| Data-driven process decomposition and robust online distributed modelling for large-scale processes | |
| Zhang Shu1; Li Lijuan1; Yao Lijuan1; Yang Shipin1; Zou Tao2 | |
| Corresponding Author | Li Lijuan(ljli@njtech.edu.cn) |
| 2018 | |
| Source Publication | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
 |
| ISSN | 0020-7721 |
| Volume | 49Issue:3Pages:449-463 |
| Abstract | 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. |
| Keyword | Canonical correlation analysis affinity propagation clustering block-wise RPLS model reduction model-predictive control process control parameter identification |
| Funding Organization | National Natural Science Foundation of China ; Research Innovation Program for College Graduates of Jiangsu Province |
| DOI | 10.1080/00207721.2017.1406551 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Automation & Control Systems ; Computer Science ; Operations Research & Management Science |
| WOS Subject | Automation & Control Systems ; Computer Science, Theory & Methods ; Operations Research & Management Science |
| WOS ID | WOS:000428635000001 |
| Publisher | TAYLOR & FRANCIS LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/127612 |
| Collection | 中国科学院金属研究所 |
| Corresponding Author | Li Lijuan |
| Affiliation | 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 |
| 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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