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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
Corresponding AuthorLi Lijuan(ljli@njtech.edu.cn)
2018
Source PublicationINTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
ISSN0020-7721
Volume49Issue:3Pages:449-463
AbstractWith 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.
KeywordCanonical correlation analysis affinity propagation clustering block-wise RPLS model reduction model-predictive control process control parameter identification
Funding OrganizationNational Natural Science Foundation of China ; Research Innovation Program for College Graduates of Jiangsu Province
DOI10.1080/00207721.2017.1406551
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaAutomation & Control Systems ; Computer Science ; Operations Research & Management Science
WOS SubjectAutomation & Control Systems ; Computer Science, Theory & Methods ; Operations Research & Management Science
WOS IDWOS:000428635000001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/127612
Collection中国科学院金属研究所
Corresponding AuthorLi Lijuan
Affiliation1.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
Recommended Citation
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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