This text provides the undergraduate chemical engineering student with the necessary tools for problem solving in chemical or bio-engineering processes. In a friendly, simple, and unified framework, the exposition aptly balances theory and practice. It uses minimal mathematical concepts, terms, algorithms, and describes the main aspects of chemical process optimization using MATLAB and GAMS. Numerous examples and case studies are designed for students to understand basic principles of each optimization method and elicit the immediate discovery of practical applications. Problem sets are directly tied to real-world situations most commonly encountered in chemical engineering applications. Chapters are structured with handy learning summaries, terms and concepts, and problem sets, and individually reinforce the basics of particular optimization methods. Additionally, the wide breadth of topics that may be encountered in courses such as Chemical Process Optimization, Chemical Process Engineering, Optimization of Chemical Processes, are covered in this accessible text. The book provides formal introductions to MATLAB, GAMS, and a revisit to pertinent aspects of undergraduate calculus. While created for coursework, this text is also suitable for independent study. A full solutions manual is available to instructors who adopt the text for their course.
Author(s): Ioannis K. Kookos
Series: Springer Optimization and Its Applications, 197
Publisher: Springer
Year: 2022
Language: English
Pages: 443
City: Cham
Preface
Contents
Chapter 1: Preliminary Concepts and Definitions
1.1 An Introductory Example
1.2 Commonly Encountered Problems in Optimization
1.3 Optimization of Functions of a Single Variable
1.4 Convex Functions
1.5 Applications
1.6 The Numerical Solution of Single Variable Optimization Problems: Newton´s Method
Learning Summary
Terms and Concepts
Problems
Chapter 2: Multidimensional Unconstrained Optimization
2.1 From Single Variable to Multivariable Optimization
2.2 Algorithms for Multivariable Unconstrained Optimization
2.3 Application Examples
2.4 Parameter Estimation: Nonlinear Least Squares
Learning Summary
Terms and Concepts
Problems
Chapter 3: Constrained Optimization
3.1 Introduction to Constrained Optimization
3.2 Equality Constrained Problems
3.3 Application Examples
3.4 Inequality Constrained Problems
3.5 General Nonlinear Programming Problems
3.6 Numerical Solution of Nonlinear Programming Problems
3.7 Application Examples
Learning Summary
Terms and Concepts
Problems
Chapter 4: Linear Programming
4.1 Introduction to Linear Programming
4.2 Examples of LP Formulations from the Chemical Industry
4.3 Graphical Solution of Linear Programming Problems
4.4 The Simplex Method: Basic Definitions and Steps
4.5 Solving LP Problems in MATLAB
4.6 Classical LP Formulations
4.7 Interior Point Methods for Solving LP Problems
Learning Summary
Terms and Concepts
Problems
Chapter 5: Integer and Mixed Integer Programming Problems
5.1 Introduction
5.2 Examples of Integer Programming Formulations
5.3 Solving Integer Programming Problems Using the Branch and Bound Method
5.4 Solving MILP Problems in MATLAB
5.5 Solving MINLP Problems Using the B&B and Outer Approximation
Learning Summary
Terms and Concepts
Problems
Chapter 6: Solving Optimization Problems in GAMS
6.1 Introduction
6.2 Elements of a GAMS Model
6.3 Two Recreational Problems Solved in GAMS
Learning Summary
Terms and Concepts
Problems
Chapter 7: Representative Optimization Problems in Chemical Engineering Solved in GAMS
7.1 Introduction
7.2 Optimization of a Multiple-Effect Evaporation System
7.3 Complex Chemical Reaction Equilibrium
7.4 Optimal Design of a Methanol-Water Distillation Column
7.5 A Representative Optimal Control Problem
7.6 Optimal Design of a Renewable Energy Production System
7.7 Metabolic Flux Analysis
7.8 Optimal Design of Proportional-Integral-Derivative (PID) Controllers
7.9 The Control Structure Selection Problem
Learning Summary
Terms and Concepts
Problems
Appendix A: Introduction to MATLAB
Introduction
Controlling the Flow
Vectorization
Basic Numerical Calculations in MATLAB
Literature and Notes for Further Study
Index
