Energy optimization of hydrogen liquefaction process via process knowledge combined with multivariate Coggin's algorithm

Abstract

Sustainable and cost-effective transportation of hydrogen is one of the major challenges to futuristic hydrogen value chain. Hydrogen, in its produced form, exhibits low energy density and large volume, rendering it highly unsuitable for transportation, particularly over extended distances. Liquefaction is one of the promising approaches to improve hydrogen's energy density and to make it more compact. However, liquefaction is an energy intensive process, raising the cost of transportation. In this study, the specific energy consumption of the hydrogen liquefaction process is aimed to reduce through knowledge-based optimization followed by optimization using the multivariate Coggin's algorithm to improve energy and economic profiles of the process. Design variables, composite curve, exergy, and economic analyses are performed to evaluate and compare the thermodynamic, economic, and environmental feasibility of the optimized process. The results depict a significant reduction (14.3%) in specific energy consumption to 9.40 kW/kgLH2 without increasing process complexity. Further key improvements include a massive drop in refrigerant flowrates (9.2%), annualized cost (m$ 0.15), and annual CO2 emissions (1008.45 tCO2/annum). The largest exergy destruction is observed in CHX-201, reflecting further room for improvement. These results underscore the potential for optimizing hydrogen liquefaction processes to enhance energy efficiency, process economics and environmental impact, providing valuable insights for researchers and industry professionals seeking to advance hydrogen liquefaction technologies. However, challenges remain in the comprehensive evaluation of chemical exergy for ortho- and para-hydrogen and in conducting advanced exergy analyses to further optimize hydrogen liquefaction processes without adding complexity.