Multi-Objective Optimizationin Chemical Engineering; Contents; List of Contributors; Preface; Part I Overview; 1 Introduction; 1.1 Optimization and Chemical Engineering; 1.2 Basic Definitions and Concepts of Multi-Objective Optimization; 1.3 Multi-Objective Optimization in Chemical Engineering; 1.4 Scope and Organization of the Book; References; 2 Optimization of Pooling Problems for Two Objectives Using the e-Constraint Method; 2.1 Introduction; 2.2 Pooling Problem Description and Formulations; 2.2.1 p-Formulation; 2.2.2 r-Formulation; 2.3 e-Constraint Method and IDE Algorithm. 2.4 Application to Pooling Problems2.5 Results and Discussion; 2.6 Conclusions; Exercises; References; 3 Multi-Objective Optimization Applications in Chemical Engineering; 3.1 Introduction; 3.2 MOO Applications in Process Design and Operation; 3.3 MOO Applications in Petroleum Refining, Petrochemicals and Polymerization; 3.4 MOO Applications in the Food Industry, Biotechnology and Pharmaceuticals; 3.5 MOO Applications in Power Generation and Carbon Dioxide Emissions; 3.6 MOO Applications in Renewable Energy; 3.7 MOO Applications in Hydrogen Production and Fuel Cells; 3.8 Conclusions; Acronyms. 5 Improved Constraint Handling Technique for Multi-Objective Optimization with Application to Two Fermentation Processes5.1 Introduction; 5.2 Constraint Handling Approaches in Chemical Engineering; 5.3 Adaptive Constraint Relaxation and Feasibility Approach for SOO; 5.4 Adaptive Relaxation of Constraints and Feasibility Approach for MOO; 5.5 Testing of MODE-ACRFA; 5.6 Multi-Objective Optimization of the Fermentation Process; 5.6.1 Three-Stage Fermentation Process Integrated with Cell Recycling; 5.6.2 Three-Stage Fermentation Process Integrated with Cell Recycling and Extraction. 5.6.3 General Discussion5.7 Conclusions; Acronyms; References; 6 Robust Multi-Objective Genetic Algorithm (RMOGA) with Online Approximation under Interval Uncertainty; 6.1 Introduction; 6.2 Background and Definition; 6.2.1 Multi-Objective Genetic Algorithm (MOGA); 6.2.2 Multi-Objective Robustness with Interval Uncertainty: Basic Idea; 6.3 Robust Multi-Objective Genetic Algorithm (RMOGA); 6.3.1 Nested RMOGA; 6.3.2 Sequential RMOGA; 6.3.3 Comparison between Nested and Sequential RMOGA; 6.4 Online Approximation-Assisted RMOGA; 6.4.1 Steps in Approximation-Assisted RMOGA; 6.4.2 Sampling.

For reasons both financial and environmental, there is a perpetual need to optimize the design and operating conditions of industrial process systems in order to improve their performance, energy efficiency, profitability, safety and reliability. However, with most chemical engineering application problems having many variables with complex inter-relationships, meeting these optimization objectives can be challenging. This is where Multi-Objective Optimization (MOO) is useful to find the optimal trade-offs among two or more conflicting objectives. This book provides an overview of the