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A ML-combined closed-loop identification method for thermodynamic process

Published: 16 April 2024 Publication History

Abstract

To deal with the adverse effects of multi-input and multi-disturbance on thermal plant model identification, a thermal closed-loop process identification method integrating machine learning is proposed. Firstly, the optimal method of identification data based on feature classification machine learning is used to select the historical operation data suitable for identification. The method establishes the feature construction rules of historical operation data, reduces the dimension of data and can better represent the dynamic information. The random forest is used to establish the identification data classification rule model, so as to obtain high prediction accuracy of identification model. Secondly, an identification process integrating input variable selection, model order determination and unbiased parameter estimation is proposed. Among them, the variance expansion factor method and the variable projection analysis method of partial least square regression are used to select the input variables of the identification model, and then the model parameters are unbiasedly identified based on the asymptotic identification method. The ML-combined identification method is applied to the deaerator water level system of thermal power unit, and its reliability and accuracy are verified by closed-loop simulation.

References

[1]
Wang Q, Pan L, Lee K Y, Deep-learning modeling and control optimization framework for intelligent thermal power plants: A practice on superheated steam temperature[J]. Korean Journal of Chemical Engineering, 2021, 38(10): 1983-2002.
[2]
Peretzki D, Isaksson A J, Bittencourt A C, Data Mining of Historic Data for Process Identification[C] Proc. of the 2011 Aiche Meeting. 2011.
[3]
Shardt Y A W, Huang B. Closed-loop identification condition for ARMAX models using routine operating data [J]. Automatica, 2011, 47(7):1534-1537.
[4]
Shardt Y A W, Huang B. Closed-loop identification with routine operating data: Effect of time delay and sampling time [J], Journal of Process Control, 2011, 21(7):997-1010.
[5]
Breiman L. Bagging Predictors[J]. Machine learning, 1996, 24(2): 123-140.
[6]
Zhang L, Meng X.L. Data Center Cooling System Temperature Prediction Based on Bayesian Random forest [J]. Automation and Instrumentation, 2023, No.283 (05): 6-9.
[7]
Partha Pratim Roy, Kunal Roy. On Some Aspects of Variable Selection for Partial Least Squares Regression Models[J]. Molecular Informatics, 2010, 27(3):302-313.
[8]
Gustavsson I, Ljung L, T. Söderström. Identification of Processes in Closed Loop-Identifiability and Accuracy Aspects[J]. 1977.13(1):59-75.
[9]
Lung L. System Identification: Theory for the User (2nd Edition )[M]. Prentice-Hall, Englewood Cliffs, N.J,1999.
[10]
Zhu Y, Butoyi F. Case studies on closed-loop identification for MPC [J]. Control Engineering Practice, 2002, 10(4): 404-417.
[11]
Zhu Y.C Multivariable system identification for process control [M] Changsha: National University of Defense Science and Technology Press, 2005
[12]
Zhu Y. Use of error criteria in identification for control [J]. IFAC Proceedings Volumes, 2000, 33(15):307-312.
[13]
Richalet J. Industrial applications of model based predictive control [J]. Automatica, 1274. The role of system1993. 29(5):1251

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  1. A ML-combined closed-loop identification method for thermodynamic process

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    ICMLCA '23: Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application
    October 2023
    1065 pages
    ISBN:9798400709449
    DOI:10.1145/3650215
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 16 April 2024

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