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Multivariate process and quality monitoring applied to an electrolysis process.: Part II - Multivariate time-series analysis of lagged latent variables
Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
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1998 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, Vol. 42, no 1-2, 233-240 p.Article in journal (Refereed) Published
Abstract [en]

Multivariate time series analysis is applied to understand and model the dynamics of an electrolytic process manufacturing copper. Here, eight metal impurities were measured, twice daily, over a period of one year, to characterize the quality of the copper. In the data analysis, these eight variables were summarized by means of principal component analysis PCA.. Two principal component PC.scores were sufficient to well summarize the eight measured variables R2s0.67.. Subse-quently, the dynamics of these PC-scores latent variables.were investigated using multivariate time series analysis, i.e., par-tial least squares PLS.modelling of the lagged latent variables. Stochastic models of the auto-regressive moving average ARMA.family were appropriate for both PC-scores. Hence, the dynamics of both scores make the exponentially weighted moving average EWMA.control chart suitable for process monitoring.

Place, publisher, year, edition, pages
1998. Vol. 42, no 1-2, 233-240 p.
Keyword [en]
Multivariate time series analysis, PLS, PCA, EWMA, control chart
National Category
Mathematics Control Engineering
Identifiers
URN: urn:nbn:se:miun:diva-4723DOI: 10.1016/S0169-7439(98)00015-XLocal ID: 2245OAI: oai:DiVA.org:miun-4723DiVA: diva2:29755
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2011-01-10Bibliographically approved

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