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A trust region filter method for glass box/black box optimization
Author(s) -
Eason John P.,
Biegler Lorenz T.
Publication year - 2016
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15325
Subject(s) - black box , trust region , white box , process (computing) , convergence (economics) , computer science , s box , mathematical optimization , nonlinear programming , filter (signal processing) , process optimization , optimization problem , algorithm , nonlinear system , mathematics , engineering , artificial intelligence , machine learning , physics , block cipher , computer security , quantum mechanics , cryptography , economics , radius , computer vision , economic growth , operating system , environmental engineering
Modern nonlinear programming solvers can be utilized to solve very large scale problems in chemical engineering. However, these methods require fully open models with accurate derivatives. In this article, we address the hybrid glass box/black box optimization problem, in which part of a system is modeled with open, equation based models and part is black box. When equation based reduced models are used in place of the black box, NLP solvers may be applied directly but an accurate solution is not guaranteed. In this work, a trust region filter algorithm for glass box/black box optimization is presented. By combining concepts from trust region filter methods and derivative free optimization, the method guarantees convergence to first‐order critical points of the original glass box/black box problem. The algorithm is demonstrated on three comprehensive examples in chemical process optimization. © 2016 American Institute of Chemical Engineers AIChE J , 62: 3124–3136, 2016