Multi-objective optimization of recompression S-CO2 cycle for gas turbine waste heat recovery

Abstract

Recompression supercritical carbon dioxide Brayton cycle (RCSCBC) has important application prospects in the fields of gas turbine waste heat recovery. A RCSCBC model considering finite temperature difference heat transfer between heat reservoir and working fluid, irreversible compression, irreversible expansion and other irreversibility losses is established. Then the mass flow rate of the working fluid, cycle pressure ratio and the heat conductance distribution ratios of four heat exchangers are taken as optimization variables, the multi-objective coordinated optimization with net power output, exergy efficiency, thermal efficiency and ecological function as the goals is carried out, and an artificial neural network model is constructed to solve the problem of long optimization time. The results show that the decision-making result by Shannon Entropy method for the four-objective optimization has the lowest deviation index, and is the optimal. Compared with the initial design point, the thermal efficiency of the cycle for the optimization results by the Shannon Entropy method can reach 40.69%, which is increased by 29.8%. The net power output can reach 10.972 MW, which is increased by 9.5%. The ecological function can reach 7.265 MW, which is increased by 114.75%. The exergy efficiency can reach 74.72%, which is increased by 24.22%.