Optimization for Chemical and Biochemical Engineering
Optimization for Chemical and Biochemical Engineering
Walter Kähm
Ehecatl Antonio del Rio Chanona, Imperial College London
Ye Yuan, Huazhong University of Science and Technology
£73.99
GBPDiscover the subject of optimization in a new light with this modern and unique treatment. Includes a thorough exposition of applications and algorithms in sufficient detail for practical use, while providing you with all the necessary background in a self-contained manner. Features a deeper consideration of optimal control, global optimization, optimization under uncertainty, multiobjective optimization, mixed-integer programming and model predictive control. Presents a complete coverage of formulations and instances in modelling where optimization can be applied for quantitative decision-making. As a thorough grounding to the subject, covering everything from basic to advanced concepts and addressing real-life problems faced by modern industry, this is a perfect tool for advanced undergraduate and graduate courses in chemical and biochemical engineering.
- Includes exercises within the chapters
- Provides several case studies relevant to Chemical and Biochemical Engineering
- Presents theory through motivating practical examples
Reviews & endorsements
'This book offers a very clear, uncluttered presentation of key ideas of optimisation in rigorous form and with plenty of examples from a decade of research and educational experience. It offers an exceptional resource for educators and students of optimisation methods, as well as a valuable reference text to practitioners.' Alexei Lapkin, University of Cambridge
'This excellent book brings together important and up-to-date elements of the theory and practice of optimisation with application to chemical and biochemical engineering. It's an ideal reference for students on advanced courses or for researchers in the field.' Nilay Shah, Imperial College
Product details
January 2021Hardback
9781107106833
350 pages
250 × 173 × 23 mm
0.73kg
Available
Table of Contents
- Part I. Overview of Optimization:
- 1. Introduction to optimization
- Part II. From General Mathematical Background to General Nonlinear Programming Problems (NLP):
- 2. General concepts
- 3. Convexity
- 4. Quadratic functions
- 5. Minimization in one dimension
- 6. Unconstrained multivariate gradient-based minimization
- 7. Constrained nonlinear programming problems (NLP)
- 8. Penalty and barrier function methods
- 9. Interior point methods (IPMs), a detailed analysis
- Part III. Formulation and Solution of Linear Programming (LP) Problem Models:
- 10. Introduction to LP models
- 11. Numerical solution of LP problems using the simplex method
- 12. A sampler of LP problem formulations
- 13. Regression revisited, using LP to fit linear models
- 14. Network flow problems
- 15, LP and sensitivity analysis, in brief
- Part IV. Further Topics in Optimization:
- 16. Multiobjective optimilzation problem (MOP)
- 17. Stochastic optimization problem (SOP)
- 18. Mixed integer programming
- 19. Global optimization
- 20. Optical control problems (dynamic optimization)
- 21. System identification and model predictive control.
