Optimization of Dual-Organic Rankine Cycle for LNG wide temperature range cold energy utilization based on Genetic Algorithm

Liquefied Natural Gas (LNG) cold energy power generation systems currently suffer from low efficiency. To tackle this issue, this study proposes a Genetic Algorithm (GA)-based collaborative optimization theory and method for the Dual-Organic Rankine Cycle (DORC). The core objectives are to realize stepped temperature utilization across different segments and to expand the temperature range for cold energy recovery. A dynamic simulation and optimization model coupled with MATLAB-HYSYS was established, and Working Fluid (WF) was selected with the maximum power generation as the core objective. In this study, ethylene-propane was selected as the dual-cycle WF from six alternative WF. The key decision variables were selected based on the core operating parameters of the DORC, and global optimization was performed using the GA. The results indicate that the changes in the temperature of LNG in the first-stage cycle (T LNG1 ) and the temperature in the second-stage cycle (T LNG2 ) dominate the impact on the power generation performance of DORC. After optimization, the total power generation of the system increased from 2087.15 kW to 3293.78 kW, resulting in a 57.81 % improvement in power generation efficiency. This research provides an effective solution for efficient LNG cold energy recovery over a wide temperature range. It is of great significance for energy conservation and emission reduction in such utilization. It also contributes to promoting the development of low-carbon energy systems and advancing green and sustainable development. • Propose GA-based DORC optimization for LNG cold energy stepped utilization. • Select ethylene-propane as dual-cycle working fluids, reducing cold energy waste to rate to below 8 %. • After GA optimization, power generation efficiency improves by 57.81 %. • Quantify impact weights of LNG cold energy power generation processes for targeted optimization.