【论文-2024】Energy harvesting via thermoelectric generators for green hydrogen production: methods and technique
发布人:袁占辉  发布时间:2024-08-09   浏览次数:

Energy harvesting via thermoelectric generators for green hydrogen production: methods and technique

AuthorsSwellam W. Sharshir*, Abanob Joseph, Mamoun M. Elsayad, A. W. Kandeal, A. S. Abdullah, Chong Wang, Sung-Hwan Jang*, Meng An, Nouby M. Ghazaly, Zhanhui Yuan*

Abstract:The integration of thermoelectric generators (TEGs) into industrial processes and multi-generation systems presents a promising solution for recovering low-grade waste heat. This study explores the use of such waste heat in producing green hydrogen, utilizing TEGs as an alternative medium for energy recovery. The hydrogen production methods examined include proton exchange membrane (PEM) electrolyzers and alkaline electrolyzers. The heat sources are categorized into geothermal sources, solar energy sources, and general waste heat sources. By incorporating TEGs into hybrid systems, the overall system efficiency and rated power output, as well as green hydrogen production, are significantly enhanced. For instance, a system combining an organic Rankine cycle powered by a geothermal source with a TEG and a PEM electrolyzer can generate 98.37 kg/day of H2 and produce 6781 kW of power, achieving an exergy efficiency of 55.39 %. Additionally, TEGs are employed in hydrogen storage and liquefaction to improve the efficiency of the compression and liquefaction processes by reutilizing waste heat. To provide a comprehensive overview, a bibliometric study using the VOSviewer tool is conducted to highlight the trends in hydrogen production via TEGs, analyzing authors' keywords and index keywords. Moreover, this study presents innovative methods and insights into the efficient recovery and utilization of waste heat for green hydrogen production, emphasizing the potential of TEGs in advancing sustainable energy solutions.

       Keywords:
Green hydrogen,
Heat recovery,
Thermoelectric generator,
Hydrogen storage,
Bibliometric analysis

DOI:https://doi.org/10.1021/acsnano.4c01416