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Process Knowledge Discovery Using Sparse Principal Component Analysis
School of Information Science and Technology, Beijing University of Chemical Technology, Beijing.
Department of Chemical Engineering, University of California, Davis.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
School of Information Science and Technology, Beijing University of Chemical Technology, Beijing.
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Number of Authors: 5
2016 (English)In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 55, no 46, 12046-12059 p.Article in journal (Refereed) Published
Abstract [en]

As the goals of ensuring process safety and energy efficiency become ever more challenging, engineers increasingly rely on data collected from such processes for informed decision making. During recent decades, extracting and interpreting valuable process information from large historical data sets have been an active area of research. Among the methods used, principal component analysis (PCA) is a well-established technique that allows for dimensionality reduction for large data sets by finding new uncorrelated variables, namely principal components (PCs). However, it is difficult to interpret the derived PCs, as each PC is a linear combination of all of the original variables and the loadings are typically nonzero. Sparse principal component analysis (SPCA) is a relatively recent technique proposed for producing PCs with sparse loadings via the variance–sparsity trade-off. We propose a forward SPCA approach that helps uncover the underlying process knowledge regarding variable relations. This approach systematically determines the optimal sparse loadings for each sparse PC while improving interpretability and minimizing information loss. The salient features of the proposed approach are demonstrated through the Tennessee Eastman process simulation. The results indicate how knowledge and process insight can be discovered through a systematic analysis of sparse loadings.

Place, publisher, year, edition, pages
2016. Vol. 55, no 46, 12046-12059 p.
National Category
Reliability and Maintenance
Research subject
Quality Technology and Management
Identifiers
URN: urn:nbn:se:ltu:diva-60485DOI: 10.1021/acs.iecr.6b03045OAI: oai:DiVA.org:ltu-60485DiVA: diva2:1047108
Note

Validerad; 2016; Nivå 2; 2016-12-02 (inah)

Available from: 2016-11-16 Created: 2016-11-16 Last updated: 2016-12-02Bibliographically approved

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