An Improved Adaptive Multi-way Principal Component Analysis for Monitoring Streptomycin  Fermentation Process

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An Improved Adaptive Multi-way Principal Component Analysis for Monitoring Streptomycin
Fermentation Process

HE Ning^a,b; WANG Shuqing^a; XIE Lei^a

^a National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou
310027, China
^b Department of Control, Jiamusi University, Jiamusi 154007,China

Received:1900-01-01 Revised:1900-01-01 Online:2004-02-28 Published:2004-02-28
Contact: HE Ning

自适应MPCA方法在链霉素过程监控中的应用

何宁^a,b; 王树青^a; 谢磊^a

^a National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou
310027, China
^b Department of Control, Jiamusi University, Jiamusi 154007,China

通讯作者: 何宁

Abstract

Abstract: Multi-way principal component analysis (MPCA) had been successfully applied to monitoring
the batch and semi-batch process in most chemical industry. An improved MPCA approach,
step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring
the batch process is presented in this paper. It does not need to estimate or fill in the
unknown part of the process variable trajectory deviation from the current time until the
end. The approach is based on a MPCA method that processes the data in a sequential and
adaptive manner. The adaptive rate is easily controlled through a forgetting factor that
controls the weight of past data in a summation. This algorithm is used to evaluate the
industrial streptomycin fermentation process data and is compared with the traditional
MPCA. The results show that the method is more advantageous than MPCA,especially when
monitoring multi-stage batch process where the latent vector structure can change at
several points during the batch.

Key words: step-by-step adaptive multi-way principal component analysis, batch monitoring, streptomycin fer-mentation, static process monitoring

摘要： Multi-way principal component analysis (MPCA) had been successfully applied to monitoring
the batch and semi-batch process in most chemical industry. An improved MPCA approach,
step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring
the batch process is presented in this paper. It does not need to estimate or fill in the
unknown part of the process variable trajectory deviation from the current time until the
end. The approach is based on a MPCA method that processes the data in a sequential and
adaptive manner. The adaptive rate is easily controlled through a forgetting factor that
controls the weight of past data in a summation. This algorithm is used to evaluate the
industrial streptomycin fermentation process data and is compared with the traditional
MPCA. The results show that the method is more advantageous than MPCA,especially when
monitoring multi-stage batch process where the latent vector structure can change at
several points during the batch.

关键词: step-by-step adaptive multi-way principal component analysis;batch monitoring; streptomycin fer-mentation;static process monitoring

HE Ning, WANG Shuqing, XIE Lei. An Improved Adaptive Multi-way Principal Component Analysis for Monitoring Streptomycin
Fermentation Process[J]. .

何宁, 王树青, 谢磊. 自适应MPCA方法在链霉素过程监控中的应用[J]. CIESC Journal.

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