Estimation of feedwater heater parameters based on a grey-box approach

Tomasz Barszcz; Piotr Czop

International Journal of Applied Mathematics and Computer Science (2011)

  • Volume: 21, Issue: 4, page 703-715
  • ISSN: 1641-876X

Abstract

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The first-principle modeling of a feedwater heater operating in a coal-fired power unit is presented, along with a theoretical discussion concerning its structural simplifications, parameter estimation, and dynamical validation. The model is a part of the component library of modeling environments, called the Virtual Power Plant (VPP). The main purpose of the VPP is simulation of power generation installations intended for early warning diagnostic applications. The model was developed in the Matlab/Simulink package. There are two common problems associated with the modeling of dynamic systems. If an analytical model is chosen, it is very costly to determine all model parameters and that often prevents this approach from being used. If a data model is chosen, one does not have a clear interpretation of the model parameters. The paper uses the so-called grey-box approach, which combines first-principle and data-driven models. The model is represented by nonlinear state-space equations with geometrical and physical parameters deduced from the available documentation of a feedwater heater, as well as adjustable phenomenological parameters (i.e., heat transfer coefficients) that are estimated from measurement data. The paper presents the background of the method, its implementation in the Matlab/Simulink environment, the results of parameter estimation, and a discussion concerning the accuracy of the method.

How to cite

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Tomasz Barszcz, and Piotr Czop. "Estimation of feedwater heater parameters based on a grey-box approach." International Journal of Applied Mathematics and Computer Science 21.4 (2011): 703-715. <http://eudml.org/doc/208082>.

@article{TomaszBarszcz2011,
abstract = {The first-principle modeling of a feedwater heater operating in a coal-fired power unit is presented, along with a theoretical discussion concerning its structural simplifications, parameter estimation, and dynamical validation. The model is a part of the component library of modeling environments, called the Virtual Power Plant (VPP). The main purpose of the VPP is simulation of power generation installations intended for early warning diagnostic applications. The model was developed in the Matlab/Simulink package. There are two common problems associated with the modeling of dynamic systems. If an analytical model is chosen, it is very costly to determine all model parameters and that often prevents this approach from being used. If a data model is chosen, one does not have a clear interpretation of the model parameters. The paper uses the so-called grey-box approach, which combines first-principle and data-driven models. The model is represented by nonlinear state-space equations with geometrical and physical parameters deduced from the available documentation of a feedwater heater, as well as adjustable phenomenological parameters (i.e., heat transfer coefficients) that are estimated from measurement data. The paper presents the background of the method, its implementation in the Matlab/Simulink environment, the results of parameter estimation, and a discussion concerning the accuracy of the method.},
author = {Tomasz Barszcz, Piotr Czop},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {first-principle model; system identification; heater; heat exchanger; grey-box},
language = {eng},
number = {4},
pages = {703-715},
title = {Estimation of feedwater heater parameters based on a grey-box approach},
url = {http://eudml.org/doc/208082},
volume = {21},
year = {2011},
}

TY - JOUR
AU - Tomasz Barszcz
AU - Piotr Czop
TI - Estimation of feedwater heater parameters based on a grey-box approach
JO - International Journal of Applied Mathematics and Computer Science
PY - 2011
VL - 21
IS - 4
SP - 703
EP - 715
AB - The first-principle modeling of a feedwater heater operating in a coal-fired power unit is presented, along with a theoretical discussion concerning its structural simplifications, parameter estimation, and dynamical validation. The model is a part of the component library of modeling environments, called the Virtual Power Plant (VPP). The main purpose of the VPP is simulation of power generation installations intended for early warning diagnostic applications. The model was developed in the Matlab/Simulink package. There are two common problems associated with the modeling of dynamic systems. If an analytical model is chosen, it is very costly to determine all model parameters and that often prevents this approach from being used. If a data model is chosen, one does not have a clear interpretation of the model parameters. The paper uses the so-called grey-box approach, which combines first-principle and data-driven models. The model is represented by nonlinear state-space equations with geometrical and physical parameters deduced from the available documentation of a feedwater heater, as well as adjustable phenomenological parameters (i.e., heat transfer coefficients) that are estimated from measurement data. The paper presents the background of the method, its implementation in the Matlab/Simulink environment, the results of parameter estimation, and a discussion concerning the accuracy of the method.
LA - eng
KW - first-principle model; system identification; heater; heat exchanger; grey-box
UR - http://eudml.org/doc/208082
ER -

