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

Kenzhebatyr Zh Bekmyrza, Kairat A Kuterbekov, Asset M Kabyshev, Marzhan M Kubenova, Aliya A Baratova, Nursultan Aidarbekov and Fohagui Fodoup Cyrille Vinceslas

International Journal of Low-Carbon Technologies, 2025, vol. 20, 1554-1570

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.

Keywords: internet of things (IoT); hydrogen infrastructure; machine learning; simulation-based modeling; sustainable energy systems (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:1554-1570.

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