SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (3): 263-270.DOI: 10.1016/S1004-9541(13)60485-4

• 过程系统工程与过程安全 • 上一篇    下一篇

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

郭辉1,2, 李宏光1   

  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
  • 收稿日期:2011-01-31 修回日期:2012-10-18 出版日期:2013-03-28 发布日期:2013-04-01
  • 通讯作者: LI Hongguang
  • 基金资助:

    lihg@mail.buct.edu.cn

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

GUO Hui1,2, LI Hongguang1   

  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
  • Received:2011-01-31 Revised:2012-10-18 Online:2013-03-28 Published:2013-04-01
  • Supported by:

    lihg@mail.buct.edu.cn

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

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