Optimization for Industrial Problems

By: Patrick Bangert

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Paperback | 5 January 2012

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Paperback
270 Pages

Dimensions(cm)
22.86 x 15.24 x 1.91

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Industrial optimization lies on the crossroads between mathematics, computer science, engineering and management. This book presents these fields in interdependence as a conversation between theoretical aspects of mathematics and computer science and the mathematical field of optimization theory at a practical level. The 19 case studies that were conducted by the author in real enterprises in cooperation and co-authorship with some of the leading industrial enterprises, including RWE, Vattenfall, EDF, PetroChina, Vestolit, Sasol, and Hella, illustrate the results that may be reasonably expected from an optimization project in a commercial enterprise. The book is aimed at persons working in industrial facilities as managers or engineers; it is also suitable for university students and their professors as an illustration of how the academic material may be used in real life. It will not make its reader a mathematician but it will help its reader in improving his plant.

Industry Reviews

From the reviews:

"This book focusses on optimal control of industrial systems that reflect real-life problems with a 'practical flavor'. ... it is aimed at managers, professionals, practitioners or engineers of industrial facilities. It also gives a good window to real-world industrial systems for those working in academic institutions. This reviewer enjoyed reading the book and it is an excellent addition to the field ... ." (D. Subbaram Naidu, Amazon.com, April, 2014)

"In Optimization for Industrial Problems, Patrick Bangert describes the tools and techniques he and his coauthors have used to address difficult industrial challenges. The book has an impressive set of 19 case studies that successfully demonstrate the role of advanced analytics in optimization. ... Managers of heavy industrial equipment, who have a reasonable understanding of statistics, could also use this book to identify opportunities for using advanced data analysis and statistical models to improve plant performance." (Kenneth Chelst, Interfaces, Vol. 43 (5), September-October, 2013)

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