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E-raamat: Practical Chemical Process Optimization: With MATLAB(R) and GAMS(R)

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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.


1 Preliminary Concepts and Definitions
1(52)
1.1 An Introductory Example
1(3)
1.2 Commonly Encountered Problems in Optimization
4(1)
1.3 Optimization of Functions of a Single Variable
5(6)
1.4 Convex Functions
11(2)
1.5 Applications
13(17)
1.6 The Numerical Solution of Single Variable Optimization Problems: Newton's Method
30(23)
Learning Summary
36(2)
Terms and Concepts
38(1)
Problems
38(15)
2 Multidimensional Unconstrained Optimization
53(48)
2.1 From Single Variable to Multivariable Optimization
53(9)
2.2 Algorithms for Multivariable Unconstrained Optimization
62(9)
2.3 Application Examples
71(13)
2.4 Parameter Estimation: Nonlinear Least Squares
84(17)
Learning Summary
91(2)
Terms and Concepts
93(1)
Problems
93(8)
3 Constrained Optimization
101(64)
3.1 Introduction to Constrained Optimization
101(1)
3.2 Equality Constrained Problems
102(5)
3.3 Application Examples
107(7)
3.4 Inequality Constrained Problems
114(7)
3.5 General Nonlinear Programming Problems
121(3)
3.6 Numerical Solution of Nonlinear Programming Problems
124(2)
3.7 Application Examples
126(39)
Learning Summary
156(1)
Terms and Concepts
157(1)
Problems
157(8)
4 Linear Programming
165(54)
4.1 Introduction to Linear Programming
165(1)
4.2 Examples of LP Formulations from the Chemical Industry
166(6)
4.3 Graphical Solution of Linear Programming Problems
172(4)
4.4 The Simplex Method: Basic Definitions and Steps
176(6)
4.5 Solving LP Problems in MATLAB®
182(5)
4.6 Classical LP Formulations
187(23)
4.7 Interior Point Methods for Solving LP Problems
210(9)
Learning Summary
213(1)
Terms and Concepts
214(1)
Problems
214(5)
5 Integer and Mixed Integer Programming Problems
219(54)
5.1 Introduction
219(1)
5.2 Examples of Integer Programming Formulations
219(19)
5.3 Solving Integer Programming Problems Using the Branch and Bound Method
238(10)
5.4 Solving MILP Problems in MATLAB®
248(3)
5.5 Solving MINLP Problems Using the B&B and Outer Approximation
251(22)
Learning Summary
264(1)
Terms and Concepts
265(1)
Problems
265(8)
6 Solving Optimization Problems in GAMS®
273(34)
6.1 Introduction
273(1)
6.2 Elements of a GAMS® Model
274(15)
6.3 Two Recreational Problems Solved in GAMS®
289(18)
Learning Summary
302(1)
Terms and Concepts
302(1)
Problems
302(5)
7 Representative Optimization Problems in Chemical Engineering Solved in GAMS®
307(88)
7.1 Introduction
307(1)
7.2 Optimization of a Multiple-Effect Evaporation System
307(6)
7.3 Complex Chemical Reaction Equilibrium
313(4)
7.4 Optimal Design of a Methanol-Water Distillation Column
317(8)
7.5 A Representative Optimal Control Problem
325(14)
7.6 Optimal Design of a Renewable Energy Production System
339(16)
7.7 Metabolic Flux Analysis
355(7)
7.8 Optimal Design of Proportional-Integral-Derivative (PID) Controllers
362(23)
7.9 The Control Structure Selection Problem
385(10)
Learning Summary
388(1)
Terms and Concepts
388(2)
Problems
390(5)
Appendix A Introduction to MATLAB®
395(40)
Introduction
395(16)
Controlling the Flow
411(5)
Vectorization
416(2)
Basic Numerical Calculations in MATLAB®
418(13)
Literature and Notes for Further Study
431(4)
Index 435
Ioannis K. Kookos is Professor in the Department of Chemical Engineering at the University of Patras. His research interests include Synthesis and optimization of biotechnological processes for the production of platform chemicals, modelling of mass transfer in SOFCs, plantwide process design and control structure selection, and synthesis, design and optimization of biofuel production.