Professor Kwon Hwioong of Soonchunhyang University Develops Technology for On-site Hydrogen Production at Charging Stations

Suncheonhyang University announced on the 17th that Professor Kwon Hwi-woong of the Department of Nano Chemical Engineering has succeeded in developing an optimal hydrogen production strategy technology that enables hydrogen stations to produce hydrogen independently.

Professor Kwon's research team collaborated with Professor Park Jin-woo's team from Dongguk University, Dr. Shin Jae-eun from the Korea Institute of Geoscience and Mineral Resources, and Professor Koo Bon-chan's team from Dong-A University to perform process modeling, simulation, and optimization applied to hydrogen production processes for future optimal hydrogen production costs and CO2 reduction.

The research team analyzed various combinations of hydrogen production processes from the perspective of the ‘Dual Modal Mode,’ a function that uses two different methods or modes simultaneously under specific conditions, to derive the optimal process.

Based on the optimization of SMR (Steam Methane Reforming) and ATR (Autothermal Reforming) processes, and through comparative analysis with other hydrogen production processes, they improved the efficiency of on-site hydrogen production processes where hydrogen stations produce hydrogen independently as a field supply method, securing economic feasibility and environmental friendliness. Additionally, sensitivity analysis identified key factors affecting hydrogen production processes.

Professor Kwon Hwi-woong stated, “To enter a hydrogen society, cost-effective hydrogen production is crucial,” adding, “This research is expected to provide important insights to decision-makers operating hydrogen stations or government stakeholders when installing on-site hydrogen stations, accelerating the entry into a hydrogen society and the popularization of hydrogen use compared to the present.”

The research results were recently published under the title “Optimal Investment Strategy Analysis of On-Site Hydrogen Production Based on the Hydrogen Demand Prediction Using Machine Learning” in the international journal International Journal of Energy Research.

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