Del i serien Cambridge Series in Chemical Engineering

Optimization for Chemical and Biochemical Engineering

Theory, Algorithms, Modeling and Applications

Inbunden, Engelska, 2021

AvVassilios S. Vassiliadis,Walter Kähm,Ehecatl Antonio del Rio Chanona,Ye Yuan

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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.

Produktinformation

Utgivningsdatum2021-01-14
Mått173 x 250 x 23 mm
Vikt740 g
FormatInbunden
SpråkEngelska
SerieCambridge Series in Chemical Engineering
Antal sidor350
FörlagCambridge University Press
ISBN9781107106833

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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, a former PhD student under Vassilios S. Vassiliadis, is a process engineer in the chemical sector. 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 is currently a professor at Huazhong University of Science and Technology.

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.