Phase Analysis and Identification Method for Multiphase Batch Processes with Partitioning Multi-way Principal Component Analysis （MPCA） Model

Multi-way principal component analysis （MPCA） is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase informati...

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Bibliographic Details
Published in:中国化学工程学报：英文版 2012, Vol.20 (6), p.1121-1127
Main Author: 董伟威 姚远 高福荣
Format: Article
Language:English
Subjects:
MPCA
PCA模型
主成分分析
多相
多路
相分析
识别方法
间歇过程
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Summary:Multi-way principal component analysis （MPCA） is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.
ISSN:1004-9541
2210-321X