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Data-driven process decomposition and robust online distributed modelling for large-scale processes
Zhang Shu1; Li Lijuan1; Yao Lijuan1; Yang Shipin1; Zou Tao2
刊名INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
2018
卷号49期号:3页码:449-463
关键词Canonical correlation analysis affinity propagation clustering block-wise RPLS model reduction model-predictive control process control parameter identification
ISSN号0020-7721
DOI10.1080/00207721.2017.1406551
通讯作者Li Lijuan(ljli@njtech.edu.cn)
英文摘要With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.
资助项目National Natural Science Foundation of China[61203072] ; National Natural Science Foundation of China[61403190] ; National Natural Science Foundation of China[61773366] ; Research Innovation Program for College Graduates of Jiangsu Province[KYLX16 0598]
WOS研究方向Automation & Control Systems ; Computer Science ; Operations Research & Management Science
语种英语
出版者TAYLOR & FRANCIS LTD
WOS记录号WOS:000428635000001
资助机构National Natural Science Foundation of China ; Research Innovation Program for College Graduates of Jiangsu Province
内容类型期刊论文
源URL[http://ir.imr.ac.cn/handle/321006/127611]  
专题金属研究所_中国科学院金属研究所
通讯作者Li Lijuan
作者单位1.Nanjing Tech Univ, Coll Elect Engn & Control Sci, Ind Syst & Automat Dept, Nanjing, Jiangsu, Peoples R China
2.Chinese Acad Sci, Shenyang Inst Automat, Ind Control Networks & Syst Dept, Shenyang, Liaoning, Peoples R China
推荐引用方式
GB/T 7714
Zhang Shu,Li Lijuan,Yao Lijuan,et al. Data-driven process decomposition and robust online distributed modelling for large-scale processes[J]. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE,2018,49(3):449-463.
APA Zhang Shu,Li Lijuan,Yao Lijuan,Yang Shipin,&Zou Tao.(2018).Data-driven process decomposition and robust online distributed modelling for large-scale processes.INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE,49(3),449-463.
MLA Zhang Shu,et al."Data-driven process decomposition and robust online distributed modelling for large-scale processes".INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 49.3(2018):449-463.
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