IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i7p1621-d340239.html
   My bibliography  Save this article

A Study of Anode-Supported Solid Oxide Fuel Cell Modeling and Optimization Using Neural Network and Multi-Armed Bandit Algorithm

Author

Listed:
  • Changhee Song

    (Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Sanghoon Lee

    (Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Bonhyun Gu

    (Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Ikwhang Chang

    (Department of Mechanical and Automotive Engineering, Won-Kwang University, 460 Iksan-daero, Iksan, Jeonbuk 54538, Korea)

  • Gu Young Cho

    (Department of Mechanical Engineering, Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do 16890, Korea)

  • Jong Dae Baek

    (Department of Automotive Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan, Gyeongbuk 38541, Korea)

  • Suk Won Cha

    (Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

Abstract

Anode-supported solid oxide fuel cells (SOFCs) model based on artificial neural network (ANN) and optimized design variables were modeled. The input parameters of the anode-supported SOFC model developed in this study are as follows: current density, temperature, electrolyte thickness, anode thickness, anode porosity, and cathode thickness. Voltage was estimated from the SOFC model with the input parameters. Numerical results show that the SOFC model constructed in this study can represent the actual SOFC characteristics very well. There are four design parameters to be optimized: electrolyte, anode, cathode thickness, and anode porosity. To derive the optimal combination of the design parameters, we have used a multi-armed bandit algorithm (MAB), and developed a methodology for deriving near-optimal parameter set without searching for all possible parameter sets.

