Automation and Remote Control

, Volume 78, Issue 4, pp 724–731 | Cite as

Structural and parametric identification of soft sensors models for process plants based on robust regression and information criteria

  • G. B. Digo
  • N. B. Digo
  • A. V. Kozlov
  • S. A. Samotylova
  • A. Yu. Torgashov
Automation in Industry Selected Articles from Avtomatizatsiya v Promyshlennosti
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Abstract

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.”

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References

  1. 1.
    Vlasov, S.S. and Shumihin, A.G., Simulation of the Oil Topping Process for Gasoline Quality Prediction, Vestn. SGTU, 2012, vol. 63, no. 1, pp. 90–94.Google Scholar
  2. 2.
    Olanrewaju, M.J., Huang, B., and Afacan, A., Online Composition Estimation and Experiment Validation of Distillation Processes with Switching Dynamics, Chemical Eng. Sci., 2010, vol. 65, no. 5, pp. 1597–1608.CrossRefGoogle Scholar
  3. 3.
    Chatterjee, T. and Saraf, D.N., On-line Estimation of Product Properties for Crude Distillation Units, J. Process Control, 2004, vol. 14, pp. 61–77.CrossRefGoogle Scholar
  4. 4.
    Holland, P.W. and Welsch, R.E., Robust Regression Using Iteratively Reweighted Least-Squares, Commun. Statis., Theor. Meth., 1977, vol. 6, no. 9, pp. 813–827.CrossRefMATHGoogle Scholar
  5. 5.
    Rawlings, J.O., Pantula, S.G., and Dickey, D.A., Applied Regression Analysis: A Research Tool, New York: Springer-Verlag, 1998.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • G. B. Digo
    • 1
  • N. B. Digo
    • 1
  • A. V. Kozlov
    • 2
  • S. A. Samotylova
    • 3
  • A. Yu. Torgashov
    • 1
  1. 1.Institute of Automation and Control Processes, Far Eastern BranchRussian Academy of SciencesVladivostokRussia
  2. 2.“OJSC Gazpromneft-Omsk Refinery”OmskRussia
  3. 3.Far Eastern Federal UniversityVladivostokRussia

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