Thermodynamic, Economic, and Environmental Multi-Criteria Optimization of a Multi-Stage Rankine System for LNG Cold Energy Utilization

Utilizing the considerable cold energy in liquefied natural gas (LNG) through the organic Rankine cycle is a highly important initiative. A multi-stage Rankine-based power generation system using LNG cold energy for waste heat utilization was proposed in this study. Moreover, a comprehensive assessment method was used to select the working fluid for this proposed system. Not only were thermodynamic and economic indicators considered, but also the environmental impact of candidate working fluids was taken into account in the evaluation process. The optimal operating points of the system were determined using non-dominated sorting genetic algorithm II and TOPSIS methods, while employing Gray Relational Analysis was applied to compute the gray relational coefficients of candidate working fluids at the optimal operating points. In addition, four weighting methods were used to calculate the final gray correlation degree of the candidate working fluids by considering the weighting influence. The stability of the calculated gray correlation degree was observed by performing a standard deviation analysis. The results indicate that R245ca was chosen as the optimal working fluid due to its superior performance based on the entropy weighting method, the independent weighting coefficient method, and the mean weighting method. Simultaneously, R245ca exhibits the best specific net power output and levelized cost of energy values of 0.283 USD/kWh and 106.9 kWh/t, respectively, among all candidate working fluids. The gray correlation degree of R1233zd(E) is 0.948, exceeding that of R245ca under the coefficient of variation method. The gray correlation degree under the mean value method is the most stable, with a standard deviation of only 0.162, while the gray correlation degree under the coefficient of variation method exhibits the greatest fluctuation, with a standard deviation of 0.17, in the stability assessment.