A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

doi:10.1016/j.cjche.2024.09.004

中国化学工程学报 ›› 2024, Vol. 76 ›› Issue (12): 251-263.DOI: 10.1016/j.cjche.2024.09.004

A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

Haokun Xiong, Lei Xie, Cheng Hu, Hongye Su

State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

收稿日期:2023-11-07 修回日期:2024-09-04 接受日期:2024-09-23 出版日期:2024-12-28 发布日期:2024-09-30
通讯作者: Lei Xie,E-mail:leix@iipc.zju.edu.cn
基金资助:
This work was supported by 2022 Zhejiang Provincial Science and Technology Plan Project (2022C01035).

A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

Haokun Xiong, Lei Xie, Cheng Hu, Hongye Su

State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Received:2023-11-07 Revised:2024-09-04 Accepted:2024-09-23 Online:2024-12-28 Published:2024-09-30
Contact: Lei Xie,E-mail:leix@iipc.zju.edu.cn
Supported by:
This work was supported by 2022 Zhejiang Provincial Science and Technology Plan Project (2022C01035).

摘要/Abstract

摘要： Proton exchange membrane (PEM) electrolyzer have attracted increasing attention from the industrial and researchers in recent years due to its excellent hydrogen production performance. Developing accurate models to predict their performance is crucial for promoting and accelerating the design and optimization of electrolysis systems. This work developed a Koopman model predictive control (MPC) method incorporating fuzzy compensation for regulating the anode and cathode pressures in a PEM electrolyzer. A PEM electrolyzer is then built to study pressure control and provide experimental data for the identification of the Koopman linear predictor. The identified linear predictors are used to design the Koopman MPC. In addition, the developed fuzzy compensator can effectively solve the Koopman MPC model mismatch problem. The effectiveness of the proposed method is verified through the hydrogen production process in PEM simulation.

关键词: Hydrogen production, PEM electrolyzer, Nonlinear control, Model predictive control, Koopman operator, Fuzzy logic system

Abstract: Proton exchange membrane (PEM) electrolyzer have attracted increasing attention from the industrial and researchers in recent years due to its excellent hydrogen production performance. Developing accurate models to predict their performance is crucial for promoting and accelerating the design and optimization of electrolysis systems. This work developed a Koopman model predictive control (MPC) method incorporating fuzzy compensation for regulating the anode and cathode pressures in a PEM electrolyzer. A PEM electrolyzer is then built to study pressure control and provide experimental data for the identification of the Koopman linear predictor. The identified linear predictors are used to design the Koopman MPC. In addition, the developed fuzzy compensator can effectively solve the Koopman MPC model mismatch problem. The effectiveness of the proposed method is verified through the hydrogen production process in PEM simulation.

Key words: Hydrogen production, PEM electrolyzer, Nonlinear control, Model predictive control, Koopman operator, Fuzzy logic system

Haokun Xiong, Lei Xie, Cheng Hu, Hongye Su. A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer[J]. 中国化学工程学报, 2024, 76(12): 251-263.

