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Thermodynamic analysis of biogas fed solid oxide fuel cell power plants

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  • Prodromidis, George N.
  • Coutelieris, Frank A.

Abstract

The present research study presents the optimization of Solid Oxide Fuel Cell (SOFC) power plants directly fed by biogas. By considering energy and exergy balances for such a system, a detailed thermodynamic model (THERMAS) was designed and implemented. A specific SOFC-based system was selected as case study, equipped with three heat exchangers (preheaters), a reformer, a SOFC-stack system and an afterburner. The use of the simulation tool THERMAS give us the opportunity to investigate all the appropriate parameters that affect system’s efficiency based on exergy analysis while incorporating a detailed parametric analysis regarding the whole system. The optimization process relies on the difference between the energy and exergy efficiency by considering an innovative Optimization Factor (OPF) for each simulated system, which is dynamically affected by operational parameters, such as fuel composition, extension of chemical reactions and temperatures. It is found that the use of a pure fuels seems to be meaningless without optimization.

Suggested Citation

  • Prodromidis, George N. & Coutelieris, Frank A., 2017. "Thermodynamic analysis of biogas fed solid oxide fuel cell power plants," Renewable Energy, Elsevier, vol. 108(C), pages 1-10.
    • Handle: RePEc:eee:renene:v:108:y:2017:i:c:p:1-10
    DOI: 10.1016/j.renene.2017.02.043
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    References listed on IDEAS

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    1. Papurello, Davide & Lanzini, Andrea & Tognana, Lorenzo & Silvestri, Silvia & Santarelli, Massimo, 2015. "Waste to energy: Exploitation of biogas from organic waste in a 500 Wel solid oxide fuel cell (SOFC) stack," Energy, Elsevier, vol. 85(C), pages 145-158.
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    2. Nassef, Ahmed M. & Fathy, Ahmed & Sayed, Enas Taha & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Tanveer, Waqas Hassan & Olabi, A.G., 2019. "Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms," Renewable Energy, Elsevier, vol. 138(C), pages 458-464.
    3. Abdelkareem, Mohammad Ali & Allagui, Anis & Sayed, Enas Taha & El Haj Assad, M. & Said, Zafar & Elsaid, Khaled, 2019. "Comparative analysis of liquid versus vapor-feed passive direct methanol fuel cells," Renewable Energy, Elsevier, vol. 131(C), pages 563-584.
    4. Wang, Yuqing & Wehrle, Lukas & Banerjee, Aayan & Shi, Yixiang & Deutschmann, Olaf, 2021. "Analysis of a biogas-fed SOFC CHP system based on multi-scale hierarchical modeling," Renewable Energy, Elsevier, vol. 163(C), pages 78-87.
    5. Aghbashlo, Mortaza & Tabatabaei, Meisam & Soltanian, Salman & Ghanavati, Hossein, 2019. "Biopower and biofertilizer production from organic municipal solid waste: An exergoenvironmental analysis," Renewable Energy, Elsevier, vol. 143(C), pages 64-76.
    6. Abdelkareem, Mohammad Ali & Tanveer, Waqas Hassan & Sayed, Enas Taha & Assad, M. El Haj & Allagui, Anis & Cha, S.W., 2019. "On the technical challenges affecting the performance of direct internal reforming biogas solid oxide fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 361-375.
    7. Farnak, M. & Esfahani, J.A. & Bozorgmehri, S., 2020. "An experimental design of the solid oxide fuel cell performance by using partially oxidation reforming of natural gas," Renewable Energy, Elsevier, vol. 147(P1), pages 155-163.
    8. Kęstutis Venslauskas & Kęstutis Navickas & Marja Nappa & Petteri Kangas & Revilija Mozūraitytė & Rasa Šližytė & Vidmantas Župerka, 2021. "Energetic and Economic Evaluation of Zero-Waste Fish Co-Stream Processing," IJERPH, MDPI, vol. 18(5), pages 1-16, February.
    9. Tan, Luzhi & Dong, Xiaoming & Gong, Zhiqiang & Wang, Mingtao, 2018. "Analysis on energy efficiency and CO2 emission reduction of an SOFC-based energy system served public buildings with large interior zones," Energy, Elsevier, vol. 165(PB), pages 1106-1118.
    10. Li, Jiawen, 2022. "A multi-objective energy coordinative and management policy for solid oxide fuel cell using triune brain large-scale multi-agent deep deterministic policy gradient," Applied Energy, Elsevier, vol. 324(C).

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