Multi-Algorithm Optimization of SCRBC Parameters for Flue Gas Waste Heat Recovery of a Marine Low-Speed Engine
A one-dimensional simulation model of supercritical carbon dioxide(S-CO2)recompression Brayton cy-cle(SCRBC)was developed for the flue gas waste heat recovery(WHR)of a marine low-speed engine using the bench test data.The relationship between the pressure ratio and split ratio with the cycle efficiency and net recovery work was obtained by fitting a neural network model.A multi-objective genetic algorithm(MOGA)optimization with the technique for order preference by similarity to an ideal solution(TOPSIS)method was used to determine the optimal combination of cycle parameters for different loads of the low-speed engine,so that the cycle efficiency and the net recovered work can be optimized.The results show that at 100% load,when the split ratio is 0.117 and the pressure ratio is 1.804,the net recovered work reaches 178.14 kW and the Brayton cycle efficiency reaches 19.22% .This means that the total system efficiency is increased by 1.68% and the fuel consumption rate is reduced by 6.43 g/(kW·h).Exergy analysis result of the system shows that the maximum heat loss of the cooler is 85 kW and the heat loss efficiency is 11.44% .The heat exchanger heat loss is 32 kW and the heat loss efficiency is 3.44% .The performance optimization method of the S-CO2 recompression Brayton cycle for flue gas waste heat recovery could provide a useful measure for marine low-speed diesel engines.
marine low-speed enginerecompression Brayton cyclecycle parametersneural network model
万方数据
维普
