Structural and parametric identification of soft sensors models for process plants based on robust regression and information criteria
- 作者: Digo G.B.1, Digo N.B.1, Kozlov A.V.2, Samotylova S.A.3, Torgashov A.Y.1
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隶属关系:
- Institute of Automation and Control Processes, Far Eastern Branch
- “OJSC Gazpromneft-Omsk Refinery”
- Far Eastern Federal University
- 期: 卷 78, 编号 4 (2017)
- 页面: 724-731
- 栏目: Automation in Industry
- URL: https://ogarev-online.ru/0005-1179/article/view/150584
- DOI: https://doi.org/10.1134/S0005117917040130
- ID: 150584
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详细
Approach to the solution of a problem of structural and parametrical identification of models of the soft sensors (SS) of technological plants on the basis of robust regression and information criteria is proposed. The robust regression is used for model parameter estimation, and choosing the best model structure in the sense of information criteria. SS is developed by means of the proposed approach which was tested in control systems for optimization of the process operation of gas separation section of fluid catalytic cracking unit of “OJSC Gazpromneft-Omsk Refinery.”
作者简介
G. Digo
Institute of Automation and Control Processes, Far Eastern Branch
Email: torgashov@iacp.dvo.ru
俄罗斯联邦, Vladivostok
N. Digo
Institute of Automation and Control Processes, Far Eastern Branch
Email: torgashov@iacp.dvo.ru
俄罗斯联邦, Vladivostok
A. Kozlov
“OJSC Gazpromneft-Omsk Refinery”
Email: torgashov@iacp.dvo.ru
俄罗斯联邦, Omsk
S. Samotylova
Far Eastern Federal University
Email: torgashov@iacp.dvo.ru
俄罗斯联邦, Vladivostok
A. Torgashov
Institute of Automation and Control Processes, Far Eastern Branch
编辑信件的主要联系方式.
Email: torgashov@iacp.dvo.ru
俄罗斯联邦, Vladivostok
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