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Performance prediction of gasification-integrated solid oxide fuel cell and gas turbine cogeneration system based on PSO-BP neural network

Author

Listed:
  • Wu, Xiao-long
  • Yang, Yuxiao
  • Li, Keye
  • Xu, Yuan-wu
  • Peng, Jingxuan
  • Chi, Bo
  • Wang, Zhuo
  • Li, Xi

Abstract

By utilizing syngas produced from biomass gasification as fuel in a combined heat and power (CHP) system comprising solid oxide fuel cells (SOFC) and gas turbines (GT). This green technology uses syngas from biomass gasification in a CHP system with SOFC and GT. It integrates biomass gasification with new energy sources and has great potential for renewable energy. However, performance prediction remains challenging due to the complex multi-physics coupling between the bubbling fluidized bed gasifier (BFBG) and the SOFC-GT system. In addition, the system has various real-world applications and requires a universal method to comprehensively analyze its thermoelectric performance during operation. Thus, a robust theoretical model is essential for investigating the application of the BFBG-SOFC-GT system. In this study, a novel BFBG-SOFC-GT system utilizing 15 different types of biomass gas is constructed using Aspen Plus, and a particle swarm optimization-backpropagation (PSO-BP) neural network model is developed for performance prediction. Numerical simulations are also carried out to validate and analyze the integrated system. The effects of gasification temperature, steam-to-biomass mass (S/B) value, and anode temperature on SOFC voltage and current density, as well as the system's electrical efficiency and CHP efficiency, are analyzed. The results demonstrate that increasing gasification temperature decreases SOFC voltage, increases current density, and reduces both electrical and cogeneration efficiency. Conversely, increasing the S/B value and anode temperature leads to higher SOFC voltage, lower current density, and improved electrical and cogeneration efficiency. Additionally, the PSO-BP neural network model's effectiveness in predicting system performance is validated. The results show that, in error evaluations of voltage, current density, electrical efficiency, and cogeneration efficiency, R2 exceeds 0.98, MAPE is below 0.06, and RMSE is less than 0.33.

Suggested Citation

  • Wu, Xiao-long & Yang, Yuxiao & Li, Keye & Xu, Yuan-wu & Peng, Jingxuan & Chi, Bo & Wang, Zhuo & Li, Xi, 2024. "Performance prediction of gasification-integrated solid oxide fuel cell and gas turbine cogeneration system based on PSO-BP neural network," Renewable Energy, Elsevier, vol. 237(PC).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pc:s0960148124017798
    DOI: 10.1016/j.renene.2024.121711
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    References listed on IDEAS

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    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. Monteiro, Eliseu & Ismail, Tamer M. & Ramos, Ana & Abd El-Salam, M. & Brito, Paulo & Rouboa, Abel, 2018. "Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant," Energy, Elsevier, vol. 142(C), pages 862-877.
    3. Geng, Zhiqiang & Zhang, Yanhui & Li, Chengfei & Han, Yongming & Cui, Yunfei & Yu, Bin, 2020. "Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature," Energy, Elsevier, vol. 194(C).
    4. Khojaste Effatpanah, Saeed & Rahbari, Hamid Reza & Ahmadi, Mohammad H. & Farzaneh, Ali, 2023. "Green hydrogen production and utilization in a novel SOFC/GT-based zero-carbon cogeneration system: A thermodynamic evaluation," Renewable Energy, Elsevier, vol. 219(P2).
    5. Roy, Dibyendu & Samanta, Samiran & Ghosh, Sudip, 2020. "Performance assessment of a biomass fuelled advanced hybrid power generation system," Renewable Energy, Elsevier, vol. 162(C), pages 639-661.
    6. Yuan, Guanghui & Yang, Weixin, 2019. "Study on optimization of economic dispatching of electric power system based on Hybrid Intelligent Algorithms (PSO and AFSA)," Energy, Elsevier, vol. 183(C), pages 926-935.
    7. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
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    1. Hristo Ivanov Beloev & Stanislav Radikovich Saitov & Antonina Andreevna Filimonova & Natalia Dmitrievna Chichirova & Egor Sergeevich Mayorov & Oleg Evgenievich Babikov & Iliya Krastev Iliev, 2025. "Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach," Energies, MDPI, vol. 18(9), pages 1-24, April.
    2. Bai, Bin & Yu, Xinyue & Wang, Yujie & Sun, Jianlong & Zhou, Weihong & Jin, Hui, 2025. "Thermodynamic insight into the integration of supercritical water gasification and solid oxide fuel cells for waste plastic energy recovery," Energy, Elsevier, vol. 332(C).
    3. Zhao, Ying & Wang, Xiaohong & Li, Chen & Guo, Ruitong & Zhang, Weibin, 2025. "Optimization study of biomass and HDPE co-gasification-SOFC system based on SSA-BP neural network," Energy, Elsevier, vol. 335(C).

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