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Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems

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  • Asensio, F.J.
  • San Martín, J.I.
  • Zamora, I.
  • Garcia-Villalobos, J.

Abstract

This paper focuses on the modelling of the performance of a Polymer Electrolyte Membrane Fuel Cell (PEMFC)-based cogeneration system to integrate it in hybrid and/or connected to grid systems and enable the optimization of the techno-economic efficiency of the system in which it is integrated. To this end, experimental tests on a PEMFC-based cogeneration system of 600 W of electrical power have been performed to train an Artificial Neural Network (ANN). Once the learning of the ANN, it has been able to emulate real operating conditions, such as the cooling water out temperature and the hydrogen consumption of the PEMFC depending on several variables, such as the electric power demanded, temperature of the inlet water flow to the cooling circuit, cooling water flow and the heat demanded to the CHP system. After analysing the results, it is concluded that the presented model reproduces with enough accuracy and precision the performance of the experimented PEMFC, thus enabling the use of the model and the ANN learning methodology to model other PEMFC-based cogeneration systems and integrate them in techno-economic efficiency optimization control systems.

Suggested Citation

  • Asensio, F.J. & San Martín, J.I. & Zamora, I. & Garcia-Villalobos, J., 2017. "Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems," Energy, Elsevier, vol. 123(C), pages 585-593.
    • Handle: RePEc:eee:energy:v:123:y:2017:i:c:p:585-593
    DOI: 10.1016/j.energy.2017.02.043
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    References listed on IDEAS

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    1. Sharifi Asl, S.M. & Rowshanzamir, S. & Eikani, M.H., 2010. "Modelling and simulation of the steady-state and dynamic behaviour of a PEM fuel cell," Energy, Elsevier, vol. 35(4), pages 1633-1646.
    2. Shao, Meng & Zhu, Xin-Jian & Cao, Hong-Fei & Shen, Hai-Feng, 2014. "An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system," Energy, Elsevier, vol. 67(C), pages 268-275.
    3. Bicer, Y. & Dincer, I. & Aydin, M., 2016. "Maximizing performance of fuel cell using artificial neural network approach for smart grid applications," Energy, Elsevier, vol. 116(P1), pages 1205-1217.
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    Citations

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    Cited by:

    1. Fragiacomo, Petronilla & Lucarelli, Giuseppe & Genovese, Matteo & Florio, Gaetano, 2021. "Multi-objective optimization model for fuel cell-based poly-generation energy systems," Energy, Elsevier, vol. 237(C).
    2. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Oñederra, O., 2018. "Model for optimal management of the cooling system of a fuel cell-based combined heat and power system for developing optimization control strategies," Applied Energy, Elsevier, vol. 211(C), pages 413-430.
    3. Fathabadi, Hassan, 2018. "Novel fuel cell/battery/supercapacitor hybrid power source for fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 143(C), pages 467-477.
    4. Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(C).
    5. Milcarek, Ryan J. & DeBiase, Vincent P. & Ahn, Jeongmin, 2020. "Investigation of startup, performance and cycling of a residential furnace integrated with micro-tubular flame-assisted fuel cells for micro-combined heat and power," Energy, Elsevier, vol. 196(C).
    6. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation," Energy, Elsevier, vol. 206(C).
    7. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Saldaña, G. & Oñederra, O., 2019. "Analysis of electrochemical and thermal models and modeling techniques for polymer electrolyte membrane fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    8. Chen, Fengxiang & Pei, Yaowang & Jiao, Jieran & Chi, Xuncheng & Hou, Zhongjun, 2023. "Energy flow and thermal voltage analysis of water-cooled PEMFC stack under normal operating conditions," Energy, Elsevier, vol. 275(C).

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