Nonlinear quality prediction for multiphase batch processes

Authors

  • Zhiqiang Ge,

    1. Dept. of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
    2. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, Zhejiang, P.R. China
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  • Zhihuan Song,

    1. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, Zhejiang, P.R. China
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  • Furong Gao

    Corresponding author
    1. Dept. of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
    2. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, Zhejiang, P.R. China
    • Dept. of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

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Abstract

Typically, a multiphase batch process comprises several steady phases and transition periods. In steady phases, the data characteristics remain similar during the phase and have a significant repeatability from batch to batch; thus most data nonlinearities can be removed through the batch normalization step. In contrast, in each transition period, process observations vary with time and from batch to batch, so nonlinearities in the data may not be eliminated through batch normalization. To improve quality prediction performance, an efficient nonlinear modeling method—relevance vector machine (RVM) was introduced. RVMs were formulated for each transition period of the batch process, and for combining the results of different process phases. For process analysis, a phase contribution index and a variable contribution index are defined. Furthermore, detailed performance analyses on the prediction uncertainty and variation were also provided. The effectiveness of the proposed method is confirmed by an industrial example. © 2011 American Institute of Chemical Engineers AIChE J, 58: 1778–1787, 2012

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