PLS: A versatile tool for industrial process improvement and optimization
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 tech...
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| Veröffentlicht in: | Applied stochastic models in business and industry 2008-11, Vol.24 (6), p.551-567 |
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| container_title | Applied stochastic models in business and industry |
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| creator | Ferrer, Alberto Aguado, Daniel Vidal-Puig, Santiago Prats, José Manuel Zarzo, Manuel |
| description | 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. |
| doi_str_mv | 10.1002/asmb.716 |
| format | Article |
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| language | eng |
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| source | EBSCOhost Business Source Complete; Access via Wiley Online Library |
| subjects | classification fault diagnosis monitoring multivariate image analysis PLS time series soft sensor |
| title | PLS: A versatile tool for industrial process improvement and optimization |
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