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PLS: A versatile tool for industrial process improvement and optimization
Author(s) -
Ferrer Alberto,
Aguado Daniel,
VidalPuig Santiago,
Prats José Manuel,
Zarzo Manuel
Publication year - 2008
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.716
Subject(s) - partial least squares regression , computer science , process (computing) , classifier (uml) , context (archaeology) , artificial intelligence , machine learning , data mining , process engineering , engineering , paleontology , biology , operating system
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.