Vassilios S. Vassiliadis (University of Cambridge), Walter Kahm, Ehecatl Antonio del Rio Chanona (Imperial College London)

Optimization for Chemical and Biochemical Engineering

Theory, Algorithms, Modeling and Applications

• Gebundenes Buch

Produktbeschreibung

A modern and unique treatment of process optimization which presents a thorough exposition of applications and algorithms in sufficient detail for practical use. A perfect tool for advanced undergraduate and graduate courses in chemical and biochemical engineering.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Cambridge Series in Chemical Engineering
• Verlag: Cambridge University Press
• Seitenzahl: 350
• Erscheinungstermin: 14. Januar 2021
• Englisch
• Abmessung: 175mm x 251mm x 27mm
• Gewicht: 732g
• ISBN-13: 9781107106833
• ISBN-10: 1107106834
• Artikelnr.: 59392352

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.