Enhanced monitoring of batch process using just-in-time learning-based kernel independent component analysis. (2017)

• Record Type: Journal Article
• Authors: Wang, Li
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 9:Number 3(2017)
• Page Start: 136
• Page End: 142
• Publication Date: 2017
• Subjects: batch process -- process modelling -- process monitoring -- time varying dynamics -- multiple operating phases -- uneven length stage -- non-Gaussian distribution -- kernel independent component analysis -- KICA -- just-in-time learning -- JITL -- online fault detection; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

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.