文献类型：期刊文献

英文题名：An adaptive soft sensor method of D-vine copula quantile regression for complex chemical processes

作者：Ni, Jianeng[1];Li, Shaojun[1]

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

年份：2021

卷号：230

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20204509448227);WOS:【SCI-EXPANDED(收录号:WOS:000600281800004)】；

基金：The authors would like to thank the National Natural Science Foundation of China for its financial support (Project No. 21676086). Many thanks to the anonymous reviewers for their careful work, thoughtful suggestions, and detailed comments that have helped improve this paper substantially.

语种：英文

外文关键词：Soft sensor; Vine copula; Quantile regression; Process control

摘要：Non-linear and non-Gaussian properties are challenging topics in the soft sensor modeling of chemical processes, and fluctuations in the environmental conditions of chemical plants will also affect the accuracy of soft sensor models. This paper proposes an adaptive soft sensor method of D-vine copula quantile regression (aDVQR). In the modeling process, a sparse vine model is established using the Bayesian information criterion. Then, the conditional quantile function value of the specified quantile can be obtained via the recursive nesting method by the h function. An online model updating system based on the aDVQR model is also proposed, and an adaptive soft sensor model is established. The proposed adaptive soft sensor method can successfully approximate the non-linear and non-Gaussian relationships between variables and adapt to unstable environments. Finally, a numerical example and an example of the ethylene industry are used to verify the effectiveness of the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.