Automatic segmentation of batch processes into multi-local state-space models for fault detection

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 non-linear batch process using a multi-stage operation over an operating region. It automatically and simul-taneously 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.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes an Auto-Segmentation Subspace Identification (AS-SID) method for modeling a nonlinear batch process. AS-SID automatically reinforces data that pertains to the corresponding local models and weakens data that does not pertain to other local models. Multiple state-space models are constructed using the partitioned data to model the entire operation range. AS-SID is also used for online detection by monitoring the running batch using only the online collected data.