Multivariate Statistical Process Control of Batch Processes Using PCA and PLS

Multivariate statistical procedures for monitoring the progress of batch processes are developed. The only information needed to exploit the procedures is a historical database of past successful batches. Multi-way Projection to Latent Structures is used to extract the information in the batch set-up data and in the multivariate trajectory data, by projecting them onto low dimensional spaces defined by the latent variables or principal components. This leads to simple monitoring charts, consistent with the philosophy of SPC, which are capable of tracking the progress of new batch runs and detecting the occurrence of observable upsets. The approach is illustrated with datasets from industrial batch processes.