Suggested Citation

  • Changhee Song & Sanghoon Lee & Bonhyun Gu & Ikwhang Chang & Gu Young Cho & Jong Dae Baek & Suk Won Cha, 2020. "A Study of Anode-Supported Solid Oxide Fuel Cell Modeling and Optimization Using Neural Network and Multi-Armed Bandit Algorithm," Energies, MDPI, vol. 13(7), pages 1-11, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1621-:d:340239
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/7/1621/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/7/1621/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fong, K.F. & Lee, C.K., 2019. "Performance investigation of a SOFC-primed micro-combined hybrid cooling and power system in hot and humid regions," Energy, Elsevier, vol. 189(C).
    2. Park, Taehyun & Chang, Ikwhang & Lee, Yoon Ho & Ji, Sanghoon & Cha, Suk Won, 2014. "Analysis of operational characteristics of polymer electrolyte fuel cell with expanded graphite flow-field plates via electrochemical impedance investigation," Energy, Elsevier, vol. 66(C), pages 77-81.
    3. Cuneo, A. & Zaccaria, V. & Tucker, D. & Sorce, A., 2018. "Gas turbine size optimization in a hybrid system considering SOFC degradation," Applied Energy, Elsevier, vol. 230(C), pages 855-864.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shan-Jen Cheng & Wen-Ken Li & Te-Jen Chang & Chang-Hung Hsu, 2021. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models," Energies, MDPI, vol. 14(18), pages 1-17, September.
    2. Khadijeh Hooshyari & Bahman Amini Horri & Hamid Abdoli & Mohsen Fallah Vostakola & Parvaneh Kakavand & Parisa Salarizadeh, 2021. "A Review of Recent Developments and Advanced Applications of High-Temperature Polymer Electrolyte Membranes for PEM Fuel Cells," Energies, MDPI, vol. 14(17), pages 1-38, September.
    3. Ong, Samuel & Al-Othman, Amani & Tawalbeh, Muhammad, 2023. "Emerging technologies in prognostics for fuel cells including direct hydrocarbon fuel cells," Energy, Elsevier, vol. 277(C).
    4. Mohsen Fallah Vostakola & Bahman Amini Horri, 2021. "Progress in Material Development for Low-Temperature Solid Oxide Fuel Cells: A Review," Energies, MDPI, vol. 14(5), pages 1-53, February.
    5. Fathy, Ahmed & Rezk, Hegazy, 2022. "Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models," Energy, Elsevier, vol. 238(PC).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, D.C. & Sun, Y.J. & Ma, Z. & Ren, H., 2021. "A review on integration and design of desiccant air-conditioning systems for overall performance improvements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    2. Ding, Xiaoyi & Lv, Xiaojing & Weng, Yiwu, 2019. "Coupling effect of operating parameters on performance of a biogas-fueled solid oxide fuel cell/gas turbine hybrid system," Applied Energy, Elsevier, vol. 254(C).
    3. Roselli, C. & Marrasso, E. & Tariello, F. & Sasso, M., 2020. "How different power grid efficiency scenarios affect the energy and environmental feasibility of a polygeneration system," Energy, Elsevier, vol. 201(C).
    4. Lee, Sanghoon & Lee, Yeageun & Park, Joonho & Yu, Wonjong & Cho, Gu Young & Kim, Yusung & Cha, Suk Won, 2019. "Effect of plasma-enhanced atomic layer deposited YSZ inter-layer on cathode interface of GDC electrolyte in thin film solid oxide fuel cells," Renewable Energy, Elsevier, vol. 144(C), pages 123-128.
    5. Habibollahzade, Ali & Gholamian, Ehsan & Behzadi, Amirmohammad, 2019. "Multi-objective optimization and comparative performance analysis of hybrid biomass-based solid oxide fuel cell/solid oxide electrolyzer cell/gas turbine using different gasification agents," Applied Energy, Elsevier, vol. 233, pages 985-1002.
    6. Giugno, Andrea & Mantelli, Luca & Cuneo, Alessandra & Traverso, Alberto, 2020. "Performance analysis of a fuel cell hybrid system subject to technological uncertainties," Applied Energy, Elsevier, vol. 279(C).
    7. Rosner, Fabian & Samuelsen, Scott, 2022. "Thermo-economic analysis of a solid oxide fuel cell-gas turbine hybrid with commercial off-the-shelf gas turbine," Applied Energy, Elsevier, vol. 324(C).
    8. Huang, Yu & Turan, Ali, 2021. "Mechanical equilibrium operation integrated modelling of recuperative solid oxide fuel cell – gas turbine hybrid systems: Design conditions and off-design analysis," Applied Energy, Elsevier, vol. 283(C).
    9. Rossi, Iacopo & Traverso, Alberto & Tucker, David, 2019. "SOFC/Gas Turbine Hybrid System: A simplified framework for dynamic simulation," Applied Energy, Elsevier, vol. 238(C), pages 1543-1550.
    10. Cheng, Tianliang & Jiang, Jianhua & Wu, Xiaodong & Li, Xi & Xu, Mengxue & Deng, Zhonghua & Li, Jian, 2019. "Application oriented multiple-objective optimization, analysis and comparison of solid oxide fuel cell systems with different configurations," Applied Energy, Elsevier, vol. 235(C), pages 914-929.
    11. Chen, Jinwei & Hu, Zhenchao & Lu, Jinzhi & Zhang, Huisheng & Weng, Shilie, 2022. "A novel control strategy with an anode variable geometry ejector for a SOFC-GT hybrid system," Energy, Elsevier, vol. 261(PA).
    12. Park, Taehyun & Chang, Ikwhang & Jung, Ju Hae & Lee, Ha Beom & Ko, Seung Hwan & O'Hayre, Ryan & Yoo, Sung Jong & Cha, Suk Won, 2017. "Effect of assembly pressure on the performance of a bendable polymer electrolyte fuel cell based on a silver nanowire current collector," Energy, Elsevier, vol. 134(C), pages 412-419.
    13. Kwan, Trevor Hocksun & Katsushi, Fujii & Shen, Yongting & Yin, Shunan & Zhang, Yongchao & Kase, Kiwamu & Yao, Qinghe, 2020. "Comprehensive review of integrating fuel cells to other energy systems for enhanced performance and enabling polygeneration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    14. Huang, Yu & Turan, Ali, 2022. "Flexible power generation based on solid oxide fuel cell and twin-shaft free turbine engine: Mechanical equilibrium running and design analysis," Applied Energy, Elsevier, vol. 315(C).
    15. Huang, Yu & Turan, Ali, 2020. "Mechanical equilibrium operation integrated modelling of hybrid SOFC – GT systems: Design analyses and off-design optimization," Energy, Elsevier, vol. 208(C).
    16. Shi, Wangying & Zhu, Jianzhong & Han, Minfang & Sun, Zaihong & Guo, Yaming, 2019. "Operating limitation and degradation modeling of micro solid oxide fuel cell-combined heat and power system," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    17. Ghorbani, Sh. & Khoshgoftar-Manesh, M.H. & Nourpour, M. & Blanco-Marigorta, A.M., 2020. "Exergoeconomic and exergoenvironmental analyses of an integrated SOFC-GT-ORC hybrid system," Energy, Elsevier, vol. 206(C).
    18. Gye-Eun Jang & Gu-Young Cho, 2022. "Effects of Ag Current Collecting Layer Fabricated by Sputter for 3D-Printed Polymer Bipolar Plate of Ultra-Light Polymer Electrolyte Membrane Fuel Cells," Sustainability, MDPI, vol. 14(5), pages 1-9, March.
    19. Iranzo, Alfredo & Boillat, Pierre, 2018. "CFD simulation of the transient gas transport in a PEM fuel cell cathode during AC impedance testing considering liquid water effects," Energy, Elsevier, vol. 158(C), pages 449-457.
    20. Kang, Yun Sik & Won, Phillip & Ko, Seung Hwan & Park, Taehyun & Yoo, Sung Jong, 2019. "Bending-durable membrane-electrode assembly using metal nanowires for bendable polymer electrolyte membrane fuel cell," Energy, Elsevier, vol. 172(C), pages 874-880.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1621-:d:340239. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.