Multi-Objective Optimization in Chemical Engineering
Developments and Applications
For reasons both financial and environmental, there is a perpetual need to optimize the design and operating conditions of industrial process systems in order to improve their performance, energy efficiency, profitability, safety and reliability. However, with most chemical engineering application problems having many variables with complex inter-relationships, meeting these optimization objectives can be challenging. This is where Multi-Objective Optimization (MOO) is useful to find the optimal trade-offs among two or more conflicting objectives. This book provides an overview of the recent developments and applications of MOO for modeling,…
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Produktdetails
Weitere Autoren: Bonilla-Petriciolet, Adrian (Hrsg.)
- ISBN: 978-1-118-34169-8
- EAN: 9781118341698
- Produktnummer: 16365989
- Verlag: Wiley
- Sprache: Englisch
- Erscheinungsjahr: 2013
- Seitenangabe: 528 S.
- Plattform: PDF
- Masse: 17'636 KB
Über den Autor
Gade Pandu Rangaiah has been with the National University of Singapore since 1982, and is Professor in the Department of Chemical & Biomolecular Engineering. His research interests are in control, modeling and optimization of chemical, petrochemical and related processes. Prof. Rangaiah published 130 papers in international journals and presented around 100 papers in conferences. He has received several awards for his teaching including Annual Teaching Excellence Awards from NUS for four consecutive years. He has edited two books for World Scientific, and has one book in production with Wiley. Adrian Bonilla-Petriciolet has been with the Instituto Tecnologico de Aguascalientes, Mexico, since 2001 and is currently Professor in the Department of Chemical Engineering and Head of Research Programs. His research interests include stochastic global optimization, applied thermodynamics, modeling and optimization of chemical processes. He has published more than 30 papers in international journals and refereed conference proceedings in the broad areas of process modeling and optimization.
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