• DocumentCode
    3572699
  • Title

    A modified PLS regression model for quality prediction

  • Author

    Yingwei Zhang ; Lingjun Zhang

  • Author_Institution
    State Lab. of Synthesis Autom. of Process Ind., Northeastern Univ., Shenyang, China
  • fYear
    2014
  • Firstpage
    1383
  • Lastpage
    1387
  • Abstract
    In this paper, a modified partial least-squares (PLS) regression modeling method is proposed. The proposed method can build a modified regression model to extract the useful information in residual subspace, which is helpful to predict the output variables. With this method, more accurate quality variables are predicted. In simulation experiment, penicillin fermentation process is used to test the proposed modified PLS method and the conventional PLS method is also applied in the process. It is shown that the proposed method is more effective than the conventional PLS method.
  • Keywords
    least squares approximations; predictive control; process control; product quality; regression analysis; information extraction; modified PLS regression model; modified partial least-squares regression modeling method; penicillin fermentation process; quality prediction; quality variables; residual subspace; Accuracy; Correlation; Mathematical model; Monitoring; Predictive models; Principal component analysis; Training; Modified PLS regression model; Prediction; Quality variables; Residual subspace;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2014 11th World Congress on
  • Type

    conf

  • DOI
    10.1109/WCICA.2014.7052921
  • Filename
    7052921