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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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