文献类型：期刊文献

英文题名：Key performance index estimation based on ensemble locally weighted partial least squares and its application on industrial nonlinear processes

作者：Chen, Xin[1];Zhong, Weimin[1,2];Jiang, Chao[1,3];Li, Zhi[1];Peng, Xin[1];Cheng, Hui[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China;[3]Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada

年份：2020

卷号：203

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616432823);WOS:【SCI-EXPANDED(收录号:WOS:000552126900018)】；

基金：The authors declare no competing financial interest. The work was supported by the National Natural Science Foundation of China under Grants 61890930-3, 61925305 and 61803157, the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, Shanghai Sailing Programme under Grant 18YF1405200, Fundamental Research Funds for the Central Universities under Grant 222201917006, 222201814041.

语种：英文

外文关键词：Locally weighted partial least squares; Soft sensors; Ensemble learning; Quality prediction; Catalytic reforming process

摘要：Recent decades have witnessed a trend that soft sensing, instead of hard sensing, has been extensively applied to estimate the key performance indices under the circumstances that practical measurements are hardly to be achieved at a reasonable cost. However, due to the existence of nonlinearities and time-varying characteristics in the practical industrial processes, the conventional soft sensor models probably suffer from severe performance degradations when the original designed models are mismatched. Although many novel methodologies have been employed to alleviate this problem, each of them merely focuses on certain aspect of model features, a comprehensive framework combining these features is needed. Therefore, this study proposes an online predictive methodology based on an integration of ensemble learning based on a novel adaptive locally weighted partial least squares. Specifically, sub-models established on the respective dataset are generated by moving window model, time difference model and just-in-time learning model for the sake of different properties in processes. The effectiveness of the proposed model is validated on the practical nonlinear processes represented by a benchmark simulation model No.1 (BSM1), in wastewater treatment plants (WWTP), and a real industrial catalytic reforming process.