文献类型：期刊文献

英文题名：Causal Network Structure Learning Based on Partial Least Squares and Causal Inference of Nonoptimal Performance in the Wastewater Treatment Process

作者：Wang, Yuhan[1];Yang, Dan[1];Peng, Xin[1];Zhong, Weimin[1];Cheng, Hui[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China

年份：2022

卷号：10

期号：5

外文期刊名：PROCESSES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000803312800001)】；

基金：This work is supported by the National Key Research & Development Program-Intergovernmental International Science and Technology Innovation Cooperation Project (2021YFE0112800), National Natural Science Foundation of China (Major Program: 61890930-3), National Natural Science Fund for Distinguished Young Scholars (61925305), National Natural Science Foundation of China (62173145) and Innovative development project of the industrial internet in 2020 (TC200802D).

语种：英文

外文关键词：nonoptimal cause identification; Granger causality analysis; Bayesian network; partial least squares

摘要：Due to environmental fluctuations, the operating performance of complex industrial processes may deteriorate and affect economic benefits. In order to obtain maximal economic benefits, operating performance assessment is a novel focus. Therefore, this paper proposes a whole framework from operating performance assessment to nonoptimal cause identification based on partial-least-squares-based Granger causality analysis (PLS-GC) and Bayesian networks (BNs). The proposed method has three main contributions. First, a multiblock operating performance assessment model is established to correspondingly extract economic-related information and dynamic information. Then, a Bayesian network structure is established by PLS-GC that excludes the strong coupling of variables and simplifies the network structure. Lastly, nonoptimal root cause and and nonoptimal transmission path are identified by Bayesian inference. The effectiveness of the proposed method was verified on Benchmark Simulation Model 1.