IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v207y2020ics036054422031433x.html
   My bibliography  Save this article

Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process

Author

Listed:
  • Fathy, Ahmed
  • Rezk, Hegazy
  • Mohamed Ramadan, Haitham Saad

Abstract

This paper proposes a recent approach-based moth-flame optimizer (MFO) to enhance the output power of solid oxide fuel cell (SOFC) via identifying the optimal parameters of its model. The cell is modeled via artificial neural network (ANN) trained by experimental dataset. Six inputs are fed to ANN to get the SOFC terminal voltage. Levenberg-Marquardt is used in training process with minimizing the mean squared error (MSE). The SOFC model polarization curves are compared to experimental data under variable operating conditions. The proposed MFO is employed to estimate the optimal values of SOFC model, anode support layer (ASL) thickness; ASL porosity; thickness of electrolyte and cathode functional layer (CFL) thickness to enhance the SOFC extracted power. Furthermore, a quantitative and qualitative comparative study with ANN-based SOFC optimized via Genetic Algorithm (GA), Social Spider Optimizer (SSO), Radial Movement Optimizer (RMO) and the experimental data is presented under different operating conditions. Sensitivity analysis is performed by changing the upper and lower thresholds of the estimated variables. The proposed ANN-MFO approach enhanced the SOFC power by 18.92% and 5.56% in comparison with ANN-GA and ANN-RMO respectively. The obtained results confirmed the significance of the proposed MFO in enhancing of the SOFC output power.

Suggested Citation

  • Fathy, Ahmed & Rezk, Hegazy & Mohamed Ramadan, Haitham Saad, 2020. "Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s036054422031433x
    DOI: 10.1016/j.energy.2020.118326
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422031433X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118326?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Oryshchyn, Danylo & Harun, Nor Farida & Tucker, David & Bryden, Kenneth M. & Shadle, Lawrence, 2018. "Fuel utilization effects on system efficiency in solid oxide fuel cell gas turbine hybrid systems," Applied Energy, Elsevier, vol. 228(C), pages 1953-1965.
    3. de Avila Ferreira, Tafarel & Wuillemin, Zacharie & Faulwasser, Timm & Salzmann, Christophe & Van herle, Jan & Bonvin, Dominique, 2019. "Enforcing optimal operation in solid-oxide fuel-cell systems," Energy, Elsevier, vol. 181(C), pages 281-293.
    4. El-Hay, E.A. & El-Hameed, M.A. & El-Fergany, A.A., 2019. "Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm," Energy, Elsevier, vol. 166(C), pages 451-461.
    5. Yang, Xiaoyu & Zhao, Hongbin, 2019. "Thermodynamic performance study of the SOFC-STIG distributed energy system fueled by LNG with CO2 recovery," Energy, Elsevier, vol. 186(C).
    6. Shayan, E. & Zare, V. & Mirzaee, I., 2019. "On the use of different gasification agents in a biomass fueled SOFC by integrated gasifier: A comparative exergo-economic evaluation and optimization," Energy, Elsevier, vol. 171(C), pages 1126-1138.
    7. Beyrami, Javid & Chitsaz, Ata & Parham, Kiyan & Arild, Øystein, 2019. "Optimum performance of a single effect desalination unit integrated with a SOFC system by multi-objective thermo-economic optimization based on genetic algorithm," Energy, Elsevier, vol. 186(C).
    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. Hachana, Oussama & El-Fergany, Attia A., 2022. "Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer," Energy, Elsevier, vol. 250(C).
    3. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
    4. Zhao, Shuchun & Guo, Junheng & Dang, Xiuhu & Ai, Bingyan & Zhang, Minqing & Li, Wei & Zhang, Jinli, 2022. "Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation," Energy, Elsevier, vol. 240(C).
    5. Fathy, Ahmed & Yousri, Dalia & Alanazi, Turki & Rezk, Hegazy, 2021. "Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm," Energy, Elsevier, vol. 225(C).
    6. Deng, Yan & Zeng, Rong & Liu, Yicai, 2022. "A novel off-design model to optimize combined cooling, heating and power system with hybrid chillers for different operation strategies," Energy, Elsevier, vol. 239(PB).
    7. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
    8. Hesham Alhumade & Ahmed Fathy & Abdulrahim Al-Zahrani & Muhyaddin Jamal Rawa & Hegazy Rezk, 2021. "Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization," Mathematics, MDPI, vol. 9(9), pages 1-19, May.
    9. Abdel-Basset, Mohamed & Mohamed, Reda & El-Fergany, Attia & Chakrabortty, Ripon K. & Ryan, Michael J., 2021. "Adaptive and efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis," Energy, Elsevier, vol. 233(C).
    10. Isen, Evren & Duman, Serhat, 2024. "Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems," Applied Energy, Elsevier, vol. 365(C).

