Knowledge Commons of Institute of Automation,CAS
Transfer learning for nonlinear batch process operation optimization | |
Chu, Fei1,2,3; Wang, Jiachen1; Zhao, Xu1; Zhang, Shuning4; Chen, Tao5; Jia, Runda6; Xiong, Gang3 | |
发表期刊 | JOURNAL OF PROCESS CONTROL |
ISSN | 0959-1524 |
2021-05-01 | |
卷号 | 101页码:11-23 |
通讯作者 | Chu, Fei(chufeizhufei@sina.com) |
摘要 | This paper concerns with the JY-KPLS model based transfer learning for the operation optimization of nonlinear batch processes. Due to problems of data insufficiency and uncertainties in a new nonlinear batch process that has just been put into production, the model-(new) process mismatch is usually inevitable, which is also the main reason for the poor performance of the batch process. To solve this problem, this paper first adopts the JY-KPLS model to capture the behavior of the nonlinear batch process, and takes full advantage of the information in similar batch processes to assist the modeling and operation optimization of a new process. Then, a data selection based batch-to-batch optimization control strategy is proposed in this paper to reduce the adverse effects of this mismatch on the operation of the new batch process. Finally, the feasibility of the proposed method is demonstrated by simulations. (C) 2021 Elsevier Ltd. All rights reserved. |
关键词 | Transfer learning Nonlinear batch process Insufficient data Operation optimization Data selection |
DOI | 10.1016/j.jprocont.2021.03.002 |
关键词[WOS] | PARTIAL LEAST-SQUARES ; ADAPTIVE-CONTROL ; DATA-DRIVEN ; PRODUCT TRANSFER ; QUALITY ; PLS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61973304] ; National Natural Science Foundation of China[61503384] ; National Natural Science Foundation of China[61873049] ; National Natural Science Foundation of China[62073060] ; National Key Research and Development Program of China[2018YFB1702701] ; Natural Science Foundation of Jiangsu Province[BK20191339] ; Selection and Training Project of High-level Talents in the Sixteenth ``Six Talent Peaks'' of Jiangsu Province[DZXX-045] ; Xuzhou science and technology plan project[KC19055] ; Open Subject of State Key Laboratory of Process Automation in Mining Metallurgy[BGRIMM-KZSKL-2019-10] |
项目资助者 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Natural Science Foundation of Jiangsu Province ; Selection and Training Project of High-level Talents in the Sixteenth ``Six Talent Peaks'' of Jiangsu Province ; Xuzhou science and technology plan project ; Open Subject of State Key Laboratory of Process Automation in Mining Metallurgy |
WOS研究方向 | Automation & Control Systems ; Engineering |
WOS类目 | Automation & Control Systems ; Engineering, Chemical |
WOS记录号 | WOS:000641987500002 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 人工智能基础理论 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44513 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Chu, Fei |
作者单位 | 1.China Univ Min & Technol, Underground Space Intelligent Control Engn Res Ct, Sch Informat & Control Engn, Minist Educ, Xuzhou 221116, Jiangsu, Peoples R China 2.Beijing Gen Res Inst Min & Met, State Key Lab Proc Automat Min & Met, Beijing Key Lab Proc Automat Min & Met, Beijing 100160, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Ludong Univ, Sch Informat & Elect Engn, Yantai 264025, Peoples R China 5.Univ Surrey, Dept Chem & Proc Engn, Guildford GU2 7XH, Surrey, England 6.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chu, Fei,Wang, Jiachen,Zhao, Xu,et al. Transfer learning for nonlinear batch process operation optimization[J]. JOURNAL OF PROCESS CONTROL,2021,101:11-23. |
APA | Chu, Fei.,Wang, Jiachen.,Zhao, Xu.,Zhang, Shuning.,Chen, Tao.,...&Xiong, Gang.(2021).Transfer learning for nonlinear batch process operation optimization.JOURNAL OF PROCESS CONTROL,101,11-23. |
MLA | Chu, Fei,et al."Transfer learning for nonlinear batch process operation optimization".JOURNAL OF PROCESS CONTROL 101(2021):11-23. |
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