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Efficient sampling technique for optimization under uncertainty
Author(s) -
Diwekar Urmila M.,
Kalagnanam Jayant R.
Publication year - 1997
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.690430217
Subject(s) - sampling design , sampling (signal processing) , parametric statistics , computer science , sample size determination , optimal design , distillation , adaptive sampling , design of experiments , context (archaeology) , stochastic optimization , mathematical optimization , statistics , mathematics , monte carlo method , machine learning , filter (signal processing) , sociology , computer vision , biology , paleontology , population , chemistry , demography , organic chemistry
Abstract The concept of robust design involves identification of design settings that make the product performance less sensitive to the effects of seasonal and environmental variations. This concept is discussed in this article in the context of batch distillation column design with feed stock variations, and internal and external uncertainties. Stochastic optimization methods provide a general approach to robust/parameter design as compared to conventional techniques. However, the computational burden of these approaches can be extreme and depends on the sample size used for characterizing the parametric variations and uncertainties. A novel sampling technique is presented that generates and inverts the Hammersley points (an optimal design for placing n points uniformly on a k‐dimensional cube) to provide a representative sample for multivariate probability distributions. The example of robust batch‐distillation column design illustrates that the new sampling technique offers significant computational savings and better accuracy.