Multiway Interval Partial Least Squares for Batch Process Performance Monitoring

The method of interval Partial Least Squares (iPLS) is combined with multiway partial least-squares (MPLS) to allow the building of enhanced statistical process performance monitoring models. A novel algorithm is proposed for segmenting batch duration, or spectral data in applications employing spectroscopy data into several subintervals for which independent PLS models can be constructed. The approach deviates from the method of using subintervals of equal length and the practice of choosing only a subset of these subintervals for prediction and/or monitoring. The proposed approach provides dramatic reduction in the number of subintervals required and subsequently the number of PLS models required to give improved prediction and monitoring performance. The proposed method, MiPLS, is applied to the well-known benchmark fed-batch penicillin production simulator, Pensim, for quality variable prediction and fault detection.