Development of Predictive Model based Control Scheme for a Molten Carbonate Fuel Cell (MCFC) Process
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.
KeywordsARMA modeling model predictive control molten carbonate fuel cells rigorous model
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- J. A. K. Suykens, “Nonlinear modeling and support vector machines,” IEEE Instrumentation and Measurement Technology Conference, Budapest, pp. 287–294, May 2001.Google Scholar
- 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
- 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
- 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
- M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Publishing Company, Boston, 1996.Google Scholar
- 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
- 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