2020年6月24日
Optimal Weighting Distance-Based Similarity for Locally Weighted PLS Modeling
Industrial and Engineering Chemistry Research
- ,
- ,
- 巻
- 59
- 号
- 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
- ID情報
-
- DOI : 10.1021/acs.iecr.9b06847
- ISSN : 0888-5885
- eISSN : 1520-5045
- ORCIDのPut Code : 138871159
- SCOPUS ID : 85087619724