Development of Predictive Model based Control Scheme for a Molten Carbonate Fuel Cell (MCFC) Process

  • Tae Young Kim
  • Beom Seok Kim
  • Tae Chang Park
  • Yeong Koo Yeo
Regular Paper Control Theory and Applications
  • 12 Downloads

Abstract

To improve availability and performance of fuel cells, the operating temperature of a molten carbonate fuel cells (MCFC) stack should be strictly maintained within a specified operation range and an efficient control technique should be employed to meet this objective. While most of modern control strategies are based on process models, many existing models for a MCFC process are not ready to be applied in synthesis and operation of control systems. In this study, auto-regressive moving average (ARMA) model, least square support vector machine (LSSVM) model and artificial neural network (ANN) model for the MCFC system are developed based on input-output operating data. Among these models, the ARMA model showed the best tracking performance. A model predictive control (MPC) method for the operation of a MCFC process is developed based on the proposed ARMA model. For the purpose of comparison, a MPC scheme based on the linearized rigorous model for a MCFC process is developed. Results of numerical simulations show that MPC based on the ARMA model exhibits better control performance than that based on the linearized rigorous model.

Keywords

ARMA modeling model predictive control molten carbonate fuel cells rigorous model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    W. He, “A dynamic performance of a molten carbonate fuel cell in power generation system,” Journal of Power Sources, vol. 52, no. 2, pp. 179–184, June 1994. [click]MathSciNetCrossRefGoogle Scholar
  2. [2]
    W. He, “Three-dimensional simulation of a molten carbonate fuel cell stack using computational fluid dynamic techniques,” Journal of Power Sources, vol. 55, no. 1, pp. 25–32, May 1995. [click]CrossRefGoogle Scholar
  3. [3]
    J. B. Ernest, H. Ghezel-Ayagh, and A. K. Kush, “Dynamic simulation of a direct carbonate fuel cell power plant,” Proc. of the Fuel Cell Seminar, Orlando, pp. 75–78, December 1996.Google Scholar
  4. [4]
    M. D. Lukas, K. Y. Lee, and H. Ghezel-Ayagh, “Development of a stack simulation model for control study on direct reforming molten carbonate fuel cell power plant,” IEEE Transactions on Energy Conversion, vol. 14, no. 4, pp. 1651–1657, December 1999. [click]CrossRefGoogle Scholar
  5. [5]
    M. Sheng, M. Mangold, and A. Kienle, “A strategy for the spatial temperature control of a molten carbonate fuel cell system,” Journal of Power Sources, vol. 162, no. 2, pp. 1213–1219, November 2006. [click]CrossRefGoogle Scholar
  6. [6]
    C. Shen, G.-Y. Cao, and X.-J. Zhu, “Nonlinear modeling of MCFC stack based on RBF neural networks identification,” Simulation Modeling Practice and Theory, vol. 10, no. 1-2, pp. 109–119, October 2002. [click]CrossRefMATHGoogle Scholar
  7. [7]
    C. Shen, G.-Y. Cao, X.-J. Zhu, and X.-J. Sun, “Nonlinear modeling and adaptive fuzzy control of MCFC stack,” Journal of Process Control, vol. 12, no. 8, pp. 831–839, December 2002. [click]CrossRefGoogle Scholar
  8. [8]
    M. Farooque, H. C. Maru, and B. Baker, “Direct carbonate fuel cell power plant design at ERC,” Proc. of the 28th Intersociety Energy Conversion Engineering Conference, Atlanta, GA,USA, pp. 181–1193, 1993.Google Scholar
  9. [9]
    M. D. Lukas, K. Y. Lee, and H. Ghezel-Ayagh, “Modeling and cycling control of carbonate fuel cell power plants,” Control Engineering Practice, vol. 10, no. 2, pp. 197–206, February 2002.CrossRefGoogle Scholar
  10. [10]
    M. D. Lukas, K. Y. Lee, and H. Ghezel-Ayagh, “Reducedorder dynamic model of carbonate fuel cell system for distributed generation control,” Proc. of the IEEE Power Engineering Society Summer Meeting, Seattle, WA,USA, pp. 1793–1797, 2000.Google Scholar
  11. [11]
    M. D. Lukas and K. Y. Lee, “Model-based analysis for the control of molten carbonate fuel cell systems,” Fuel Cells, vol. 5, no. 1, pp. 115–125, August 2004.CrossRefGoogle Scholar
  12. [12]
    S. E. Said and D. A. Dickey, “Testing for unit roots in autoregressive-moving average models of unknown order,” Biometrika, vol. 71, no. 3, pp. 599–607, December 1984. [click]MathSciNetCrossRefMATHGoogle Scholar
  13. [13]
    V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.CrossRefMATHGoogle Scholar
  14. [14]
    N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.MATHGoogle Scholar
  15. [15]
    J. A. K. Suykens, “Nonlinear modeling and support vector machines,” IEEE Instrumentation and Measurement Technology Conference, Budapest, pp. 287–294, May 2001.Google Scholar
  16. [16]
    J. A. K. Suykens, L. Lukas, and J. Vandewalle, “Sparse approximation using least squares support vector machines,” Proc. of IEEE International Symposium on Circuits and Systems, Geneva, pp. 757–760, May 2000. [click]Google Scholar
  17. [17]
    P. Samui, “Application of least square support vector machine (LSSVM) for determination of evaporation losses in reservoirs,” Scientific Research, vol. 3, pp. 431–434, December 2011.Google Scholar
  18. [18]
    H. Wang and D. Hu, “Comparison of SVM and LS-SVM for regression,” IEEE Neural Networks and Brain, Beijing, pp. 279–283, October 2005.Google Scholar
  19. [19]
    M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Publishing Company, Boston, 1996.Google Scholar
  20. [20]
    Y. D. Tian, X. J. Zhu, and G. Y. Cao, “Proton exchange membrane fuel cells modeling based on artificial neural networks,” Journal of University of Science and Technology Beijing, vol. 12, no. 1, pp. 72–77, 2005.Google Scholar
  21. [21]
    J. H. Cho, H. Y. Kim, K. S. Lee, S. K. Yook, and W. H. Jung, “Delta-operator-based adaptive MPC for an MCFC system,” Proc. of the 11th International Conference on Control, Automation and Systems, Gyeonggi-do, pp. 1801–1806, December 2011.Google Scholar
  22. [22]
    S. Bououden, M. Chadli, L. Zhang, and T. Yang “Constrained model predictive control for time-varying delay systems: Application to an active car suspension,” International Journal of Control, Automation, and Systems, vol. 14, no. 1, pp. 51–58, February 2016. [click]CrossRefGoogle Scholar
  23. [23]
    K. S. Oh, J. H. Seo, J. G. Kim, and K. S. Yi, “MPC-based approach to optimized steering for minimum turning radius and efficient steering of multi-axle crane,” International Journal of Control, Automation, and Systems, vol. 15, no. 4, pp. 1799–1813, August 2017. [click]CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Tae Young Kim
    • 1
  • Beom Seok Kim
    • 1
  • Tae Chang Park
    • 1
  • Yeong Koo Yeo
    • 1
  1. 1.Department of Chemical EngineeringHanyang UniversitySeoulKorea

Personalised recommendations