Data Selection and Regression Method and Its Application to Softsensing Using Multirate Industrial Data

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The estimation of difficult and infrequently measured variables (composition, melt flow index, viscosity, etc.) using easily and frequently measured variables (temperatures, flow rates, pressure, etc.) is of industrial interest. From such multirate data (data available at different sampling rates), a mathematical model that relates the frequently measured variables to the infrequently measured variable is developed—this model is often referred to as the soft sensor. This work considers the development of soft sensors to predict the concentration of a hydrocarbon species <I>R</I> at the exit of a two-reactor train. Specifically, we examine the development of soft sensors (one for each reactor) using optimal window size and demonstrate the efficacy of multiple model based prediction.

収録刊行物

  • Journal of chemical engineering of Japan  

    Journal of chemical engineering of Japan 41(5), 374-383, 2008-05-01 

    The Society of Chemical Engineers, Japan

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各種コード

  • NII論文ID(NAID)
    10021110269
  • NII書誌ID(NCID)
    AA00709658
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    00219592
  • NDL 記事登録ID
    9498288
  • NDL 雑誌分類
    ZP1(科学技術--化学・化学工業)
  • NDL 請求記号
    Z53-R395
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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