Efficient Design of the Hydrogen Liquefaction System: Thermodynamic, Economic, Environmental, and Uncertainty Perspectives

Hydrogen (H2) liquefaction is one of the most promising approaches for storing and transporting clean energy on a large scale for long periods. However, this strategy faces the challenges of high energy consumption, relatively low exergy efficiency, substantial economic costs, boil-off gas losses, and limited knowledge of its environmental perspectives. A robust systematic framework is introduced by integrating thermodynamic, machine learning (ML), and multiobjective optimization (MOO) approaches to optimize the operational variables of the H2 liquefaction process. The H2 liquefaction process includes a mixed refrigerant precooling unit and a Joule-Brayton cryogenic cascade cycle. The combination of the pinch analysis approach and enumerative algorithms is used in the initial optimization phase as a nonlinear method to determine the operational variables of the precooling and liquefaction systems. The exergy efficiency and exergy destruction of H2 liquefaction cycles are calculated as 49% and 5073 kW to produce 50 tons/day of liquid H2. Based on life cycle assessment and economic analysis, the global warming and levelized cost to produce 1 kg liquid H2 are calculated at 124 kgCO2eq and 4. 833 US, respectively. The sensitivity analysis, ML, and MOO algorithms (particle swarm, genetic algorithm, and gray wolf techniques) in the final optimization phase are used to determine the Pareto frontier. The multicriteria decision techniques are used to identify the optimal operating conditions considering the thermodynamic, economic, and environmental aspects. The uncertainty levels of objective functions based on different parameters are studied by uncertainty quantification using Monte Carlo.