Research On Dynamic BOG Generation Model Of LNG Satellite Statio

With the development of the economy,the consumption of energy structure has also changed,and Liquefied Natural Gas(LNG)as a transitional material has gradually expanded its share in the world energy industry.The control and accurate prediction of Boil-off Gas(BOG)generated by LNG receiving stations is the key to storage tank design and the theoretical basis for subsequent BOG re-cycling.The current formula for calculating BOG generation does not accurately calculate the BOG generation under dynamic conditions,which is due to the fact that the flash steam caused by pressure drop when the submerged liquid pump outgoing LNG does not get accurate calculation.In this paper,Fluent2021 R2 is used to analyze the causes of BOG generation at 80%,55% and 30% for the external wall heat transfer only(storage process)and the external LNG transfer by submersible pump(loading process and vaporization process),respectively,and to establish an optimization model for LNG flashing based on pressure change,analyze liquid surface temperature,storage tank pressure,saturation temperature,wall heat leakage,submersible pump heat dissipation,liquid surface The model is based on Back Propagation(BP)neural network to predict BOG generation.The results show that the main causes of BOG generation under external transmission conditions include: evaporation caused by strong heat dissipation of the submerged pump during operation and LNG flash caused by pressure fluctuation due to external LNG transmission,among which the proportion of BOG generated by flash reaches 30%.In addition,the BP network prediction model of BOG dynamic generation is only 2.47% wrong compared with the original theoretical model.The more accurate BOG dynamic generation prediction model proposed in this study can greatly improve the energy-saving efficiency of BOG re-liquefaction process,and provide theoretical basis for the optimized design of BOG recycling process in LNG satellite stations.