Dimensionality reduction in feasibility analysis by latent variable modeling

Gabriele Bano, Zilong Wang, Pierantonio Facco, Fabrizio Bezzo, Massimiliano Barolo, Marianthi Ierapetritou

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We propose a systematic methodology to exploit partial least-squares (PLS) regression modeling to reduce the dimensionality of a feasibility analysis problem. PLS is used to project the original multidimensional space of input factors onto a lower dimensional latent space. We then apply a radial basis function (RBF) adaptive sampling feasibility analysis on this lower dimensional space to identify the feasible region of the process. A simple low-dimensional representation of the feasible region is thus obtained with this combined PLS-RBF approach. The performance of the methodology is tested on a mathematical example involving six inputs. We show the ability of this PLS-RBF approach to reduce the computational burden of the feasibility analysis while maintaining an accurate and robust identification of the feasible region.

Original languageEnglish (US)
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages1477-1482
Number of pages6
DOIs
StatePublished - Jan 1 2018

Publication series

NameComputer Aided Chemical Engineering
Volume44
ISSN (Print)1570-7946

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Keywords

  • adaptive sampling
  • feasibility analysis
  • model reduction
  • partial least-squares regression
  • radial basis function

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