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Surrogate‐based superstructure optimization framework
Author(s) -
Henao Carlos A.,
Maravelias Christos T.
Publication year - 2011
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.12341
Subject(s) - superstructure , surrogate model , computer science , process (computing) , mathematical optimization , integer programming , unit operation , selection (genetic algorithm) , nonlinear programming , unit (ring theory) , nonlinear system , engineering , algorithm , machine learning , mathematics , physics , structural engineering , quantum mechanics , chemical engineering , operating system , mathematics education
In principle, optimization‐based “superstructure” methods for process synthesis can be more powerful than sequential‐conceptual methods as they account for all complex interactions between design decisions. However, these methods have not been widely adopted because they lead to mixed‐integer nonlinear programs that are hard to solve, especially when realistic unit operation models are used. To address this challenge, we develop a superstructure‐based strategy where complex unit models are replaced with surrogate models built from data generated via commercial process simulators. In developing this strategy, we study aspects such as the systematic design of process unit surrogate models, the generation of simulation data, the selection of the surrogate's structure, and the required model fitting. We also present how these models can be reformulated and incorporated into mathematical programming superstructure formulations. Finally, we discuss the application of the proposed strategy to a number of applications. © 2010 American Institute of Chemical Engineers AIChE J, 2011