MONITORING BATCH PROCESSES USING MULTIWAY PRINCIPAL COMPONENT ANALYSIS

Multivariate statistical Procedures for monitoring the progress of bat ch Processes are developed. The only information needed to exploit the procedures is a historical database of past successful batches. Multi way PrinciPal component analysis is used to extract the information in the multivariate trajectory data by projecting them onto low-dimensio nal spaces defined by the latent variables or principal components. Th is leads to simple monitoring charts, consistent with the philosophy o f statistical process control, which are capable of tracking the progr ess of new batch runs and detecting the occurrence of observable upset s. The approach is contrasted with other approaches which use theoreti cal or knowledge-based models, and its potential is illustrated using a detailed simulation study of a semibatch reactor for the production of styrene-butadiene latex.