Data-driven process decomposition and robust online distributed modelling for large-scale processes

doi:10.1080/00207721.2017.1406551

IMR OpenIR

	Data-driven process decomposition and robust online distributed modelling for large-scale processes
	Zhang Shu 1; Li Lijuan 1; Yao Lijuan 1; Yang Shipin 1; Zou Tao 2
通讯作者	Li Lijuan(ljli@njtech.edu.cn)
	2018
发表期刊	INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
ISSN	0020-7721
卷号	49 期号:3 页码:449-463
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.
关键词	Canonical correlation analysis affinity propagation clustering block-wise RPLS model reduction model-predictive control process control parameter identification
资助者	National Natural Science Foundation of China ; Research Innovation Program for College Graduates of Jiangsu Province
DOI	10.1080/00207721.2017.1406551
收录类别	SCI
语种	英语
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
WOS类目	Automation & Control Systems ; Computer Science, Theory & Methods ; Operations Research & Management Science
WOS记录号	WOS:000428635000001
出版者	TAYLOR & FRANCIS LTD
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/127610
专题	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.