参考文献

[1] W. Lubitz, W. Tumas, Hydrogen: an overview, Chem. Rev. 107 (10) (2007) 3900-3903.
[2] S.A. Grigoriev, V.N. Fateev, D.G. Bessarabov, P. Millet, Current status, research trends, and challenges in water electrolysis science and technology, Int. J. Hydrog. Energy 45 (49) (2020) 26036-26058.
[3] J. Koponen, A. Kosonen, V. Ruuskanen, K. Huoman, M. Niemela, J. Ahola, Control and energy efficiency of PEM water electrolyzers in renewable energy systems, Int. J. Hydrog. Energy 42 (50) (2017) 29648-29660.
[4] P. Millet, R. Ngameni, S.A. Grigoriev, V.N. Fateev, Scientific and engineering issues related to PEM technology: water electrolysers, fuel cells and unitized regenerative systems, Int. J. Hydrog. Energy 36 (6) (2011) 4156-4163.
[5] G. Correa, P. Marocco, P. Munoz, T. Falaguerra, D. Ferrero, M. Santarelli, Pressurized PEM water electrolysis: Dynamic modelling focusing on the cathode side, Int. J. Hydrog. Energy 47 (7) (2022) 4315-4327.
[6] B. Han, S.M. Steen III, J.K. Mo, F.Y. Zhang, Electrochemical performance modeling of a proton exchange membrane electrolyzer cell for hydrogen energy, Int. J. Hydrog. Energy 40 (22) (2015) 7006-7016.
[7] H. Gorgun, Dynamic modelling of a proton exchange membrane (PEM) electrolyzer, Int. J. Hydrog. Energy 31 (1) (2006) 29-38.
[8] Z. Abdin, C.J. Webb, E.M. Gray, Modelling and simulation of a proton exchange membrane (PEM) electrolyser cell, Int. J. Hydrog. Energy 40 (39) (2015) 13243-13257.
[9] N.V. Dale, M.D. Mann, H. Salehfar, Semiempirical model based on thermodynamic principles for determining 6kW proton exchange membrane electrolyzer stack characteristics, J. Power Sources 185 (2) (2008) 1348-1353.
[10] R. Datta, D.J. Martino, Y. Dong, P. Choi. Modeling of PEM Water Electrolyzer. PEM Electrolysis for Hydrogen Production-Principles and Applications,CRC Press,Boca Raton, FL, USA,2016.
[11] S. Dutta, S. Shimpalee, J.W. Van Zee, Numerical prediction of mass-exchange between cathode and anode channels in a PEM fuel cell, Int. J. Heat Mass Transf. 44 (11) (2001) 2029-2042.
[12] C. Marr, X. Li, An engineering model of proton exchange membrane fuel cell performance, ARI Int. J. Phys. Eng. Sci. 50 (4) (1997) 190-200.
[13] T. Yigit, O.F. Selamet, Mathematical modeling and dynamic Simulink simulation of high-pressure PEM electrolyzer system, Int. J. Hydrog. Energy 41 (32) (2016) 13901-13914.
[14] A. Tabanjat, M. Becherif, M. Emziane, D. Hissel, H.S. Ramadan, B. Mahmah, Fuzzy logic-based water heating control methodology for the efficiency enhancement of hybrid PV-PEM electrolyser systems, Int. J. Hydrog. Energy 40 (5) (2015) 2149-2161.
[15] J.T. Pukrushpan, huei Peng, A.G. Stefanopoulou, Control-oriented modeling and analysis for automotive fuel cell systems, J. Dyn. Syst. Meas. Contr. 126 (1) (2004) 14-25.
[16] C. Kunusch, P.F. Puleston, M.A. Mayosky, J. Riera, Sliding mode strategy for PEM fuel cells stacks breathing control using a super-twisting algorithm, IEEE Trans. Contr. Syst. Technol. 17 (1) (2009) 167-174.
[17] I. Matraji, S. Laghrouche, M. Wack, Pressure control in a PEM fuel cell via second order sliding mode, Int. J. Hydrog. Energy 37 (21) (2012) 16104-16116.
