Enhanced batch process monitoring and quality prediction using multi-phase dynamic PLS

In industrial manufacturing, most batch processes are multi-phase and uneven-length batch processes in nature, phase-based approaches are intuitively well suited for batch process monitoring and quality prediction. In this paper, a new strategy is proposed using multi-phase dynamic partial least squares (DPLS) for batch processes monitoring and quality prediction. Firstly, batch process data was automatically divided into several phases using Gaussian mixture model (GMM) clustering arithmetic. Then run¯to¯run variations among different instances of a phase are synchronized by using dynamic time warping (DTW). Finally, multi-phase DPLS model is built between each phase and the quality variables. 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 dynamic 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. The simulation results demonstrate the effectiveness of the proposed method in comparison to original DPLS.