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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.
Chapter : Preliminary Concepts and Definitions
An Introductory Example
Commonly Encountered Problems in Optimization
Optimization of Functions of a Single Variable
Convex Functions
Applications
The Numerical Solution of Single Variable Optimization Problems: Newton´s Method
Learning Summary
Terms and Concepts
Problems
Chapter : Multidimensional Unconstrained Optimization
From Single Variable to Multivariable Optimization
Algorithms for Multivariable Unconstrained Optimization
Application Examples
Parameter Estimation: Nonlinear Least Squares
Learning Summary
Terms and Concepts
Problems
Chapter : Constrained Optimization
Introduction to Constrained Optimization
Equality Constrained Problems
Application Examples
Inequality Constrained Problems
General Nonlinear Programming Problems
Numerical Solution of Nonlinear Programming Problems
Application Examples
Learning Summary
Terms and Concepts
Problems
Chapter : Linear Programming
Introduction to Linear Programming
Examples of LP Formulations from the Chemical Industry
Graphical Solution of Linear Programming Problems
The Simplex Method: Basic Definitions and Steps
Solving LP Problems in MATLAB
Classical LP Formulations
Interior Point Methods for Solving LP Problems
Learning Summary
Terms and Concepts
Problems
Chapter : Integer and Mixed Integer Programming Problems
Examples of Integer Programming Formulations
Solving Integer Programming Problems Using the Branch and Bound Method
Solving MILP Problems in MATLAB
Solving MINLP Problems Using the B&ampB and Outer Approximation
Learning Summary
Terms and Concepts
Problems
Chapter : Solving Optimization Problems in GAMS
Elements of a GAMS Model
Two Recreational Problems Solved in GAMS
Learning Summary
Terms and Concepts
Problems
Chapter : Representative Optimization Problems in Chemical Engineering Solved in GAMS
Optimization of a Multiple-Effect Evaporation System
Complex Chemical Reaction Equilibrium
Optimal Design of a Methanol-Water Distillation Column
A Representative Optimal Control Problem
Optimal Design of a Renewable Energy Production System
Metabolic Flux Analysis
Optimal Design of Proportional-Integral-Derivative (PID) Controllers
The Control Structure Selection Problem
Learning Summary
Terms and Concepts
Problems
Appendix A: Introduction to MATLAB
Controlling the Flow
Vectorization
Basic Numerical Calculations in MATLAB
Literature and Notes for Further Study