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

摘要

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 problem of temporal drift of the process and/or measurement technique by updating the NOC covariance matrix to adapt to normal process changes. The unifying multiblock 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.