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Nonlinear Optimization Strategies for Process Separations and Process Intensification
Author(s) -
Biegler Lorenz T.
Publication year - 2020
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.202000014
Subject(s) - process optimization , process (computing) , computer science , nonlinear system , exploit , nonlinear programming , process systems , distillation , mathematical optimization , engineering optimization , process modeling , complementarity (molecular biology) , optimization problem , process engineering , engineering , mathematics , algorithm , chemistry , physics , computer security , organic chemistry , quantum mechanics , environmental engineering , biology , genetics , operating system
Advanced nonlinear programming (NLP) strategies based on equation‐oriented (EO) process models are leading to significant improvements in computer‐aided process engineering. The EO paradigm allows the development of large, integrated optimization platforms that expand the scope of continuous optimization tasks in process engineering. In particular, these platforms deploy significantly faster NLP strategies than in commercial simulation tools. Moreover, they exploit exact derivatives and system structure in order to consider much larger and more challenging systems. Finally, they allow the incorporation of much more general models, such as multi‐level optimization and complementarity constraints. For process optimization this allows the treatment of extended models for complex phase equilibrium and process separations. These advances facilitate the optimization of novel integrated systems that arise in process intensification. Several separation case studies are presented that illustrate these optimization concepts and demonstrate their effectiveness for hybrid membrane/distillation separations and reactive distillation systems that typify novel systems in process intensification.