A Simulation-Optimization Framework for Enhancing the Sustainability of Hydrogen Liquefaction through Energy Consumption Minimization

• A 3-cycle MFC-based process with adiabatic reactor and liquid baths is simulated. • Four different Optimization techniques have been comprehensively evaluated. • Sensitivity analysis has been employed as a tool to setup system bounds. • Minimizing energy consumption enhances the sustainability of the process. Optimization is an essential tool to improve the efficiency and economic viability of the hydrogen liquefaction process. To reduce the specific energy consumption (SEC) of this process, several optimization techniques have been employed; however, comparative research on their efficacy is scarce. This work presents a comprehensive and analytical evaluation of four optimization methods applied to a three-cycle mixed fluid cascaded hydrogen liquefaction process. The methods are Aspen HYSYS® built-in optimizer (BOX), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Knowledge-Based Optimization (KBO). To simulate the actual process and improve ortho-to-para hydrogen conversion, the study incorporates a redesigned simulation framework that includes adiabatic looping reactors and a dedicated liquid hydrogen bath. The most important design factors were obtained through sensitivity analysis, which also determined the best lower and upper boundaries for optimization. The results show that KBO performs far better than other methods, attaining the lowest SEC (10.45 kWh/kgLH2), a 9.6% improvement over the base scenario. With a 7.7% decrease in SEC, PSO comes in second, proving its efficacy in managing constraints. On the other hand, due to initialization dependence and convergence in local optima, GA and BOX show limited benefits. This study demonstrates the importance of integrating domain expertise with computational optimization methods to enhance hydrogen liquefaction efficiency, paving the way for AI-assisted frameworks in sustainable energy systems.