Recursive PLS algorithms for adaptive data modeling

S. Joe QIN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

591 Citations (Scopus)


Partial least squares (PLS) regression is effectively used in process modeling and monitoring to deal with a large number of variables with collinearity. In this paper, several recursive partial least squares (RPLS) algorithms are proposed for on-line process modeling to adapt process changes and off-line modeling to deal with a large number of data samples. A block-wise RPLS algorithm is proposed with a moving window and forgetting factor adaptation schemes. The block-wise RPLS algorithm is also used off-line to reduce computation time and computer memory usage in PLS regression and cross-validation. As a natural extension, the recursive algorithm is extended to dynamic modeling and nonlinear modeling. An application of the block recursive PLS algorithm to a catalytic reformer is presented to adapt the model based on new data. © 1998 Elsevier Science Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)503-514
Number of pages12
JournalComputers and Chemical Engineering
Issue number4-5
Early online date27 Aug 1998
Publication statusPublished - 1998
Externally publishedYes


  • Chemical process modeling
  • Cross-validation
  • Dynamic modeling
  • Forgetting factors
  • Partial least squares
  • Recursive PLS


Dive into the research topics of 'Recursive PLS algorithms for adaptive data modeling'. Together they form a unique fingerprint.

Cite this