Online prediction of quality‐related variables for batch processes using a sequential phase partition method

Abstract

Batch processes inherently have multiple operation phases; different phases exhibit different characteristics. Hence, it is reasonable to partition the process into phases and build sub‐phase models for online quality prediction. To this end, a sequential phase partition method based on the information increment is proposed. To address the multiphase behaviours in batch processes, this work utilizes a new information increment index to capture the dynamic characteristics of batch processes along a time direction and divides the process into sub‐phases. Next, phase‐based multiway partial least squares (MPLS) models are built to model within‐phase characteristics and predict the quality‐related variables online. Information increment is able to exploit the process evolution by focusing on the changing variable correlations derived from two adjacent extend time slice. It directly utilizes the available process measurements of successful history batch processes without data transformation or dimensionality reduction. The method is sequential and can overcome the limits of some phase partition methods that may divide the samples with discontinuous time sequence but similar characteristics into the same phase. In addition, the information increment is capable of reflecting the change of the process intuitively with high computation efficiency. Advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli (E. coli) fermentation, respectively.