Discriminating between Critical and Noncritical Disturbances in (Bio)Chemical Batch Processes Using Multimodel Fault Detection and End-Quality Prediction

This paper proposes a novel multimodel methodology for discriminating between critical and noncritical process disturbances in (bio)chemical batch processes, in addition to providing online prediction of batch-end quality. A multivariate multiway partial least squares (MPLS) or multiway principal component analysis (MPCA) model monitoring all available measurements is coupled with an MPLS or MPCA model monitoring only those measurements influencing the final product quality. Hence, process disturbances are labeled critical or noncritical, depending on whether they impact final quality and require immediate attention. This avoids unnecessary control actions or even early batch terminations for noncritical disturbances. The presented approach is illustrated on a simulated industrial-scale penicillin production process. On the basis of extensive simulation results, it is concluded that the proposed methodology discriminates between critical (according to a hypothesis test with 0.05 significance level) and noncritical disturbances. In addition, accurate online estimations of the batch-end product quality are provided.