An Improved MICA Approach with Applications to Batch Process Monitoring
نویسندگان
چکیده
On-line monitoring of batch processes using multi-way independent component analysis has attracted much attention in both academia and industry. This paper focuses on two knotty 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. Additionally, the monitored statistics are transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. Finally, the proposed method is applied to on-line monitoring of a fed-batch penicillin fermentation simulator, giving rise to satisfied results. Key-Words: Batch process monitoring, MICA, System deviation, Box-Cox transformation, Contribution plots, Fed-batch fermentation
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