Process Systems Engineering

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

  1. Zhiqiang Ge1,*,
  2. Zhihuan Song1,
  3. Furong Gao2,3,*

Article first published online: 25 JUN 2012

DOI: 10.1002/aic.13864

Issue

AIChE Journal

AIChE Journal

Volume 59, Issue 3, pages 772–779, March 2013

Additional Information

How to Cite

Ge, Z., Song, Z. and Gao, F. (2013), Incorporating setting information for maintenance-free quality modeling of batch processes. AIChE J., 59: 772–779. doi: 10.1002/aic.13864

Author Information

  1. 1

    State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Department of Control Science and Engineering, Zhejiang University, Hangzhou, China

  2. 2

    Dept. of Chemical and Biomolecular engineering, The Hong Kong University of Science and Technology, Hong Kong, China

  3. 3

    Center for Polymer Processing and Systems, Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, Hong Kong, China

*Correspondence concerning this article should be addressed to Z. Ge at zqge@iipc.zju.edu.cn or F. Gao at kefgao@ust.hk.

Publication History

  1. Issue published online: 20 FEB 2013
  2. Article first published online: 25 JUN 2012
  3. Accepted manuscript online: 5 JUN 2012 10:52AM EST
  4. Manuscript Revised: 22 MAY 2012
  5. Manuscript Received: 20 DEC 2011

Funded by

  • National Natural Science Foundation of China (NSFC). Grant Number: 61004134
  • National Project 973. Grant Number: 2012CB720500
  • Guangzhou scientific and technological project. Grant Number: 11F11140010
  • Fundamental Research Funds

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Keywords:

  • quality prediction;
  • multiphase batch process;
  • maintenance-free;
  • setting information;
  • robust modeling

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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