Advanced Optimization for Process Systems Engineering
CAD$120.95 (X)
Part of Cambridge Series in Chemical Engineering
- Author: Ignacio E. Grossmann, Carnegie Mellon University, Pennsylvania
- Date Published: May 2021
- availability: In stock
- format: Hardback
- isbn: 9781108831659
CAD$
120.95
(X)
Hardback
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Based on the author's forty years of teaching experience, this unique textbook covers both basic and advanced concepts of optimization theory and methods for process systems engineers. Topics covered include continuous, discrete and logic optimization (linear, nonlinear, mixed-integer and generalized disjunctive programming), optimization under uncertainty (stochastic programming and flexibility analysis), and decomposition techniques (Lagrangean and Benders decomposition). Assuming only a basic background in calculus and linear algebra, it enables easy understanding of mathematical reasoning, and numerous examples throughout illustrate key concepts and algorithms. End-of-chapter exercises involving theoretical derivations and small numerical problems, as well as in modeling systems like GAMS, enhance understanding and help put knowledge into practice. Accompanied by two appendices containing web links to modeling systems and models related to applications in PSE, this is an essential text for single-semester, graduate courses in process systems engineering in departments of chemical engineering.
Read more- Covers both basic and advanced concepts of optimization theory and methods, ideal for single semester courses
- Mathematically accessible, requiring only a basic background in calculus and linear algebra
- Provides numerous end-of-chapter exercises involving theoretical derivations and small numerical problems, as well as in modeling systems like GAMS. A full solutions manual accompanies the text
Reviews & endorsements
‘Authored by Ignacio Grossmann, the creator and key developer of the field of mixed integer nonlinear programming, this outstanding textbook provides a thorough and comprehensive treatment of fundamental concepts, optimization models and effective solution strategies for discrete and continuous optimization. It is an essential, 'must-have' reference for all students, researchers and practitioners in process systems engineering.’ Lorenz Biegler, Carnegie Mellon University
See more reviews'From the globally recognized leading authority in the field of process systems engineering, this long-awaited book will definitely become the standard reference for anyone interested in optimization. It is very well thought and written, with excellent presentation of the material. The theory is described in a very effective, rigorous, and clear way, with appropriate explanations and examples used throughout, covering traditional topics such as linear and nonlinear optimization concepts and mixed-integer linear programming, along with more advanced topics, such as disjunctive programming, global optimization, and stochastic programming. A real gem and a must read!' Stratos Pistikopoulos, Texas A & M University
‘Excellent coverage of the basic concepts and approaches developed in the area of process systems engineering in the last forty years. A unique book that can be easily adapted to advanced undergraduate and graduate-level classes to provide overall guidance to different tools that can be used to model and optimize complex engineering problems. I am certainly looking forward to using it in my class on mathematical modeling and optimization principles.’ Marianthi Ierapetritou, University of Delaware
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×Product details
- Date Published: May 2021
- format: Hardback
- isbn: 9781108831659
- length: 102 pages
- dimensions: 252 x 194 x 14 mm
- weight: 0.6kg
- availability: In stock
Table of Contents
Preface
1. Optimization in process systems engineering
2. Solving nonlinear equations
3. Basic theoretical concepts in optimization
4. Nonlinear programming algorithms
5. Linear programming
6. Mixed-integer programming models
7. Systematic modeling of constraints with logic
8. Mixed-integer linear programming
9 Mixed-integer nonlinear programming
10. Generalized disjunctive programming
11. Constraint programming
12. Nonconvex optimization
13. Lagrangean decomposition
14. Stochastic programming
15. Flexibility analysis
Appendix A. Modeling systems and optimization software
Appendix B. Optimization models for process systems engineering
References
Index.
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