References

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  1. Barszcz, T. (2007). Virtual power plant in condition monitoring of power generation unit, Proceedings of the 20th International Congress on Condition Monitoring and Diagnostic Engineering Management, Faro, Portugal, pp. 1-10. 
  2. Barszcz, T. and Czop, P. (2007). Methodologies and Applications of Virtual Power Plant: New Environment for Power Plant Elements Modeling, Institute of Sustainable Technologies, Radom. 
  3. Bohlin, T. (2006). Practical Grey-box Process Identification: Theory and Applications (Advances in Industrial Control), Springer-Verlag, London. Zbl1122.93059
  4. Bonivento, C., Castaldi, P. and Mirotta, D. (2001). Predictive control vs. pid control of an industrial heat exchanger, Proceeding of the 9th IEEE Mediterranean Conference on Control and Automation, Dubrovnik, Croatia, pp. 27-29. 
  5. Bradatsch, T., Gühmann, C., Röpke, K., Schneider, C. and Filbert, D. (1993). Analytical redundancy methods for diagnosing electric motors, Applied Mathematics and Computer Science 3(3): 461-486. Zbl0800.93070
  6. Flynn, D. (2000). Thermal Power Plants. Simulation and Control, Institution of Electrical Engineers, London. 
  7. Funkquist, J. (1997). Grey-box identification of a continuous digester a distributed parameter process, Control Engineering Practice (5): 919-930. 
  8. Gewitz, A. (2005). EKF-based parameter estimation for a lumped, single plate heat exchanger, www.cespr.fsu.edu/people/myh/reu_ppt05. 
  9. Hangos, K.M. and Cameron, I. (2001). Process Modeling and Model Analysis, Academic Press, London. 
  10. Korbicz, J., Kościelny, J., Kowalczuk, Z. and Cholewa, W. (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer-Verlag, Berlin. Zbl1074.93004
  11. Korbicz, J., Uciński, D., Pieczyński, A. and Marczewska, G. (1993). Knowledge-based fault detection and isolation system for power plant simulator, Applied Mathematics and Computer Science 3(3): 613-630. 
  12. Li, K. and Thompson, S. (2001). Fundamental grey-box modelling. Proceedings of the European Control Conference, Oporto, Portugal, pp. 3648-3653. 
  13. Liu, Y. (2005). Grey-box Identification of Distributed Parameter Systems, Ph.D. thesis, Signals, Sensors and Systems, KTH, Stockholm. 
  14. Liu, Y. and Jacobsen, E. W. (2004). Error detection and control in grey-box modelling of distributed parameter processes, Proceedings of IFAC DYCOPS7, Boston, MA, USA, pp. 841-846. 
  15. Ljung, L. (1999). System Identification-Theory for the User, Prentice Hall, Upper Saddle River NJ. Zbl0615.93004
  16. Mathworks (2007). Matlab System Identification Toolbox Guide, The Mathworks Inc., Natick, MA. 
  17. Patton, R.J., Frank, P.M. and Clark, R.N. (2000). Issues of Fault Diagnosis for Dynamic Systems, Springer-Verlag, London. 
  18. Pearson, R.K. and Pottmann, M. (2000). Gray-box identification of block-oriented nonlinear models, Journal of Process Control 10(4): 301-315. 
  19. Sierociuk, D. and Dzieliński, A. (2006). Fractional Kalman filter algorithm for the states, parameters and order of fractional system estimation, International Journal of Applied Mathematics and Computer Science 16(1): 129-140. Zbl1334.93172
  20. Sohlberg, B. and Jacobsen, E.W. (2008). Grey box modeling - Branches and experience, Proceedings of the 17th IFAC World Congress, Seoul, Korea, pp. 11415-11420. 
  21. Upadhyaya, B.R. and Hines, J.W. (2004). On-Line Monitoring and Diagnostics of the Integrity of Nuclear Plant Steam Generators and Heat Exchangers, Report No. DE-FG07-01ID14114/UTNE07, NEER Grant No. DE-FG07-01ID14114, www.osti.gov/bridge/servlets/purl/832717-6tYnaS/native/. 
  22. Tulleken, H.J. (1993). Grey-box modelling and identification using physical knowledge and Bayesian techniques, Automatica 29(2): 285-308. Zbl0800.93080
  23. Weyer, E., Szederkenyi, G. and Hangos, K.M. (2000). Grey box fault detection of heat exchangers, Control Engineering Practice 8(2): 121-131. 

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