Multivariate SPC Charts for Monitoring Batch Processes

TLDR

The problem of using time‑varying trajectory data measured on many process variables over the finite duration of a batch process is considered. Multiway principal‑component analysis compresses the information in the data trajectories into low‑dimensional spaces that describe past batch operation, and control limits for the resulting multivariate SPC charts are derived from the historical reference distribution of successful batches, as demonstrated on data from an industrial batch polymerization reactor. This approach facilitates the analysis of operational and quality‑control problems in past batches and enables the development of multivariate SPC charts for online monitoring of new batch progress.

Abstract

The problem of using time-varying trajectory data measured on many process variables over the finite duration of a batch process is considered. Multiway principal-component analysis is used to compress the information contained in the data trajectories into low-dimensional spaces that describe the operation of past batches. This approach facilitates the analysis of operational and quality-control problems in past batches and allows for the development of multivariate statistical process control charts for on-line monitoring of the progress of new batches. Control limits for the proposed charts are developed using information from the historical reference distribution of past successful batches. The method is applied to data collected from an industrial batch polymerization reactor.