This book addresses modern nonlinear programming (NLP) concepts and algorithms, especially as they apply to challenging applications in chemical process engineering. The author provides a firm grounding in fundamental NLP properties and algorithms, and relates them to real-world problem classes in process optimization, thus making the material understandable and useful to chemical engineers and experts in mathematical optimization.
Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes shows readers
• which NLP methods are best suited for specific applications,
• how large-scale problems should be formulated and what features of these problems should be emphasized, and
• how existing NLP methods can be extended to exploit specific structures of large-scale optimization models.
The book is intended for chemical engineers interested in using NLP algorithms for specific applications, experts in mathematical optimization who want to understand process engineering problems and develop better approaches to solving them, and researchers from both fields interested in developing better methods and problem formulations for challenging engineering problems.
About the Authors
Lorenz T. Biegler is the Bayer Professor of Chemical Engineering at Carnegie Mellon University and a Fellow of the American Institute of Chemical Engineers. He has authored or coauthored over 200 journal articles and 2 books. His research interests lie in the field of computer-aided process engineering, including flowsheet optimization; optimization of systems of differential and algebraic equations; reactor network synthesis; and algorithms for constrained, nonlinear process control.
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