Stochastic Process Optimization using Aspen Plus®

Stochastic Process Optimization using Aspen® Plus Bookshop Category: Chemical Engineering Optimization can be simply defined as "choosing the best alternative among a set of feasible options". In all the engineering areas, optimization has a wide range of applications, due to the high number of decisions involved in an engineering environment. Chemical engineering, and particularly process engineering, is not an exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models. In this book, the combined use of the modular simulator Aspen**®** Plus and stochastic optimization methods, codified in MATLAB, is presented. Some basic concepts of optimization are first presented, then, strategies to use the simulator linked with the optimization algorithm are shown. Finally, examples of application for process engineering are discussed. The reader will learn how to link the process simulator Aspen**®** Plus and stochastic optimization algorithms to solve process design problems. They will gain ability to perform multi-objective optimization in several case studies. **Key Features:**• The book links simulation and optimization through numerical analyses and stochastic optimization techniques • Includes use of examples to illustrate the application of the concepts and specific guidance on the use of software (Aspen**®** Plus, Excel, MATLB) to set up and solve models representing complex problems. • Illustrates several examples of applications for the linking of simulation and optimization software with other packages for optimization purposes. • Provides specific information on how to implement stochastic optimization with process simulators. • Enable readers to identify practical and economic solutions to problems of industrial relevance, enhancing the safety, operation, environmental, and economic performance of chemical processes. Content: ""Cover"" ""Half title"" ""Title page"" ""Copyright page"" ""Dedication"" ""Contents"" ""Preface"" ""Acknowledgment"" ""Editors"" ""Contributors"" ""Chapter 1: Introduction to Optimization"" ""1.1 What Is Optimization?"" ""1.2 Mathematical Modeling and Optimization"" ""1.3 Classification of Optimization Problems"" ""1.4 Objective Function"" ""1.5 Optimization with Constraints: Feasible Region"" ""1.6 Multiobjective Optimization"" ""1.6.1 Weighted Sum Method"" ""1.6.2 Constraint Method"" ""1.7 Process Optimization"" ""References"" ""Chapter 2: Deterministic Optimization"" ""2.1 Introduction""""2.2 Single-Variable Deterministic Optimization"" ""2.3 Continuity and Convexity"" ""2.4 Unconstrained Optimization"" ""2.5 Equality-Constrained Optimization"" ""2.5.1 Method of Lagrange Multipliers"" ""2.5.2 Generalized Reduced Gradient Method"" ""2.6 Equality- and Inequality-Constrained Optimization"" ""2.6.1 Active Set Strategy"" ""2.7 Software for Deterministic Optimization"" ""References"" ""Chapter 3: Stochastic Optimization"" ""3.1 Introduction to Stochastic Optimization"" ""3.2 Stochastic Optimization vs. Deterministic Optimization"" ""3.3 Stochastic Optimization with Constraints""""3.4 Genetic Algorithms"" ""3.5 Differential Evolution"" ""3.6 Tabu Search"" ""3.7 Simulated Annealing"" ""3.8 Other Methods"" ""3.8.1 Ant Colony Optimization"" ""3.8.2 Particle Swarm Optimization"" ""3.8.3 Harmony Search"" ""References"" ""Chapter 4: The Simulator Aspen Plus®"" ""4.1 Importance of Software for Process Analysis"" ""4.2 Characteristics of the Process Simulator Aspen Plus"" ""4.3 Sequential Modular Simulation"" ""References"" ""Chapter 5: Direct Optimization in Aspen Plus®"" ""5.1 Optimization Methods"" ""5.2 Sensitivity Analysis Tools in Aspen Plus""""5.3 Sequential Quadratic Programming in Aspen Plus"" ""5.4 Optimization of a Heat Exchanger"" ""5.4.1 Description of the Problem"" ""5.4.2 Initial Simulation"" ""5.4.3 Optimization through Sensitivity Analysis"" ""5.4.4 Optimization through Sequential Quadratic Programming"" ""5.5 Optimization of a Flash Drum"" ""5.5.1 Description of the Problem"" ""5.5.2 Initial Simulation"" ""5.5.3 Optimization through Sensitivity Analysis"" ""5.5.4 Optimization through Sequential Quadratic Programming"" ""5.6 Optimization of a Tubular Reactor"" ""5.6.1 Description of the Problem""""5.6.2 Initial Simulation"" ""5.6.3 Optimization through Sensitivity Analysis"" ""5.6.4 Optimization through Sequential Quadratic Programming"" ""References"" ""Chapter 6: Optimization using Aspen Plus® and Stochastic Toolbox*"" ""6.1 Introduction"" ""6.2 Software for Stochastic Optimization"" ""6.3 Linking Aspen Plus with the Stochastic Optimization Software"" ""6.3.1 Creating a Function to be Optimized with MATLAB"" ""6.3.2 Creating a Subroutine in Microsoft Excel"" ""6.4 Mono-Objective Optimization of a Multicomponent Distillation Column"" "Stochastic Process Optimization using Aspen® PlusBookshop Category: Chemical EngineeringOptimization can be simply defined as "choosing the best alternative among a set of feasible options". In all the engineering areas, optimization has a wide range of applications, due to the high number of decisions involved in an engineering environment. Chemical engineering, and particularly process engineering, is not an exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models. In this book, the combined use of the