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Integrating machine learning and IoT in hydrogen production, storage, and distribution for a decarbonized transport future

Author

Listed:
  • Kenzhebatyr Zh Bekmyrza
  • Kairat A Kuterbekov
  • Asset M Kabyshev
  • Marzhan M Kubenova
  • Aliya A Baratova
  • Nursultan Aidarbekov
  • Fohagui Fodoup Cyrille Vinceslas

Abstract

This study integrates reinforcement learning (RL) optimization and internet of things (IoT) monitoring within a MATLAB/Simulink simulation framework for hydrogen infrastructure. IoT sensors provide real-time data, enabling dynamic adjustments, while RL optimizes hydrogen logistics, reducing costs and emissions. This approach enhances predictive accuracy beyond conventional models, offering a scalable solution for sustainability. IoT sensors improve model precision, identifying underground storage as the most economical. Renewable energy integration lowered emissions by 97.8% (from 9.00 to 0.20 kg CO2-eq/kg H₂) and reduced hydrogen costs by 40% (from US$5.50 to US$3.30/kg), while RL optimization achieved US$15 000 in cost savings and a 30% emissions reduction.

Suggested Citation

  • Kenzhebatyr Zh Bekmyrza & Kairat A Kuterbekov & Asset M Kabyshev & Marzhan M Kubenova & Aliya A Baratova & Nursultan Aidarbekov & Fohagui Fodoup Cyrille Vinceslas, 2025. "Integrating machine learning and IoT in hydrogen production, storage, and distribution for a decarbonized transport future," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1554-1570.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1554-1570.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf103
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