introduction
Williams-Otto Process Optimization for Multiple Economic Objectives
Process Model
Objectives for Optimization
Multi-Objective Optimization
LDPE Plant Optimization for Multiple Economic Objectives
Process Model and Objectives
Multi-Objective Optimization
Optimizing an Industrial Ecosystem for Economic and Environmental Objectives
Model of an IE with Six Plants
Objectives, Results and Discussion
Conclusions
Nomenclature
References
Exercises
Multi-Objective Emergency Response Optimization Around Chemical Plants
Paraskevi S Georgiadou, Ioannis A Papazoglou, Chris T Kiranoudis and Nikolaos C Markatos
Introduction
Multi-Objective Emergency Response Optimization
Decision Space
Consequence Space
Determination of the Pareto Optimal Set of Solutions
General Structure of the Model
Consequence Assessment
Assessment of the Health Consequences on the Population
Socioeconomic Impacts
A MOEA for the Emergency Response Optimization
Representation of the Problem
General Structure of the MOEA
Initialization
Fitness Assignment
Environmental Selection
Termination
Mating Selection
Variation
Case Studies
Conclusions
Acknowledgements
References
Array Informatics using Multi-Objective Genetic Algorithms: From Gene Expressions to Gene Networks
Sanjeev Garg
Introduction
Biological Background
Interpreting the Scanned Image
Preprocessing of Microarray Data
Gene Expression Profiling and Gene Network Analysis
Gene Expression Profiling
Gene Network Analysis
Need for Newer Techniques?
Role of Multi-Objective Optimization
Model for Gene Expression Profiling
Implementation Details
Seed Population based NSGA-II
Model for Gene Network Analysis
Results and Discussion
Conclusions
Acknowledgments
References
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
Introduction
Central Carbon Metabolism of Escherichia coli
Formulation of the MOO Problem
Procedure used for Solving the MIMOO Problem
Optimization of Gene Knockouts
Optimization of Gene Manipulation
Conclusions
Nomenclature
References
Index