Vassilios S. Vassiliadis

Optimization for Chemical and Biochemical Engineering (eBook, ePUB)

Theory, Algorithms, Modeling and Applications

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 14. Januar 2021
• Englisch
• ISBN-13: 9781108916127
• Artikelnr.: 62747195

Autorenporträt

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.

Inhaltsangabe

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.