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

郭辉; 李宏光

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

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

过程系统工程与过程安全 | Updated：2026-01-06

- On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis
- On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis
- 中国化学工程学报（英文版） 2013年21卷第3期页码：263-2701004-954160485-4
- Affiliations：
  
  2 College of Mathematics & Computer Science, Ningxia University,Yinchuan,China,750021
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(13)60485-4
  中图分类号：
- 纸质出版：2013
- Accepted：
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郭辉, 李宏光. On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis[J]. 中国化学工程学报（英文版）, 2013,21(3):263-2701004-954160485-4.

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]. 中国化学工程学报（英文版）, 2013,21(3):263-2701004-954160485-4. DOI： 10.1016/S1004-9541(13)60485-4.

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. DOI： 10.1016/S1004-9541(13)60485-4.

摘要

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.

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