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Performance analysis of a novel solar-to-hydrogen system with energy storage via machine learning and particle swarm optimization

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  • Liu, Xianyang
  • Zhu, Tianyu
  • Wei, Zhihao
  • Cai, Shanshan
  • Long, Rui
  • Liu, Zhichun

Abstract

Efficient solar-to-hydrogen system can substantially accelerate the achievement of the carbon neutrality commitment. Here, a novel solar powered hydrogen production system with energy storage is proposed. It comprises a solar energy collector, an adsorption desalination (AD) module, solution storage devices, a reverse electrodialysis (RED) module, a DC-DC converter module, and a proton exchange membrane (PEM) electrolyzer module. The impacts of solar radiation intensity, RED working voltage, and AD adsorption time on the hydrogen production power, hydrogen production rate, and overall hydrogen efficiency are systematically investigated. The optimal system performance under the direct coupling mode and energy storage mode in a day are identified via machine learning and particle swarm optimization. When the system operating under the direct coupling mode, the optimal total amount of hydrogen produced reaches 126.29 L per day. When the system operating under the energy storage mode, the optimal total hydrogen production presents a maximum value of 140.84 L per day at the RED working time of 13 h. Compared with the direct coupling mode, the total hydrogen production of the system under the energy storage mode is increased by 11.5 %.

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  • Liu, Xianyang & Zhu, Tianyu & Wei, Zhihao & Cai, Shanshan & Long, Rui & Liu, Zhichun, 2025. "Performance analysis of a novel solar-to-hydrogen system with energy storage via machine learning and particle swarm optimization," Energy, Elsevier, vol. 315(C).
    • Handle: RePEc:eee:energy:v:315:y:2025:i:c:s0360544225000222
    DOI: 10.1016/j.energy.2025.134380
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    References listed on IDEAS

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