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Bayesian inference and joint probability analysis for batch process monitoring

Authors

  • Zhiqiang Ge,

    Corresponding author
    1. Dept. of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, Zhejiang, China
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  • Zhihuan Song

    1. Dept. of Control Science and Engineering, State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, Zhejiang, China
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Abstract

A new probabilistic monitoring method for batch processes that have multiple operating conditions is described. Particularly, for multiphase batch processes, a phase-based Bayesian inference strategy is introduced, which can efficiently combine the information of multiple operation modes together into a single model in each specific phase. Therefore, without any process knowledge, local monitoring results in different operation modes can be automatically integrated. Besides, the information of the operation mode can be obtained through joint probability analysis under the Bayesian monitoring framework. Potential extensions of the proposed method for fault diagnosis and identification are also discussed. A benchmark case study on the penicillin fermentation process is given to evaluate the feasibility and efficiency of the proposed method. It is demonstrated that the monitoring performance and the process comprehension have both been improved. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3702–3713, 2013

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