文献类型：期刊文献

英文题名：A flexible multi-step prediction architecture for process variable monitoring in chemical intelligent manufacturing

作者：Li, Yue[1];Cao, Hongtao[2];Li, Zhongmei[3];Du, Wenli[4];Shen, Weifeng[2,4]

机构：[1]Guangxi Minzu Univ, Sch Chem & Chem Engn, Nanning 530006, Peoples R China;[2]Chongqing Univ, Sch Chem & Chem Engn, Chongqing 400044, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：316

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20252318573440);WOS:【SCI-EXPANDED(收录号:WOS:001506629600001)】；

基金：We acknowledge the financial support provided by the National Natural Science Foundation for Excellent Young Scientists of China (No. 22122802) ; the National Natural Science Foundation of China (No. 22278044) ; the Chongqing Science Fund for Distinguished Young Scholars (No. CSTB2022NSCQ-JQX0021) ; the Chongqing Innovation Support Key Program for Returned Overseas Chinese Scholars (cx2023002) ; and the Open Research Project of the State Key Laboratory of Industrial Control Technology, China (Grant No. ICT2024B01) .

语种：英文

外文关键词：Flexible multi-step prediction; Process variable monitoring; Interpretable deep learning; Multivariable monitoring; Intelligent modeling architecture

摘要：Multi-step prediction models can forecast variables ahead of time, which is valuable for process variable monitoring. Although deep learning (DL) is promising in multi-step prediction, its compatibility, interpretability and practicality, which are crucial for chemical applications, have received little attention. Thus, a DL architecture-Light Attention-Mixed Base Target Autoregression Unit (LAMB-TAU) is proposed. It utilizes specialdesigned networks to simulate process driving forces, and wraps these networks with a decoder, delivering an interpretable and high-accuracy multi-step prediction on process variables. Moreover, an adaptable sampling procedure is proposed to free the multi-step predictions on difficult-to-measure variables from high-cost experiments. The effectiveness of LAMB-TAU is verified by modeling studies on chemical processes including esterification and formaldehyde production. Besides, model practicalities including extended multi-step prediction, uncertainty and computational cost are explored. The proposed LAMB-TAU is instructive for DL multistep prediction studies toward chemical process monitoring, which promotes the development of intelligent chemical industry.