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Multivariate statistical monitoring of multiphase batch processes with between-phase transitions and uneven operation durations

Authors

  • Yuan Yao,

    Corresponding author
    1. Department of Chemical Engineering, National Tsing Hua University, Hsinchu, Taiwan
    • Department of Chemical Engineering, National Tsing Hua University, Hsinchu, Taiwan.
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  • Weiwei Dong,

    1. Center for Polymer Processing and Systems, The Hong Kong University of Science and Technology, Nansha, Guangzhou, China
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  • Luping Zhao,

    1. Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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  • Furong Gao

    1. Center for Polymer Processing and Systems, The Hong Kong University of Science and Technology, Nansha, Guangzhou, China
    2. Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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Abstract

In order to achieve satisfactory monitoring, multivariate statistical process models should well reflect process nature. In manufacturing systems, many batch processes are inherently multiphase. Usually, different phases have different characteristics, while gradual transitions are often observed between phases. Another important feature of batch processes is the unevenness of operation durations. Especially, in multiphase batch processes, the situation becomes more complicated. In this study, a batch process modelling and monitoring strategy is proposed based on Gaussian mixture model (GMM), which can automatically extract phase and transition information for uneven-duration batch processes. The application results verify the effectiveness of the proposed method. © 2011 Canadian Society for Chemical Engineering

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