A Thermo-economic optimization of dual multicomponent refrigerant Cycles: Minimizing operational expenditure (OPEX) in natural gas liquefaction

Muhammad Yasir, Saad Saeed, Arslan Khan and Imtiaz Ali

Energy, 2025, vol. 334, issue C

Abstract: Natural gas (NG) liquefaction processes demand substantial energy, primarily due to the overall power consumption of refrigeration units. Conventional dual mixed refrigerant-based processes, consisting of two refrigerants in separate cycles, typically produce liquefied natural gas (LNG) at a temperature of −160 °C and atmospheric pressure. In this work, a dual multicomponent NG liquefaction process is modeled that produces the LNG at an elevated pressure of 27 bar and temperature typically above −112 °C. This particular technology has reduced the total refrigeration load required because the process no longer needs extreme cooling. To further reduce the power consumption of refrigeration and to enhance economic efficiency, optimization of the composition of both refrigerants simultaneously using a Genetic Algorithm (GA) by integrating MATLAB with Aspen Plus has been performed. GA has successfully optimized the composition, present in the original work, of both refrigerants. The optimum composition of the dual multicomponent refrigerants, derived from GA, has led to a 26 % reduction in the total power consumption of compressors from 530.5 to 392.4 kJ/kg LNG, which subsequently resulted in a reduction in the utility cost of compression. Additionally, the optimized refrigerant composition reduced the cooling duties of intercoolers by approximately 13 %, further decreasing cooling utility expenses. Collectively, the optimized composition offers a 23 % reduction in overall operational expenditures (considering only utility costs) from $14.61/tonne LNG to $11.23/tonne LNG produced, compared to the original work. This highlights a significant improvement in process efficiency and cost-effectiveness of the NG liquefaction processes.

Keywords: Natural gas; Liquefaction; Dual mixed refrigerant; Genetic algorithm; Optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:334:y:2025:i:c:s0360544225032049

DOI: 10.1016/j.energy.2025.137562

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