[18] A. Majumdar, M. Haas, I. Elliot, S. Nazari, Control and control-oriented modeling of PEM water electrolyzers: a review, Int. J. Hydrog. Energy 48 (79) (2023) 30621-30641.
[19] B. Flamm, C. Peter, F.N. Buchi, J. Lygeros, Electrolyzer modeling and real-time control for optimized production of hydrogen gas, Appl. Energy 281 (2021) 116031.
[20] R. Findeisen, F. Allgower, An introduction to nonlinear model predictive control. In: 21st Benelux meeting on systems and control, Veldhoven, 2002.
[21] M.M. Morato, J.E. Normey-Rico, O. Sename, Model predictive control design for linear parameter varying systems: a survey, Annu. Rev. Contr. 49 (2020) 64-80.
[22] J.H. Lee, N.L. Ricker, Extended Kalman filter based nonlinear model predictive control, 1993 American Control Conference. June 2-4, 1993, San Francisco, CA, USA. IEEE, (1993) 1895-1899.
[23] M. Korda, I. Mezic, Linear predictors for nonlinear dynamical systems: koopman operator meets model predictive control, Automatica 93 (2018) 149-160.
[24] M.O. Williams, I.G. Kevrekidis, C.W. Rowley, A data-driven approximation of the koopman operator: extending dynamic mode decomposition, J. Nonlinear Sci. 25 (6) (2015) 1307-1346.
[25] M. Korda, Y. Susuki, I.Mezic, Power grid transient stabilization using Koopman model predictive control, IFAC-PapersOnLine 51 (28) (2018) 297-302.
[26] S.H. Son, H.K. Choi, J.S.I. Kwon, Application of offset-free Koopman-based model predictive control to a batch pulp digester, AlChE. J. 67 (9) (2021) e17301.
[27] L. Magdalena, Fuzzy rule-based systems. Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, (2015), pp 03-218.
[28] H. Ashraf, M.M. Elkholy, S.O. Abdellatif, A.A. El-Fergany, Synergy of neuro-fuzzy controller and tuna swarm algorithm for maximizing the overall efficiency of PEM fuel cells stack including dynamic performance, Energy Convers. Manag. X 16 (2022) 100301.
[29] A.T. Nguyen, T. Taniguchi, L. Eciolaza, V. Campos, R. Palhares, M. Sugeno, Fuzzy control systems: past, present and future, IEEE Comput. Intell. Mag. 14 (1) (2019) 56-68.
[30] M.H. Cano, S. Kelouwani, K. Agbossou, Y.Dube, Power management system for off-grid hydrogen production based on uncertainty, Int. J. Hydrog. Energy 40 (23) (2015) 7260-7272.
[31] N.A. Spielberg, M. Brown, J.C. Gerdes, Neural network model predictive motion control applied to automated driving with unknown friction, IEEE Trans. Contr. Syst. Technol. 30 (5) (2022) 1934-1945.
[32] A. Surana, A. Banaszuk, Linear observer synthesis for nonlinear systems using Koopman Operator framework, IFAC-PapersOnLine 49 (18) (2016) 716-723.
[33] A. Surana, Koopman operator framework for time series modeling and analysis, J. Nonlinear Sci. 30 (5) (2020) 1973-2006.
[34] J.N. Kutz, S.L. Brunton, B.W. Brunton, J.L. Proctor. Dynamic mode decomposition: data-driven modeling of complex systems. SIAM Press，U.S, 2016.
[35] R.R. Yager, J. Kacprzyk, G. Beliakov, Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice. Springer Berlin Heidelberg, (2011).
[36] E.W. Lemmon, M.L. Huber, J.W. Leachman, Revised standardized equation for hydrogen gas densities for fuel consumption applications, J. Res. Natl. Inst. Stand. Technol. 113 (6) (2008) 341-350.

