Maximization of the power production in LNG cold energy recovery plant via genetic algorithm

This paper presents an optimization model via genetic algorithm (GA) to maximize the power generation potential of a liquefied natural gas (LNG) cold energy recovery plant. LNG releases a large amount of cold energy during vaporization prior to transport for service, and this cold energy can be effectively utilized to generate power using a heat engine. We performed a thermodynamic analysis for a power generation system combining the organic rankine cycle (ORC) driven by LNG exergy and the direct expansion cycle. Both LNG and the working fluid in the combined ORC are light hydrocarbon mixtures, and their physical properties were estimated using the Peng-Robinson equation. We conducted a thorough investigation of the effects that the working fluid composition brought about on the thermal efficiency of the heat engine through an analysis using Aspen HYSYS interfaced with a GA-based Matlab solver. The results showed that optimization of the working fluid composition led to an increase of 58.4% in the performance of the combined ORC in terms of the net work.