Hydrogen is getting great attention as a key energy carrier for a cleaner energy future, with demand projections up to 20% of global energy demand by 2050. However, the low volumetric density of hydrogen leads to a challenge for storage and transport purposes, making liquefaction a promising solution that also ensures high purity. However, at the current time, the high energy consumption of liquefaction remains a major obstacle. Within this process, the precooling stage is the second most energy-intensive step, covering the broadest temperature range but offering flexibility in terms of refrigerant choice, cycle configuration, and operating conditions.

However, most of the study of the hydrogen precooling omits economic analysis, refrigerant freeze-out discussion, and employs a portion of a non-environmentally sustainable substance as the refrigerant. This study focused on addressing the gap by conducting multi-objective optimization (MOO) on specific energy consumption (SEC) and levelized precooling cost (LPC) to find out the optimal trade-off between the technical and economic competitiveness of the precooling stage, while ensuring the freeze-out risk in the streams was avoided and using environmentally friendly mixed refrigerant (MR) mixtures.

Two cycles, namely single mixed refrigerant (SMR) and dual mixed refrigerant (DMR), are modeled in Aspen HYSYS V12, where the configurations are defined based on freeze-out consideration. Nine MR mixtures for SMR and DMR are defined based on their thermophysical properties. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm was used for MOO through the pymoo library package in Python, which was then coupled with Aspen HYSYS. The decision variables to be optimized include MR composition, MR flow rate, compressor discharge pressure, and JT valve outlet pressure. Several constraints are also introduced, such as vapor fraction at the inlet compressor, minimum internal temperature difference (MITD) of heat exchangers, JT valve temperature difference, and several temperature constraints to ensure thermodynamic behavior is not violated.

In this study, Mixture 8 (for SMR) and Mixture 6 (for DMR) appear to be the top-performing mixtures, achieving specific energy consumptions (SEC) of 1.25 kWh/kg_H2 and 1.13 kWh/kg_H2, respectively, with levelized precooling costs (LPC) of €0.47/kg_H2 and €0.60/kg_H2. The study indicates that aligning the boiling points of mixed refrigerant components to enhance temperature glide, combined with tuning of operating conditions, is the key to achieving both energy efficiency and cost competitiveness. Furthermore, the optimized results in DMR also suggest that the intermediate-compression stage in the MR2 cycle could be removed.

From the sensitivity analysis, it was observed that as the precooling temperature target increased, the gap in SEC between the SMR and DMR configurations narrowed. Starting from 95 K, both systems reached similar SEC values, highlighting equal technical performance. However, the LPC further SMR dominant over the DMR. This indicates that beyond this temperature target, DMR was no longer economically competitive. Additionally, variations in pressure drop across heat exchangers and coolers had a stronger impact on the SMR configuration. The percentage increase in SEC and LPC was more severe due to the accumulation of pressure losses within a single-loop cycle. In contrast, the DMR system distributes losses across two separate loops, making it less sensitive to pressure drop effects.