ArticleScientific reports2025
Experimental analysis and optimization of hydrogen pre-cooling liquefaction process with composite catalyst through a hybrid priority cluster modeling approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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9 authors.
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Abstract
The analysis studies impact of nanocomposites (NCs) to improve thermal efficiency in hydrogen liquefaction while decreasing energy consumption. The study uses an innovative combination of experimental investigations coupled with machine learning methods to identify superior nanocomposites for their peak performance characteristics. Experimental data measurement of key thermophysical characteristics are estimated by using Pearson's-r correlation analysis. The weightage analysis obtained through the MEREC analysis. The priority weights obtained for the inputs are concentration of nano-additives concentration at 33% while flow rates take 29% and pressure receives 23% and temperature stands at 14%. The optimum input operating characteristics were found in combination 9, with pressure of 0.23 MPa, temperature of 260 K, flow rate of 0.11 kg/s, and NC concentration of 0.24 wt%, leading to the most efficient performance in the hydrogen precooling process. The combination of Graphene/TiO
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