modular simulator Aspen® Plus and stochastic optimization methods, codified in MATLAB, is presented. Some basic concepts of optimization are first presented, then, strategies to use the simulator linked with the optimization algorithm are shown. Finally, examples of application for process engineering are discussed. The reader will learn how to link the process simulator Aspen® Plus and stochastic optimization algorithms to solve process design problems. They will gain ability to perform multi-objective optimization in several case studies. Key Features:" The book links simulation and optimization through numerical analyses and stochastic optimization techniques " Includes use of examples to illustrate the application of the concepts and specific guidance on the use of software (Aspen® Plus, Excel, MATLB) to set up and solve models representing complex problems." Illustrates several examples of applications for the linking of simulation and optimization software with other packages for optimization purposes." Provides specific information on how to implement stochastic optimization with process simulators." Enable readers to identify practical and economic solutions to problems of industrial relevance, enhancing the safety, operation, environmental, and economic performance of chemical processes."--Provided by publisher Stochastic Process Optimization using Aspenr Plus Bookshop Category: Chemical Engineering Optimization can be simply defined as "choosing the best alternative among a set of feasible options". In all the engineering areas, optimization has a wide range of applications, due to the high number of decisions involved in an engineering environment. Chemical engineering, and particularly process engineering, is not an exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models. In this book, the combined use of the modular simulator Aspenr Plus and stochastic optimization methods, codified in MATLAB, is presented. Some basic concepts of optimization are first presented, then, strategies to use the simulator linked with the optimization algorithm are shown. Finally, examples of application for process engineering are discussed. The reader will learn how to link the process simulator Aspenr Plus and stochastic optimization algorithms to solve process design problems. They will gain ability to perform multi-objective optimization in several case studies. Key Features: The book links simulation and optimization through numerical analyses and stochastic optimization techniques Includes use of examples to illustrate the application of the concepts and specific guidance on the use of software (Aspenr Plus, Excel, MATLB) to set up and solve models representing complex problems. Illustrates several examples of applications for the linking of simulation and optimization software with other packages for optimization purposes. Provides specific information on how to implement stochastic optimization with process simulators. Enable readers to identify practical and economic solutions to problems of industrial relevance, enhancing the safety, operation, environmental, and economic performance of chemical processes "Cover"--"Half title"--"Title page"--"Copyright page" -- "Dedication" -- "Contents" -- "Preface" -- "Acknowledgment" -- "Editors" -- "Contributors" -- "Chapter 1: Introduction to Optimization" -- "1.1 What Is Optimization?" -- "1.2 Mathematical Modeling and Optimization" -- "1.3 Classification of Optimization Problems" -- "1.4 Objective Function" -- "1.5 Optimization with Constraints: Feasible Region" -- "1.6 Multiobjective Optimization" -- "1.6.1 Weighted Sum Method" -- "1.6.2 Constraint Method" -- "1.7 Process Optimization" -- "References" -- "Chapter 2: Deterministic Optimization" -- "2.1 Introduction" -- "2.2 Single-Variable Deterministic Optimization" -- "2.3 Continuity and Convexity" -- "2.4 Unconstrained Optimization" -- "2.5 Equality-Constrained Optimization" -- "2.5.1 Method of Lagrange Multipliers" -- "2.5.2 Generalized Reduced Gradient Method" -- "2.6 Equality- and Inequality-Constrained Optimization" -- "2.6.1 Active Set Strategy" -- "2.7 Software for Deterministic Optimization" -- "References" -- "Chapter 3: Stochastic Optimization" -- "3.1 Introduction to Stochastic Optimization" -- "3.2 Stochastic Optimization vs. Deterministic Optimization" -- "3.3 Stochastic Optimization with Constraints" -- "3.4 Genetic Algorithms" -- "3.5 Differential Evolution" -- "3.6 Tabu Search" -- "3.7 Simulated Annealing" -- "3.8 Other Methods" -- "3.8.1 Ant Colony Optimization" -- "3.8.2 Particle Swarm Optimization" -- "3.8.3 Harmony Search" -- "References" -- "Chapter 4: The Simulator Aspen Plus®" -- "4.1 Importance of Software for Process Analysis" -- "4.2 Characteristics of the Process Simulator Aspen Plus" -- "4.3 Sequential Modular Simulation" -- "References" -- "Chapter 5: Direct Optimization in Aspen Plus®" -- "5.1 Optimization Methods" -- "5.2 Sensitivity Analysis Tools in Aspen Plus This book present methodologies for solving optimization problems in the area of process design, using the simulator Aspen Plus as the solver of the model, linked with stochastic optimization techniques formulated in Excel or MATLAB language. It covers basic concepts of optimization and strategies to use the simulator with application examples.