Recursive PLS algorithms for adaptive data modeling

• Published in: Computers & chemical engineering Vol. 22; no. 4; pp. 503 - 514
• Main Author: Joe Qin, S.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.01.1998; Elsevier
• Subjects: Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization; Applied sciences; Chemical engineering; chemical process modeling; cross-validation; dynamic modeling; Exact sciences and technology; forgetting factors; Partial least squares; recursive PLS; forgetting factors; cross-validation; recursive PLS; dynamic modeling; Partial least squares; chemical process modeling; PLS regression; Catalytic reforming; Dynamic model; Modeling; Non linear model
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/S0098-1354(97)00262-7

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.