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Phase partition and KPI-related process monitoring for batch processes using a novel canonical correlation analysis strategy |
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| Authors: | Xuejin Gao Zihe He Huihui Gao Yongsheng Qi |
| Affiliation: | 1. Faculty of Information Technology, Beijing University of Technology, Beijing, China;2. Faculty of Information Technology, Beijing University of Technology, Beijing, China Engineering Research Centre of Digital Community, Ministry of Education, Beijing, China Beijing Laboratory for Urban Mass Transit, Beijing, China Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China Contribution: Methodology, Writing - original draft;3. Faculty of Information Technology, Beijing University of Technology, Beijing, China Engineering Research Centre of Digital Community, Ministry of Education, Beijing, China Beijing Laboratory for Urban Mass Transit, Beijing, China Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China Contribution: Supervision;4. School of Electric Power, Inner Mongolia University of Technology, Hohhot, China Contribution: Supervision |
| Abstract: | Key performance indicators (KPI)-related process monitoring has been of great significance to ensure product quality and economic benefits for batch processes. Considering that different phases exhibit different characteristics, one of the key issues is how to partition the whole batch process into different phases and characterize them separately by multiple phase models. In order to model and monitor batch processes more accurately and efficiently, a novel canonical correlation analysis (CCA) strategy is proposed in this paper. The phase partition algorithm is designed based on the joint canonical variable matrix (JCVM). Different from previous methods, it considers the time sequence of operation phases and can distinguish the phase switches from dynamics anomalies. Using this algorithm, phases are separated in order from a KPI-related perspective, revealing high correlation among variables. After phase partition, a novel multi-phase local neighbourhood standardization CAA (MPLNSCCA) method focusing on KPI is set up for online monitoring, which could further address the misclassification problems. The advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli fermentation, respectively. |
| Keywords: | batch process key performance indicator phase partition process monitoring |