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Full Description
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.
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.
