Enhanced batch process monitoring and quality prediction based on multi-phase multi-way partial least squares

For multistage, time-variant, nonlinear characteristic and unavailable on-line product qualities of batch process, a multi-phase Multi-way partial least squares (MP-MPLS) method is proposed. Using ISODATA dynamic clustering algorithm, process data was automatically divided into several operation stages according to relevance. Then, Using recursive DTW algorithm to synchronize these unequal Sub-phase, and sub-phase MPLS models were developed for every phases for on-line monitoring and quality prediction. The proposed method easily handles the following problems: (1) static single model; (2) process and its model do not match; (3) linear method may not be efficient in compressing and extracting nonlinear process data. The idea and algorithm are illustrated with respect to the typical data collected from a benchmark simulation of fed-batch penicillin fermentation production. For comparison purposes, a traditional MPLS model and a knowledge-based MPLS model was established. The results demonstrate the effectiveness of the proposed method.