Performance analysis and multi-objective optimization of recompression S-CO2 Brayton cycle
The solar thermal power generation technology combined with heat storage has stable output and strong peak shaving capabilities.The introduction of supercritical carbon dioxide(S-CO2)Brayton cycle can further improve thermoelectric conversion efficiency.Most of the existing studies evaluated the performance of S-CO2 cycle based on a single index,leading to inconsistent evaluation results.Hence,it is necessary to carry out multi-index comprehensive evaluation to objectively reflect the cycle performance.In the present paper,mathematical models were established to investigate the thermodynamic performance and economy of a 35 MW recompression S-CO2 cycle,and the effects of critical parameters on cycle performance was analyzed.A BP-GA optimization method of back propagation neural network combined with elitist nondominated sorting genetic algorithm was constructed for multi-objective optimization of cycle performance.The results indicate that the cycle efficiency increases with an increasing total thermal conductivity of the recuperators,but there is a ceiling on growth.There are significant non-monotonic relations between turbine inlet temperature,minimum cycle pressure,maximum cycle pressure,split ratio and cycle performance,and the corresponding optimal values are 639.14℃,8.10 MPa,29.74 MPa and 0.70,respectively.Compared with the cycle performance based on the design conditions,the optimized cycle shows a reduction of 11.1%in LCOE,and an increase of 5.1%and 27.6%in efficiency and specific work,respectively.
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