Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compchemeng.2007.05.010
Title: Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control
Authors: Doan, X.-T.
Srinivasan, R. 
Keywords: Dynamic PCA
Dynamic time warping
Feature synchronization
Multi-stage process
Singular point
Issue Date: Jan-2008
Source: Doan, X.-T., Srinivasan, R. (2008-01). Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control. Computers and Chemical Engineering 32 (1-2) : 230-243. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compchemeng.2007.05.010
Abstract: Online monitoring of batch processes using multivariate statistical methods has attracted enormous research interests due to its practical importance. In this paper, we focus on an important issue that continues to confound online batch process monitoring-run-to-run variations that do not confirm to a normal distribution around a reference trajectory. Here, we show that a phase-based decomposition of the trajectory offers a systematic way to overcome this challenge. In our approach, phase changes are detected online using Singular points in key variables. Run-to-run variations among different instances of a phase are synchronized by using time warping. Finally, phased-based multivariate statistical process control models are used to monitor the execution of the batch and detect abnormalities. This phase-based monitoring approach is robust to run-to-run variations arising from changes in initial conditions and event timings as is illustrated using a well-known fermentation process simulation. © 2007 Elsevier Ltd. All rights reserved.
Source Title: Computers and Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/64330
ISSN: 00981354
DOI: 10.1016/j.compchemeng.2007.05.010
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