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

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	Data-driven process decomposition and robust online distributed modelling for large-scale processes
	Zou T(邹涛); Li LJ(李丽娟); Yao LJ(姚莉娟); Zhang, Shu ; Yang SP(杨世品)
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
产权排序	2
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关键词	EASTMAN CHALLENGE PROCESS ; AFFINITY PROPAGATION ; SYSTEMS ; DESIGN ; ALGORITHM ; DIAGNOSIS ; TOPOLOGY ; STRATEGY ; NETWORK ; GAIN
WOS研究方向	Automation & Control Systems ; Computer Science ; Operations Research & Management Science
语种	英语
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.sia.cn/handle/173321/21466]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Li LJ(李丽娟)
作者单位	1.Industrial Control Networks and Systems Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 2.Industrial System and Automation Department, College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, China
推荐引用方式 GB/T 7714	Zou T,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	Zou T,Li LJ,Yao LJ,Zhang, Shu,&Yang SP.(2018).Data-driven process decomposition and robust online distributed modelling for large-scale processes.International Journal of Systems Science,49(3),449-463.
MLA	Zou T,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.