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Incorporating setting information for maintenance-free quality modeling of batch processes

Authors

  • Zhiqiang Ge,

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

    1. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Department of Control Science and Engineering, Zhejiang University, Hangzhou, China
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  • Furong Gao

    Corresponding author
    1. Center for Polymer Processing and Systems, Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, Hong Kong, China
    • Dept. of Chemical and Biomolecular engineering, The Hong Kong University of Science and Technology, Hong Kong, China
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Correspondence concerning this article should be addressed to Z. Ge at zqge@iipc.zju.edu.cn or F. Gao at kefgao@ust.hk.

Abstract

Typically, the operation condition of the batch process is changed frequently, following different recipes or manufacturing various production grades. For quality prediction purpose, the prediction model should also be updated or rebuilt, which leads to a significant model maintenance effort, especially for those processes which have various phases. To reduce such effort, a maintenance-free method is proposed in this article, which incorporates the setting information of the batch process for modeling. The whole process variations are separated into two parts: setting information related and other quality related variations. By constructing a relationship between setting variables and other process variables, the data variations explained by the setting information can be efficiently removed. Then, a robust regression model connecting process variables to the quality variable is developed in different phases of the batch process. The feasibility and effectiveness of the proposed method is evaluated through an industrial injection molding process. © 2012 American Institute of Chemical Engineers AIChE J, 59: 772–779, 2013

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