Application of Phase Division Based on Dissimilarity Index in Batch Process Monitoring

Abstract

An important feature of batch process data is that many batch processes have multiple phases. Many different phased-based monitoring methods had been proposed. The key question of those methods is how to divide the phases of batch process. However, PCA-based methods of phase division that identify phases by extracting the first principal component of each time slice lead easily to high misclassification. In order to overcome the shortcoming of PCA-based methods, a novel phase-division method based on dissimilarity index is proposed. In proposed division method, integral information of each time slice is used to divide phases. The phase-based PCA is built in each phase to monitoring Penicillin fermentation process in order to verify performance of proposed method. The simulation results show that the proposed method is able to detect process faults more prompt and accurate than single MPCA model.