論文

査読有り
2020年6月24日

Optimal Weighting Distance-Based Similarity for Locally Weighted PLS Modeling

Industrial and Engineering Chemistry Research
  • Xinmin Zhang
  • ,
  • Manabu Kano
  • ,
  • Zhihuan Song

59
25
開始ページ
11552
終了ページ
11558
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1021/acs.iecr.9b06847
出版者・発行元
American Chemical Society ({ACS})

Real-time prediction of product quality or other key performance indicators is critical to ensuring high-quality products and increasing economic profit. In this study, a new locally weighted partial least-squares (LW-PLS) method using optimal weighting distance-based similarity, denoted as OLW-PLS, is proposed. OLW-PLS is a nonlinear just-in-time modeling method, which can handle process collinearity, nonlinearity, and time-varying characteristics. In OLW-PLS, a weighted PLS regression model is constructed based on the optimal weighting distance-based similarity, which considers variable interactions and nonlinear dependencies between the input variables and the output in an optimal manner. The feasibility and effectiveness of the proposed OLW-PLS method were validated through its applications to a numerical example, an industrial ethylene fractionation process, and a pharmaceutical process. The application results have demonstrated that OLW-PLS has superior prediction performance than the conventional PLS, LW-PLS, and CbLW-PLS.

リンク情報
DOI
https://doi.org/10.1021/acs.iecr.9b06847
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087619724&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85087619724&origin=inward
ID情報
  • DOI : 10.1021/acs.iecr.9b06847
  • ISSN : 0888-5885
  • eISSN : 1520-5045
  • ORCIDのPut Code : 138871159
  • SCOPUS ID : 85087619724

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