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A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency

Author

Listed:
  • Yaping Wu

    (College of Liberal Arts, Jiangxi Science and Technology Normal University, Nanchang 330000, China)

  • Xiaolong Wu

    (Belt and Road Joint Laboratory on Measurement and Control Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Shenzhen Research Institute, Huazhong University of Science and Technology, Shenzhen 518055, China
    School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Yuanwu Xu

    (School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Yongjun Cheng

    (Wuhan Maritime Communication Research Institute, Wuhan 430205, China)

  • Xi Li

    (Belt and Road Joint Laboratory on Measurement and Control Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Shenzhen Research Institute, Huazhong University of Science and Technology, Shenzhen 518055, China)

Abstract

Efficiency prediction plays a crucial role in the ongoing development of electrochemical energy technology. Our industries heavily depend on a reliable energy supply for power and electricity, and solid oxide fuel cell (SOFC) systems stand out as renewable devices with immense potential. SOFCs, as one of the various types of fuel cells, are renowned for their capability of combined heat and power generation. They can achieve an efficiency of up to 90% in operation. Furthermore, due to the fact that water is the byproduct of their electricity generation process, they are extremely environmentally friendly, contributing significantly to humanity’s sustainable development. With the advancement of renewable energy technologies and the increasing emphasis on sustainable development requirements, predicting and optimizing the efficiency of SOFC systems is gaining importance. This study leverages data collected from an SOFC system and applies an improved neural network structure, specifically the dendritic network (DN) architecture, to forecast thermoelectric efficiency. The key advantage of this method lies in the adaptive neural network algorithm based on the dendritic network structure without manually setting hidden nodes. Moreover, the predicted model of thermoelectric efficiency is validated using 682 and 1099 h of operational data from the SOFC system, and the results are compared against a conventional machine learning method. After comparison, it is found that when the novel method with adaptive characteristics proposed was used for SOFC system efficiency prediction, the MAE and RMSE values were both lower than 0.014; the result is significantly better than from other traditional methods. Additionally, this study demonstrated its effectiveness in predicting the thermoelectric efficiency of SOFC systems through secondary experiments. This study offers guidance on enhancing SOFC systems thermoelectric efficiency. Therefore, this study provides a foundation for the future industrialization of fuel cell systems.

Suggested Citation

  • Yaping Wu & Xiaolong Wu & Yuanwu Xu & Yongjun Cheng & Xi Li, 2023. "A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14402-:d:1251821
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    References listed on IDEAS

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    1. Wang, Fu & Deng, Shuai & Zhang, Houcheng & Wang, Jiatang & Zhao, Jiapei & Miao, He & Yuan, Jinliang & Yan, Jinyue, 2020. "A comprehensive review on high-temperature fuel cells with carbon capture," Applied Energy, Elsevier, vol. 275(C).
    2. Youming Wang & Didi Qing & Thach Ngoc Dinh, 2021. "Model Predictive Control of Nonlinear System Based on GA-RBP Neural Network and Improved Gradient Descent Method," Complexity, Hindawi, vol. 2021, pages 1-14, March.
    3. Wu, Xiao-long & Xu, Yuan-wu & Zhao, Dong-qi & Zhong, Xiao-bo & Li, Dong & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Extended-range electric vehicle-oriented thermoelectric surge control of a solid oxide fuel cell system," Applied Energy, Elsevier, vol. 263(C).
    4. Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Enas Taha Sayed, 2023. "Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production," Sustainability, MDPI, vol. 15(3), pages 1-12, January.
    5. Wu, Xiao-long & Xu, Yuan-Wu & Xue, Tao & Zhao, Dong-qi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2019. "Health state prediction and analysis of SOFC system based on the data-driven entire stage experiment," Applied Energy, Elsevier, vol. 248(C), pages 126-140.
    6. Deng, Huiwen & Hu, Weihao & Cao, Di & Chen, Weirong & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2022. "Degradation trajectories prognosis for PEM fuel cell systems based on Gaussian process regression," Energy, Elsevier, vol. 244(PA).
    7. Orlando Corigliano & Leonardo Pagnotta & Petronilla Fragiacomo, 2022. "On the Technology of Solid Oxide Fuel Cell (SOFC) Energy Systems for Stationary Power Generation: A Review," Sustainability, MDPI, vol. 14(22), pages 1-73, November.
    8. Han, Yongming & Li, Jingze & Lou, Xiaoyi & Fan, Chenyu & Geng, Zhiqiang, 2022. "Energy saving of buildings for reducing carbon dioxide emissions using novel dendrite net integrated adaptive mean square gradient," Applied Energy, Elsevier, vol. 309(C).
    9. 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).
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    1. Tancredi Testasecca & Manfredi Picciotto Maniscalco & Giovanni Brunaccini & Girolama Airò Farulla & Giuseppina Ciulla & Marco Beccali & Marco Ferraro, 2024. "Toward a Digital Twin of a Solid Oxide Fuel Cell Microcogenerator: Data-Driven Modelling," Energies, MDPI, vol. 17(16), pages 1-15, August.

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