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A novel feasibility analysis method for black‐box processes using a radial basis function adaptive sampling approach
Author(s) -
Wang Zilong,
Ierapetritou Marianthi
Publication year - 2017
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.15362
Subject(s) - kriging , radial basis function , black box , surrogate model , adaptive sampling , sampling (signal processing) , process (computing) , computer science , basis (linear algebra) , mathematical optimization , function (biology) , algorithm , data mining , mathematics , artificial intelligence , machine learning , monte carlo method , statistics , artificial neural network , geometry , filter (signal processing) , evolutionary biology , computer vision , biology , operating system
Feasibility analysis is used to determine the feasible region of a multivariate process. This can be difficult when the process models include black‐box constraints or the simulation is computationally expensive. To address such difficulties, surrogate models can be built as an inexpensive approximation to the original model and help identify the feasible region. An adaptive sampling method is used to efficiently sample new points toward feasible region boundaries and regions where prediction uncertainty is high. In this article, cubic Radial Basis Function (RBF) is used as the surrogate model. An error indicator for cubic RBF is proposed to indicate the prediction uncertainty and is used in adaptive sampling. In all case studies, the proposed RBF‐based method shows better performance than a previously published Kriging‐based method. © 2016 American Institute of Chemical Engineers AIChE J , 63: 532–550, 2017