Automatic segmentation of batch processes into multi-local state-space models for fault detection. (5th March 2023)

• Record Type: Journal Article
• Authors: Gu, Shaowu; Chen, Junghui; Xie, Lei
• Is Part Of: Chemical engineering science. Volume 267(2023)
• Publication Date: 2023-03-05
• Subjects: Batch process -- Fault detection -- Multiple local models clustering algorithm -- Subspace identification; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2022.118274
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: Multi-local subspace models are proposed for nonlinear multimode batch processes. The modeling method handles clustering and modeling simultaneously. It can deal with the uneven data length in both phase and operation durations. A residual generator of each local model is designed for fault detection. Case studies regarding monitoring batch processes with multimodes are presented. Abstract: Several local models are used to approximate the nonlinear and multiphase characteristics of the batch process. Existing methods using phase partition and modeling can yield a model that does not describe the optimal local behavior. Batch data with uneven length also increases the difficulty of modeling local behavior. This study proposes Auto-Segmentation Subspace IDentification (AS-SID) for modeling a nonlinear batch process using a multi-stage operation over an operating region. It automatically and simultaneously reinforces data that pertains to the corresponding local models and weakens data that does not pertain to other local models. The entire range of the operation is partitioned and multiple state-space models are constructed using the partitioned data. To monitor the running batch using only the online collected data, a residual generator AS-SID is used for online detection. The merits of the proposed AS-SID are demonstrated using a numerical example and a fed-batch penicillin production process.