Discover 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
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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
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Product details
- Date Published: March 2021
- format: Hardback
- isbn: 9781107106833
- length: 350 pages
- dimensions: 250 x 173 x 23 mm
- weight: 0.73kg
- availability: 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.
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Authors
Vassilios S. Vassiliadis, University of Cambridge
Vassilios S. Vassiliadis is a Senior Lecturer in the Department of Chemical Engineering at the University of Cambridge. He is also the CEO and CTO of the spin-out company, Cambridge Simulation Solutions LTD.
Walter Kähm
Walter Kähm, a former PhD student under Vassilios S. Vassiliadis, is a process engineer in the chemical sector.
Ehecatl Antonio del Rio Chanona, Imperial College London
Ehecatl Antonio del Rio-Chanona is a lecturer and head of the optimization and machine learning for the process systems engineering group in the Department of Chemical Engineering and the Centre for Process Systems Engineering (CPSE) at Imperial College London.
Ye Yuan, Huazhong University of Science and Technology
Ye Yuan is currently a professor at Huazhong University of Science and Technology.