An Adjoined Multi-dpca Approach for Online Monitoring of Fed-batch Processes

Batch processes are common in the manufacturing of high value-added products. Monitoring with the highly popular principal components analysis (PCA) approaches do not function adequately in the face of the sequential nature of batch processes, as the basic assumptions that its monitoring statistics (SPE and Hotelling’s T) are developed upon – stationary, normal distribution of source data are violated. Consequently, these monitoring techniques become prone to Type-I (false positives) and Type-II Errors (false negatives). In this article, an extension of PCA, called adjoined dynamic principal component analysis (ADPCA), is proposed for online monitoring of batch processes by using multiple dynamic-PCA (DPCA) models. The ADPCA models are developed by first clustering process data using fuzzy c-means algorithm and developing a DPCA model for each cluster. Each cluster is selected so that it satisfies the PCA’s assumption. The problem of switching between the models which normally confounds multiple model-based approaches is overcome by allowing adjoining models to overlap and thus enabling smooth switching from one model to another during the course of batch operations. As shown in this paper, the proposed methodology reduces both Type-I and Type-II errors compared to single block methods. Copyright © 2006 IFAC.