Window consensus PCA for multiblock statistical process control: Adaption to small and time-dependent normal operating condition regions, illustrated by online high performance liquid chromatography of a three-stage continuous process

Diana L S Ferreira, Sila Kittiwachana, Louise A. Fido, Duncan R. Thompson, Richard E A Escott, Richard G. Brereton*, Diana L Santos Ferreira

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)

Abstract

A method for multiblock statistical process control is described, involving the computation of Q and D statistics both for individual blocks and for the overall process using window consensus principal components analysis (WCPCA). The approach overcomes two common problems. The first is a small normal operating conditions (NOC) region, which is done by determining the Q-statistic limits and D statistics using leave-one-out (LOO) residuals and scores, rather than employing the residuals and scores of a single training set model obtained from the entire NOC region. The second overcomes the problemof temporal drift of the process and/or measurement technique by updating the NOC covariance matrix to adapt to normal process changes. The unifyingmultiblock statistical process control and relevant statistics are adapted to cope with these issues and are illustrated in this paper using CPCA as applied to online high performance liquid chromatography (HPLC) of a three-stage continuous process.

Original languageEnglish
Pages (from-to)596-609
Number of pages14
JournalJournal of Chemometrics
Volume24
Issue number9
DOIs
Publication statusPublished - 1 Sep 2010

Keywords

  • Consensus principal component analysis
  • Control limits
  • D statistic
  • High performance liquid chromatography
  • Multiblock methods
  • Process monitoring
  • Q statistic
  • Time-dependent covariance
  • Windowing

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