Advanced Optimization for Process Systems Engineering
eBook - PDF

Advanced Optimization for Process Systems Engineering

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Advanced Optimization for Process Systems Engineering

About this book

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.

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Yes, you can access Advanced Optimization for Process Systems Engineering by Ignacio E. Grossmann in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Chemical & Biochemical Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-title
  3. Series information
  4. Title page
  5. Copyright information
  6. Dedication
  7. Contents
  8. Preface
  9. 1 Optimization in Process Systems Engineering
  10. 2 Solving Nonlinear Equations
  11. 3 Basic Theoretical Concepts in Optimization
  12. 4 Nonlinear Programming Algorithms
  13. 5 Linear Programming
  14. 6 Mixed-Integer Programming Models
  15. 7 Systematic Modeling of Constraints with Logic
  16. 8 Mixed-Integer Linear Programming
  17. 9 Mixed-Integer Nonlinear Programming
  18. 10 Generalized Disjunctive Programming
  19. 11 Constraint Programming
  20. 12 Nonconvex Optimization
  21. 13 Lagrangean Decomposition
  22. 14 Stochastic Programming
  23. 15 Flexibility Analysis
  24. Appendix A Modeling Systems and Optimization Software
  25. Appendix B Optimization Models for Process Systems Engineering
  26. References
  27. Index