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Optimizing energy efficiency and emission reduction: Leveraging the power of machine learning in an integrated compressed air energy storage-solid oxide fuel cell system

Author

Listed:
  • Wang, Yongfeng
  • Li, Shuguang
  • Bu sinnah, Zainab Ali
  • Ghandour, Raymond
  • Khan, Mohammad Nadeem
  • Ali, H. Elhosiny

Abstract

This research introduces a cutting-edge energy system that combines a solid oxide fuel cell (SOFC) with compressed air energy storage (CAES) to generate compressed air, electrical power, and heat. The system's performance was assessed and enhanced using regression-based machine learning models, concentrating on three main process variables: temperature, current density, and utilization factor. The machine learning models achieved impressive accuracy, with R-squared values greater than 98 %, demonstrating their effectiveness in predicting system performance. The results from multi-objective optimization indicated that the ideal conditions for maximizing energy storage, efficiency, and minimizing emissions include a temperature of 973 K, a current density of 6000 A/m2, and a utilization factor of 0.74. At these optimal parameters, the system reached an energy storage capacity of 28.12 cm3, an efficiency of 64.19 %, and emissions of 274.04 kg/MWh. These results underscore the potential of the integrated SOFC-CAES system to tackle significant energy and environmental issues by enhancing energy efficiency, lowering emissions, and offering a sustainable approach to power generation. The findings from this study contribute to the development of hybrid energy systems and facilitate the transition to more sustainable and resilient energy frameworks.

Suggested Citation

  • Wang, Yongfeng & Li, Shuguang & Bu sinnah, Zainab Ali & Ghandour, Raymond & Khan, Mohammad Nadeem & Ali, H. Elhosiny, 2024. "Optimizing energy efficiency and emission reduction: Leveraging the power of machine learning in an integrated compressed air energy storage-solid oxide fuel cell system," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s036054422403740x
    DOI: 10.1016/j.energy.2024.133962
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