On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

doi:10.1016/S1004-9541(13)60485-4

Chin.J.Chem.Eng. ›› 2013, Vol. 21 ›› Issue (3): 263-270.DOI: 10.1016/S1004-9541(13)60485-4

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

GUO Hui^1,2, LI Hongguang¹

1 College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China ;
2 College of Mathematics & Computer Science, Ningxia University, Yinchuan 750021, China

Received:2011-01-31 Revised:2012-10-18 Online:2013-04-01 Published:2013-03-28
Supported by:
lihg@mail.buct.edu.cn

On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

郭辉^1,2, 李宏光¹

1 College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China ;
2 College of Mathematics & Computer Science, Ningxia University, Yinchuan 750021, China

通讯作者: LI Hongguang
基金资助:
lihg@mail.buct.edu.cn

Abstract

Abstract: In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and determining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components, we introduce a novel concept of system deviation, which is able to evaluate the reconstructed observations with different independent components. The monitored statistics are transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-batch penicillin fermentation simulator, and the experimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.

Key words: batch process monitoring, multi-way independent component analysis, system deviation, Box-Cox transformation

摘要： In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and determining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components, we introduce a novel concept of system deviation, which is able to evaluate the reconstructed observations with different independent components. The monitored statistics are transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-batch penicillin fermentation simulator, and the experimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.

关键词: batch process monitoring, multi-way independent component analysis, system deviation, Box-Cox transformation

GUO Hui, LI Hongguang. On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis[J]. Chin.J.Chem.Eng., 2013, 21(3): 263-270.

郭辉, 李宏光. On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis[J]. Chinese Journal of Chemical Engineering, 2013, 21(3): 263-270.

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On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

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