An alternative PLS Algorithm for the Monitoring of Industrial Process

U. KRUGER, X. WANG, Q. CHEN, S. J. QIN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

22 Citations (Scopus)

Abstract

Partial least squares (PLS) has been developed and established as one of the multivariate statistical process control (MSPC) methods. PLS is designed to identify a parametric regression matrix between the input, or predictor variables and the output, or response variables of the process. With PLS, the regression matrix is determined on the basis of a subset of the predictor variables and thus, PLS is able to reduce the number of variables to be considered. In this paper, an alternative variable reduction method is introduced, which is termed latent variable least squares or LVLS. LVLS identifies the process behavior on the basis of the score models rather than a parametric relationship between the predictor and the response variables. In similar fashion to PLS, LVLS can also be applied to monitor industrial processes. An application study, which relates to a realistic simulation of a fluid catalytic cracking unit (FCCU), is presented to demonstrate the monitoring aspect of LVLS.
Original languageEnglish
Title of host publicationProceedings of the 2001 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers
Pages4455-4459
Number of pages5
ISBN (Electronic)078036497X
ISBN (Print)0780364953
DOIs
Publication statusPublished - Jun 2001
Externally publishedYes
Event2001 American Control Conference, ACC 2001 - Arlington, United States
Duration: 25 Jun 200127 Jun 2001

Publication series

NameProceedings of the American Control Conference
PublisherInstitute of Electrical and Electronics Engineers
ISSN (Print)0743-1619
ISSN (Electronic)2378-5861

Conference

Conference2001 American Control Conference, ACC 2001
Country/TerritoryUnited States
CityArlington
Period25/06/0127/06/01

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