Optimization for Chemical and Biochemical Engineering
Theory, Algorithms, Modeling and Applications

Modern and unique treatment of process optimization which presents an exposition of applications and algorithms in detail for practical use.

Vassilios S. Vassiliadis (Author), Walter Kähm (Author), Ehecatl Antonio del Rio Chanona (Author), Ye Yuan (Author)

9781107106833, Cambridge University Press

Hardback, published 14 January 2021

350 pages
25 x 17.3 x 2.3 cm, 0.73 kg

'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

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.

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.

Subject Areas: Chemical engineering [TDCB], Biochemical engineering [TC], Mathematical modelling [PBWH], Optimization [PBU]