    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. Chitgar, Nazanin & Moghimi, Mahdi, 2020. "Design and evaluation of a novel multi-generation system based on SOFC-GT for electricity, fresh water and hydrogen production," Energy, Elsevier, vol. 197(C).
    2. Liu, Lijun & Qian, Jin & Hua, Li & Zhang, Bin, 2022. "System estimation of the SOFCs using fractional-order social network search algorithm," Energy, Elsevier, vol. 255(C).
    3. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
    4. Fathy, Ahmed & Rezk, Hegazy, 2022. "Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models," Energy, Elsevier, vol. 238(PC).
    5. Yang, Bo & Guo, Zhengxun & Yang, Yi & Chen, Yijun & Zhang, Rui & Su, Keyi & Shu, Hongchun & Yu, Tao & Zhang, Xiaoshun, 2021. "Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells," Applied Energy, Elsevier, vol. 303(C).
    6. Hesham Alhumade & Ahmed Fathy & Abdulrahim Al-Zahrani & Muhyaddin Jamal Rawa & Hegazy Rezk, 2021. "Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization," Mathematics, MDPI, vol. 9(9), pages 1-19, May.
    7. Wang, Jian & Xu, Yi-Peng & She, Chen & Xu, Ping & Bagal, Hamid Asadi, 2022. "Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm," Energy, Elsevier, vol. 240(C).
    8. Mingfei Li & Jingjing Wang & Zhengpeng Chen & Xiuyang Qian & Chuanqi Sun & Di Gan & Kai Xiong & Mumin Rao & Chuangting Chen & Xi Li, 2024. "A Comprehensive Review of Thermal Management in Solid Oxide Fuel Cells: Focus on Burners, Heat Exchangers, and Strategies," Energies, MDPI, vol. 17(5), pages 1-30, February.
    9. Teng, Su & Hamrang, Farzad & Ashraf Talesh, Seyed Saman, 2021. "Economic performance assessment of a novel combined power generation cycle," Energy, Elsevier, vol. 231(C).
    10. Amiralipour, M. & Kouhikamali, R., 2020. "Guilan combined power plant in Iran: As case study for feasibility investigation of converting the combined power plant into water and power unit," Energy, Elsevier, vol. 201(C).
    11. Li, Chen & Wang, Yinglong & Chen, Guanghui & Li, Quan & Gu, Xinchun & Li, Xin & Wang, Yuguang & Zhu, Zhaoyou & Li, Jianlong, 2022. "Thermodynamic analysis and process optimization of organosilicon distillation systems," Energy, Elsevier, vol. 252(C).
    12. Guk, Erdogan & Venkatesan, Vijay & Babar, Shumaila & Jackson, Lisa & Kim, Jung-Sik, 2019. "Parameters and their impacts on the temperature distribution and thermal gradient of solid oxide fuel cell," Applied Energy, Elsevier, vol. 241(C), pages 164-173.
    13. Damo, U.M. & Ferrari, M.L. & Turan, A. & Massardo, A.F., 2019. "Solid oxide fuel cell hybrid system: A detailed review of an environmentally clean and efficient source of energy," Energy, Elsevier, vol. 168(C), pages 235-246.
    14. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    15. Amiri, Hamed & Sotoodeh, Amir Farhang & Amidpour, Majid, 2021. "A new combined heating and power system driven by biomass for total-site utility applications," Renewable Energy, Elsevier, vol. 163(C), pages 1138-1152.
    16. Wang, Erlei & Xia, Jiangying & Li, Jia & Sun, Xianke & Li, Hao, 2022. "Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm," Energy, Elsevier, vol. 261(PA).
    17. Abdul Ghani Olabi & Nabila Shehata & Hussein M. Maghrabie & Lobna A. Heikal & Mohammad Ali Abdelkareem & Shek Mohammod Atiqure Rahman & Sheikh Khaleduzzaman Shah & Enas Taha Sayed, 2022. "Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals," Energies, MDPI, vol. 15(24), pages 1-31, December.
    18. Zhang, Jifu & Cui, Peizhe & Yang, Sheng & Zhou, Yaru & Du, Wei & Wang, Yinglong & Deng, Chengwei & Wang, Shuai, 2023. "Thermodynamic analysis of SOFC–CCHP system based on municipal sludge plasma gasification with carbon capture," Applied Energy, Elsevier, vol. 336(C).
    19. Su, Hongcai & Yan, Mi & Wang, Shurong, 2022. "Recent advances in supercritical water gasification of biowaste catalyzed by transition metal-based catalysts for hydrogen production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    20. Alirahmi, Seyed Mojtaba & Ebrahimi-Moghadam, Amir, 2022. "Comparative study, working fluid selection, and optimal design of three systems for electricity and freshwater based on solid oxide fuel cell mover cycle," Applied Energy, Elsevier, vol. 323(C).

    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:eee:energy:v:207:y:2020:i:c:s036054422031433x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.