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Received April 17, 2017
Accepted October 2, 2017
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Model-based control of a molten carbonate fuel cell (MCFC) process
Department of Chemical Engineering, Hanyang University, Seoul 04763, Korea
Korean Journal of Chemical Engineering, January 2018, 35(1), 118-128(11), 10.1007/s11814-017-0274-z
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Abstract
To improve availability and performance of fuel cells, the operating temperature of 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 modern control strategies are based on process models, many existing models of MCFC are not ready to be applied in synthesis and operation of control systems. In this study, we developed an auto-regressive moving average (ARMA) model and machine learning methods of least squares support vector machine (LS-SVM), artificial neural network (ANN) and partial least squares (PLS) for the MCFC system based on input-output operating data. The ARMA model showed the best tracking performance. A model predictive control method for the operation of MCFC system was developed based on the proposed ARMA model. The control performance of the proposed MPC methods was compared with that of conventional controllers using numerical simulations performed on various process models including an MCFC process. Numerical results show that ARMA model based control provides improved control performance compared to other control methods.
References
He W, J. Power Sources, 52, 179 (1994)
He W, J. Power Sources, 55, 25 (1995)
Ernest JB, Ghezel-Ayagh H, Kush AK, Proceedings of the 1996 fuel cell seminar, Orlando, FL, U.S.A., 75 (1996).
Lukas MD, Lee KY, Ghezel-Ayagh H, IEEE Trans. Energy Convers., 14(4), 1651 (1999)
Comite A, Costa C, Felice RD, Pagliai P, Vitiello D, Korean J. Chem. Eng., 32(2), 239 (2015)
Gu C, Zhang C, Zhang X, Ding N, Li B, Yuan Z, Korean J. Chem. Eng., 34(1), 20 (2017)
Sheng M, Mangold M, Kienle A, J. Power Sources, 162(2), 1213 (2006)
Shen C, Cao GY, Zhu XJ, Simulation Modeling Practice Theory, 10, 109 (2002)
Shen C, Cao GY, Zhu XJ, Sun XJ, J. Process Control, 12(8), 831 (2002)
Farooque M, Maru HC, Baker B, Atlanta, GA, U.S.A., 181 (1993).
Lukas MD, Lee KY, Ghezel-Ayagh H, Control Engineering Practice, 197 (2002).
Lukas MD, Lee KY, Ghezel-Ayagh H, Seattle, WA, U.S.A., 1793 (2000).
Lukas MD, Lee KY, Fuel Cells, 5(1), 115 (2004)
Murshed AKMM, Huang B, Nandakumar K, J. Power Sources, 163(2), 830 (2007)
Hirschenhofer JH, Stauffer DB, Engleman RR, Klett MG, Fuel Cell Handbook, U.S. Department of Energy (1998).
Said SE, David D, Dickey A, Biometrika, 71(3), 599 (1984)
Monson H, Statistical Digital Signal Processing and Modeling, Jone Wiley & Sons., New York, U.S.A., 541 (1996).
Suykens JAK, Proceeding of IEEE Instrumentation and measurement technology, Budapest, Hungary, 287 (2001).
Samui P, Scientific Research, 431 (2011).
Wang H, Hu D, IEEE, 279 (2005).
Hagan MT, Demuth HB, Beale MH, Boston, MA: PWS Publishing Company (1996).
Tian YD, Zhu XJ, Cao GY, J. University of Science and Technology Beijing, 12, 72 (2005)
Thamizmani S, Narasimman S, Int. J. Emerging Res. in Management Technology, 3(4), 66 (2014)
Lee JK, Park SW, Korean J. Chem. Eng., 8(4), 195 (1991)
He W, J. Power Sources, 55, 25 (1995)
Ernest JB, Ghezel-Ayagh H, Kush AK, Proceedings of the 1996 fuel cell seminar, Orlando, FL, U.S.A., 75 (1996).
Lukas MD, Lee KY, Ghezel-Ayagh H, IEEE Trans. Energy Convers., 14(4), 1651 (1999)
Comite A, Costa C, Felice RD, Pagliai P, Vitiello D, Korean J. Chem. Eng., 32(2), 239 (2015)
Gu C, Zhang C, Zhang X, Ding N, Li B, Yuan Z, Korean J. Chem. Eng., 34(1), 20 (2017)
Sheng M, Mangold M, Kienle A, J. Power Sources, 162(2), 1213 (2006)
Shen C, Cao GY, Zhu XJ, Simulation Modeling Practice Theory, 10, 109 (2002)
Shen C, Cao GY, Zhu XJ, Sun XJ, J. Process Control, 12(8), 831 (2002)
Farooque M, Maru HC, Baker B, Atlanta, GA, U.S.A., 181 (1993).
Lukas MD, Lee KY, Ghezel-Ayagh H, Control Engineering Practice, 197 (2002).
Lukas MD, Lee KY, Ghezel-Ayagh H, Seattle, WA, U.S.A., 1793 (2000).
Lukas MD, Lee KY, Fuel Cells, 5(1), 115 (2004)
Murshed AKMM, Huang B, Nandakumar K, J. Power Sources, 163(2), 830 (2007)
Hirschenhofer JH, Stauffer DB, Engleman RR, Klett MG, Fuel Cell Handbook, U.S. Department of Energy (1998).
Said SE, David D, Dickey A, Biometrika, 71(3), 599 (1984)
Monson H, Statistical Digital Signal Processing and Modeling, Jone Wiley & Sons., New York, U.S.A., 541 (1996).
Suykens JAK, Proceeding of IEEE Instrumentation and measurement technology, Budapest, Hungary, 287 (2001).
Samui P, Scientific Research, 431 (2011).
Wang H, Hu D, IEEE, 279 (2005).
Hagan MT, Demuth HB, Beale MH, Boston, MA: PWS Publishing Company (1996).
Tian YD, Zhu XJ, Cao GY, J. University of Science and Technology Beijing, 12, 72 (2005)
Thamizmani S, Narasimman S, Int. J. Emerging Res. in Management Technology, 3(4), 66 (2014)
Lee JK, Park SW, Korean J. Chem. Eng., 8(4), 195 (1991)
