Advanced Optimization for Process Systems Engineering

A unique text covering both basic and advanced concepts of optimization theory and methods for process systems engineers.

Ignacio E. Grossmann (Author)

9781108831659, Cambridge University Press

Hardback, published 25 March 2021

102 pages
25.2 x 19.4 x 1.4 cm, 0.6 kg

'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

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.

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.

Subject Areas: Chemical engineering [TDCB], Industrial chemistry [TDC], Technology, engineering, agriculture [T], Optimization [PBU], Mathematics [PB], Mathematics & science [P]