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Optimum design parameters and operating condition for maximum power of a direct methanol fuel cell using analytical model and genetic algorithm

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  • Tafaoli-Masoule, M.
  • Bahrami, A.
  • Elsayed, E.M.

Abstract

It is well known that anode and cathode pressures, cell temperature and channel geometry are the effective parameters in the performance of DMFC (direct methanol fuel cell). In the present paper, the GA (genetic algorithm) as one of the most powerful optimization tools is applied to determine the optimal values for these parameters which result in maximum power density of a DMFC. The predominant part of the genetic algorithm is the fitness function. For the fitness function calculation, calculation of more than one thousand cases is necessary. Unfortunately, large numbers of experiments are needed, which is very time-consuming and costly. To overcome this challenge, a quasi two dimensional, isothermal model is used to obtain the power of DMFC as the fitness function of GA. For validation of this model, the results of the model are compared with experimental results and literature and shown to be in good agreement with them.

Suggested Citation

  • Tafaoli-Masoule, M. & Bahrami, A. & Elsayed, E.M., 2014. "Optimum design parameters and operating condition for maximum power of a direct methanol fuel cell using analytical model and genetic algorithm," Energy, Elsevier, vol. 70(C), pages 643-652.
    • Handle: RePEc:eee:energy:v:70:y:2014:i:c:p:643-652
    DOI: 10.1016/j.energy.2014.04.051
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    Cited by:

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    2. Zhengang Zhao & Dongjie Li & Xiaoping Xu & Dacheng Zhang, 2023. "An Adaptive Joint Operating Parameters Optimization Approach for Active Direct Methanol Fuel Cells," Energies, MDPI, vol. 16(5), pages 1-14, February.
    3. Yuan, Zhenyu & Zhang, Manna & Zuo, Kaiyuan & Ren, Yongqiang, 2018. "The effect of gravity on inner transport and cell performance in passive micro direct methanol fuel cell," Energy, Elsevier, vol. 150(C), pages 28-37.
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    5. Sayadi, Parvin & Rowshanzamir, Soosan & Parnian, Mohammad Javad, 2016. "Study of hydrogen crossover and proton conductivity of self-humidifying nanocomposite proton exchange membrane based on sulfonated poly (ether ether ketone)," Energy, Elsevier, vol. 94(C), pages 292-303.
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    8. Rahnavard, Aylin & Rowshanzamir, Soosan & Parnian, Mohammad Javad & Amirkhanlou, Gholam Reza, 2015. "The effect of sulfonated poly (ether ether ketone) as the electrode ionomer for self-humidifying nanocomposite proton exchange membrane fuel cells," Energy, Elsevier, vol. 82(C), pages 746-757.
    9. Yuan, Zhenyu & Yang, Jie & Zhang, Yufeng, 2015. "A self-adaptive supply method of micro direct methanol fuel cell," Energy, Elsevier, vol. 91(C), pages 1064-1069.
    10. Qinwen Yang & Gang Xiao & Tao Liu & Bin Gao & Shujun Chen, 2022. "Efficient Prediction of Fuel Cell Performance Using Global Modeling Method," Energies, MDPI, vol. 15(22), pages 1-14, November.
    11. Yang, Qinwen & Xiao, Gang & Li, Lexi & Che, Mengjie & Hu, Xu-Qu & Meng, Min, 2021. "Collaborative design of multi-type parameters for design and operational stage matching in fuel cells," Renewable Energy, Elsevier, vol. 175(C), pages 1101-1110.
    12. Fang, Shuo & Zhang, Yufeng & Zou, Yuezhang & Sang, Shengtian & Liu, Xiaowei, 2017. "Structural design and analysis of a passive DMFC supplied with concentrated methanol solution," Energy, Elsevier, vol. 128(C), pages 50-61.
    13. Yuan, Zhenyu & Yang, Jie & Zhang, Yufeng & Wang, Shikai & Xu, Tingnian, 2015. "Mass transport optimization in the anode diffusion layer of a micro direct methanol fuel cell," Energy, Elsevier, vol. 93(P1), pages 599-605.
    14. El-Fergany, Attia A., 2018. "Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer," Renewable Energy, Elsevier, vol. 119(C), pages 641-648.
    15. Fang, Shuo & Zhang, Yufeng & Ma, Zezhong & Sang, Shengtian & Liu, Xiaowei, 2016. "Systemic modeling and analysis of DMFC stack for behavior prediction in system-level application," Energy, Elsevier, vol. 112(C), pages 1015-1023.
    16. Wang, Nan & Wang, Dongxuan & Xing, Yazhou & Shao, Limin & Afzal, Sadegh, 2020. "Application of co-evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model," Renewable Energy, Elsevier, vol. 150(C), pages 221-233.
    17. Yuan, Zhenyu & Yang, Jie & Ye, Ning & Li, Zipeng & Sun, Yuge & Shen, Hongyuan, 2015. "Analysis of the capillary-force-based μDMFC (micro direct methanol fuel cell) supplied with pure methanol," Energy, Elsevier, vol. 89(C), pages 858-863.
    18. Yuan, Zhenyu & Yang, Jie & Li, Zipeng & Sun, Yuge & Ye, Ning & Shen, Hongyuan, 2015. "Analysis of CO2 transmission in a micro direct methanol fuel cell," Energy, Elsevier, vol. 83(C), pages 496-502.
    19. Ahmed M. Agwa & Attia A. El-Fergany & Gamal M. Sarhan, 2019. "Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer," Energies, MDPI, vol. 12(10), pages 1-14, May.
    20. Yuan, Zhenyu & Yang, Jie & Zhang, Yufeng & Zhang, Xiwei, 2015. "The optimization of air-breathing micro direct methanol fuel cell using response surface method," Energy, Elsevier, vol. 80(C), pages 340-349.

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