文献类型：期刊文献

英文题名：Ensemble model of wastewater treatment plant based on rich diversity of principal component determining by genetic algorithm for status monitoring

作者：Li, Zhichao[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2019

卷号：88

起止页码：38

外文期刊名：CONTROL ENGINEERING PRACTICE

收录：;EI(收录号：20191906903741);WOS:【SCI-EXPANDED(收录号:WOS:000472695600004)】；

基金：The authors are grateful for the support of National Natural Science Foundation of China (21878081), Fundamental Research Funds for the Central Universities under Grant of China (222201917006), and the Program of Introducing Talents of Discipline to Universities (the 111 Project), China under Grant B17017.

语种：英文

外文关键词：Principal component analysis; Ensemble learning; Bayesian inference; Process monitoring; Genetic algorithm

摘要：Wastewater treatment plants (WWTPs) is a complex process, effective process monitoring can make it stable and prevent the destruction of the ecological environment. Principal component analysis (PCA) has been widely used in process monitoring. However, most PCA-based methods construct a single PCA model using several principal components (PCs), causing loss of information on some faults and less generalization ability of the PCA model. Thus, this study proposed a novel ensemble process monitoring method based on genetic algorithm (GA) for selective diversity of PCs. GA is used to determine a set of principal component subspaces with the greatest diversity as the base models. Bayesian inference is adopted to combine the results of base models into a probability index. Cases study on TE benchmark process and an actual WWTP show the excellent performance of the proposed method compared with several PCA-based methods and the strong generalization ability of the ensemble model.