Data-driven process decomposition and robust online distributed modelling for large-scale processes
文献类型：期刊论文
| 作者 | Zhang Shu1; Li Lijuan1; Yao Lijuan1; Yang Shipin1; Zou Tao2 |
| 刊名 | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
 |
| 出版日期 | 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 |
| DOI | 10.1080/00207721.2017.1406551 |
| 通讯作者 | Li Lijuan(ljli@njtech.edu.cn) |
| 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研究方向 | Automation & Control Systems ; Computer Science ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000428635000001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China ; Research Innovation Program for College Graduates of Jiangsu Province |
| 源URL | [http://ir.imr.ac.cn/handle/321006/127609]  |
| 专题 | 金属研究所_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. |
入库方式： OAI收割
来源：金属研究所
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