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.
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References
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]
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]
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.
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]
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]
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]
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]
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.
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.
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.
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.
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]
V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.
J. A. K. Suykens, “Nonlinear modeling and support vector machines,” IEEE Instrumentation and Measurement Technology Conference, Budapest, pp. 287–294, May 2001.
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]
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.
H. Wang and D. Hu, “Comparison of SVM and LS-SVM for regression,” IEEE Neural Networks and Brain, Beijing, pp. 279–283, October 2005.
M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Publishing Company, Boston, 1996.
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.
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.
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]
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]
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Recommended by Associate Editor Soohee Han under the direction of Editor Yoshito Ohta. This journal was supported by the National Research Foundation of Korea (NRF- 2017R1A2B1005649).
Tae Young Kim is a Ph.D. student at the department of Chemical Engineering, Hanyang University, Korea. His research interests include Fuel cell process modeling, model predictive control and machine learning.
Beom Seok Kim is a Ph.D. student at the department of Chemical Engineering, Hanyang University, Korea. His research interests include Fuel cell process modeling, control.
Tae Chang Park is a Ph.D. student at the department of Chemical Engineering, Hanyang University, Korea. His research interests include machine learning, model predictive.
Yeong Koo Yeo is a Professor at the Department of Chemical Engineering, Hanyang University, Korea. His research interests include model predictive control, plant modeling, simulation and optimization.
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Kim, T.Y., Kim, B.S., Park, T.C. et al. Development of Predictive Model based Control Scheme for a Molten Carbonate Fuel Cell (MCFC) Process. Int. J. Control Autom. Syst. 16, 791–803 (2018). https://doi.org/10.1007/s12555-016-0234-0
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DOI: https://doi.org/10.1007/s12555-016-0234-0