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PLS: A versatile tool for industrial process improvement and optimization

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Listed:
  • Alberto Ferrer
  • Daniel Aguado
  • Santiago Vidal‐Puig
  • José Manuel Prats
  • Manuel Zarzo

Abstract

Modern industrial processes are characterized by acquiring massive amounts of highly collinear data. In this context, partial least‐squares (PLS) regression, if wisely used, can become a strategic tool for process improvement and optimization. In this paper we illustrate the versatility of this technique through several real case studies that basically differ in the structure of the X matrix (process variables) and Y matrix (response parameters). By using the PLS approach, the results show that it is possible to build predictive models (soft sensors) for monitoring the performance of a wastewater treatment plant, to help in the diagnosis of a complex batch polymerization process, to develop an automatic classifier based on image data, or to assist in the empirical model building of a continuous polymerization process. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Alberto Ferrer & Daniel Aguado & Santiago Vidal‐Puig & José Manuel Prats & Manuel Zarzo, 2008. "PLS: A versatile tool for industrial process improvement and optimization," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(6), pages 551-567, November.
    • Handle: RePEc:wly:apsmbi:v:24:y:2008:i:6:p:551-567
    DOI: 10.1002/asmb.716
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    References listed on IDEAS

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    1. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    2. John Hulland, 1999. "Use of partial least squares (PLS) in strategic management research: a review of four recent studies," Strategic Management Journal, Wiley Blackwell, vol. 20(2), pages 195-204, February.
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