Enhanced monitoring of batch process using just-in-time learning-based kernel independent component analysis

Abstract

A new method is developed for batch process monitoring in this paper. In the developed method, just-in-time learning (JITL) and kernel independent component analysis (KICA) are integrated to build JITL-KICA monitoring scheme. JITL is employed to tackle with the characteristics of batch process such as inherent time-varying dynamics, multiple operating phases, and especially the case of uneven length stage. According to new coming test data, the most correlated segmentation is obtained from batch-wise unfolded training data by JITL. Then, KICA serves as the principal components extraction approach. Therefore, the non-Gaussian distributed data can also be addressed under this modelling framework. The effectiveness and superiority of JITL-KICA-based monitoring method is demonstrated through benchmark data of DuPont industrial batch polymerisation reactor.