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Data-driven process decomposition and robust online distributed modelling for large-scale processes
Zhang, Shu; Li LJ(李丽娟); Yao LJ(姚莉娟); Yang SP(杨世品); Zou T(邹涛)
Department工业控制网络与系统研究室
Source PublicationInternational Journal of Systems Science
ISSN0020-7721
2018
Volume49Issue:3Pages:449-463
Indexed BySCI ; EI
EI Accession number20175104554528
WOS IDWOS:000428635000001
Contribution Rank2
Funding OrganizationNational Natural Science Foundation of China ; Research Innovation Program for College Graduates of Jiangsu Province
KeywordCanonical Correlation Analysis Affinity Propagation Clustering Block-wise Rpls Model Reduction Model-predictive Control Process Control Parameter Identification
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.
Language英语
WOS SubjectAutomation & Control Systems ; Computer Science, Theory & Methods ; Operations Research & Management Science
WOS KeywordEASTMAN CHALLENGE PROCESS ; AFFINITY PROPAGATION ; SYSTEMS ; DESIGN ; ALGORITHM ; DIAGNOSIS ; TOPOLOGY ; STRATEGY ; NETWORK ; GAIN
WOS Research AreaAutomation & Control Systems ; Computer Science ; Operations Research & Management Science
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]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21466
Collection工业控制网络与系统研究室
Corresponding AuthorLi LJ(李丽娟)
Affiliation1.Industrial System and Automation Department, College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, China
2.Industrial Control Networks and Systems Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Zhang, Shu,Li LJ,Yao LJ,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 LJ,Yao LJ,Yang SP,&Zou T.(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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