Multivariate process and quality monitoring applied to an electrolysis process: Part II. Multivariate time-series analysis of lagged latent variables

✍ Scribed by Conny Wikström; Christer Albano; Lennart Eriksson; Håkan Fridén; Erik Johansson; Åke Nordahl; Stefan Rännar; Maria Sandberg; Nouna Kettaneh-Wold; Svante Wold

• Publisher: Elsevier Science
• Year: 1998
• Tongue: English
• Weight: 334 KB
• Volume: 42
• Category: Article
• ISSN: 0169-7439
• DOI: 10.1016/s0169-7439(98)00015-x

✦ Synopsis

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 Ž . the copper. In the data analysis, these eight variables were summarized by means of principal component analysis PCA .

Ž . Ž 2 . Two principal component PC scores were sufficient to well summarize the eight measured variables R s 0.67 . Subse-Ž . quently, the dynamics of these PC-scores latent variables were investigated using multivariate time series analysis, i.e., par-Ž . tial least squares PLS modelling of the lagged latent variables. Stochastic models of the auto-regressive moving average Ž . ARMA family were appropriate for both PC-scores. Hence, the dynamics of both scores make the exponentially weighted Ž . moving average EWMA control chart suitable for process monitoring.