A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Zhourong Xiao, Changxuan Zhang, Peng Li, Desong Wang, Xiangwen Zhang, Li Wang, Jijun Zou, Guozhu Li. Engineering oxygen vacancies on Tb-doped ceria supported Pt catalyst for hydrogen production through steam reforming of long-chain hydrocarbon fuels[J]. 中国化学工程学报, 2024, 68(4): 181-192.
[2]	Peng Jiang, Hao Zhang, Guanhan Zhao, Lin Li, Tuo Ji, Liwen Mu, Xiaohua Lu, Jiahua Zhu. A thermodynamic view on the in-situ carbon dioxide reduction by biomass-derived hydrogen during calcium carbonate decomposition[J]. 中国化学工程学报, 2024, 68(4): 231-240.
[3]	Shan Liu, Wenqi Zhong, Li Sun, Xi Chen, Rafal Madonski. Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system[J]. 中国化学工程学报, 2024, 67(3): 182-194.
[4]	Porapak Suriya, Shanshan Xu, Shengzhe Ding, Sarayute Chansai, Yilai Jiao, Joseph Hurd, Daniel Lee, Yuxin Zhang, Christopher Hardacre, Prasert Reubroycharoen, Xiaolei Fan. Ethanol steam reforming over Ni/ZSM-5 nanosheet for hydrogen production[J]. 中国化学工程学报, 2024, 67(3): 247-256.
[5]	Yi-Fei Liang, Jin-Rong Lu, Shang-Kun Tian, Wen-Quan Cui, Li Liu. Pt nanoclusters modified porous g-C₃N₄ nanosheets to significantly enhance hydrogen production by photocatalytic water reforming of methanol[J]. 中国化学工程学报, 2024, 66(2): 40-50.
[6]	Huairong Zhou, Xin Wu, Chunlei Li, Siyu Yang, Zhichen Chen, Jun Lu, Chen Fang. Optimization of capacity configuration and comprehensive evaluation of a renewable energy electrolysis of water for hydrogen production system[J]. 中国化学工程学报, 2024, 76(12): 301-317.
[7]	Shan Liu, Wenqi Zhong, Xi Chen, Li Sun, Lukuan Yang. Multiobjective economic model predictive control using utopia-tracking for the wet flue gas desulphurization system[J]. 中国化学工程学报, 2023, 54(2): 343-352.
[8]	Yishuang Wang, Na Li, Mingqiang Chen, Defang Liang, Chang Li, Quan Liu, Zhonglian Yang, Jun Wang. Glycerol steam reforming over hydrothermal synthetic Ni-Ca/attapulgite for green hydrogen generation[J]. 中国化学工程学报, 2022, 48(8): 176-190.
[9]	Ying Zhou, Ruiying Li, Zexuan Lv, Jian Liu, Hongjun Zhou, Chunming Xu. Green hydrogen: A promising way to the carbon-free society[J]. 中国化学工程学报, 2022, 43(3): 2-13.
[10]	Xuanyi Jia, Xiaomin Hu, Qiao Wang, Baiquan Chen, Xingyue Xie, Lihong Huang. Auto-thermal reforming of acetic acid for hydrogen production by Zn_xNi_yCrO_m±δ catalysts: Effect of Cr promoted Ni-Zn intermetallic compound[J]. 中国化学工程学报, 2022, 43(3): 216-221.
[11]	Feng Guo, Haoran Sun, Yuxing Shi, Fengyu Zhou, Weilong Shi. CdS nanoparticles decorated hexagonal Fe₂O₃ nanosheets with a Z-scheme photogenerated electron transfer path for improved visible-light photocatalytic hydrogen production[J]. 中国化学工程学报, 2022, 43(3): 266-274.
[12]	Yixuan Gong, Jiasai Yao, Ping Wang, Zhenxing Li, Hongjun Zhou, Chunming Xu. Perspective of hydrogen energy and recent progress in electrocatalytic water splitting[J]. 中国化学工程学报, 2022, 43(3): 282-296.
[13]	Jiankang Wang, Yajing Wang, Zhongping Yao, Zhaohua Jiang. Metal-organic framework-derived Ni doped Co₃S₄ hierarchical nanosheets as a monolithic electrocatalyst for highly efficient hydrogen evolution reaction in alkaline solution[J]. 中国化学工程学报, 2022, 42(2): 380-388.
[14]	Patsakol Prayoonpunratn, Trin Jedsukontorn, Mali Hunsom. Photocatalytic activity of metal nanoparticle-decorated titanium dioxide for simultaneous H₂ production and biodiesel wastewater remediation[J]. 中国化学工程学报, 2021, 36(8): 86-100.
[15]	Xuehua Ruan, Wenbo Huo, Jiaming Wang, Minggang Guo, Wenji Zheng, Yun Zou, Aibin Huang, Jianxiang Shou, Gaohong He. Multi-technique integration separation frameworks after steam reforming for coal-based hydrogen generation[J]. 中国化学工程学报, 2021, 35(7): 163-172.