Experimental analysis and optimization of hydrogen pre-cooling liquefaction process with composite catalyst through a hybrid priority cluster modeling approach.

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 (Anatase) with g-CN/TiO (Graphitic) and SiC/TiO (Silicon Carbide) nano-additives delivering optimum energy consumption and coefficient of performance of 2.70 kWh/kgLH and 5. Effective heat transfer combined with reduced energy losses from integrating these NCs leads to more sustainable and cost-effective hydrogen liquefaction. New energy infrastructure designs benefits from these findings that support hydrogen as a clean energy vector while enhancing industrial liquefaction procedures.