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A sustainable biomass-powered multi-generation system utilizing hydrogen energy storage: Techno-economic-environmental analysis and deep learning-assisted optimization

Author

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  • Lotfollahi, Amirhosein
  • Korpeh, Mobin
  • Balali, Adel
  • Moghimi, Mahdi

Abstract

This paper introduces a novel biomass-based multi-generation system (MGS) integrated with hydrogen energy storage technology to enhance performance, efficiency, and sustainability. By utilizing hydrogen as a clean and sustainable energy carrier, this approach provides a promising solution to both environmental and energy challenges. The system employs multiple waste heat recovery cycles to deliver continuous power, cooling, heating, freshwater, and hydrogen, while minimizing environmental impacts. The study is divided into two phases: charge and discharge. During the charge phase, hydrogen is produced and stored in high-pressure tanks, while the discharge phase uses a fuel cell to convert stored hydrogen into power. The MGS's performance is evaluated from energy, exergy, economic, and environmental perspectives across both operational phases. The results demonstrate that the proposed integration enhances system flexibility and efficiency while significantly reducing greenhouse gas emissions, thus contributing to sustainability goals. To optimize the system's performance, two multi-objective optimization algorithms are employed, with artificial neural networks facilitating the acceleration of the optimization process. The optimization focuses on hydrogen management by maximizing hydrogen production during the charge phase and consumption during the discharge phase using the fuel cell. Under optimal conditions in the charge phase, using the genetic algorithm, the exergy efficiency, cost rate, and hydrogen production are 35.68 %, 217.11 $/h, and 24.95 kg/h, respectively.

Suggested Citation

  • Lotfollahi, Amirhosein & Korpeh, Mobin & Balali, Adel & Moghimi, Mahdi, 2026. "A sustainable biomass-powered multi-generation system utilizing hydrogen energy storage: Techno-economic-environmental analysis and deep learning-assisted optimization," Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:energy:v:342:y:2026:i:c:s0360544225053496
    DOI: 10.1016/j.energy.2025.139706
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