This book addresses modern nonlinear programming concepts and algorithms, especially as they apply to challenging applications in chemical process engineering. It relates the material to real-world problem classes in process optimisation, thus bridging the gap between the mathematical material and the practical uses. Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes shows readers which methods are best suited for specific applications, how large-scale problems should be formulated and what features of these problems should be emphasised, and how existing NLP methods can be extended to exploit specific structures of large-scale optimisation models. The book serves a dual function: it will be useful to chemical engineers who wish to understand and use nonlinear programming; it will also be of interest to experts in mathematical optimisation who want to understand process engineering problems and develop better approaches to solving them.

This book addresses modern nonlinear programming concepts and algorithms, especially as they apply to challenging applications in chemical process engineering. It relates the material to real-world problem classes in process optimization, thus making the material understandable and useful to chemical engineers and experts in mathematical optimization.

Preface; 1. Introduction to process optimization; 2. Concepts of unconstrained optimization; 3. Newton-type methods for unconstrained optimization; 4. Concepts of constrained optimization; 5. Newton methods for equality constrained optimization; 6. Numerical algorithms for constrained optimization; 7. Steady state process optimization; 8. Introduction to dynamic process optimization; 9. Dynamic optimization methods with embedded DAE solvers; 10. Simultaneous methods for dynamic optimization; 11. Process optimization with complementarity constraints; Bibliography; Index.