Abstract
Phasing out fossil fuels is crucial for combating climate change, and hydrogen offers a viable carbon-free alternative. Natural hydrogen from the Earth's crust could significantly aid the transition to renewable energy, yet comprehensive analysis on its extraction remains limited. This study reviews existing research, identifies knowledge gaps, and explores potential reserves and ongoing projects. Former Soviet Union countries have 223 hydrogen discoveries, and rift zones show strong potential for exploration, with the highest hydrogen concentrations (33.7–96.3%), while salt deposits have the lowest (1.1–34.6%). Key challenges include hydrogen identification, well management, leakage risks, and explosion hazards. Areal Location of Hazardous Atmosphere (ALOHA) simulations assess these risks, showing wind speed significantly reduces outdoor hydrogen concentration, while increasing borehole diameter raises it. Simulations for the Bourakebougou (Mali) hydrogen well indicate accidental release concentrations remain below explosive limits. Statistical Experimental Design reveals that larger boreholes increase hydrogen concentration, while higher gas mass, temperature, and wind speed decrease it. These calculations help determine safe distances for preventing explosions. Machine learning has accelerated ALOHA calculations from 40 minutes to seconds, enabling real-time gas concentration estimates. These findings support safer and more efficient natural hydrogen exploration, addressing both opportunities and risks associated with its extraction.
| Original language | English |
|---|---|
| Pages (from-to) | 958-972 |
| Number of pages | 15 |
| Journal | International Journal of Hydrogen Energy |
| Volume | 138 |
| Early online date | 21 May 2025 |
| DOIs | |
| Publication status | Published - 16 Jun 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 13 Climate Action
Keywords
- ALOHA
- Bibliometric analysis
- Challenges and solutions
- Machine learning
- Natural hydrogen
- Natural hydrogen reserves
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