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Prediction of parameters for boil-off gas in cryogenic tanks. Part 2. Analysis of modeling results

DOI: 10.17586/1606-4313-2025-24-2-14-22
UDC 621.64

Prediction of parameters for boil-off gas in cryogenic tanks. Part 2. Analysis of modeling results

Andrei V. Zaitsev, Saftli Adham

For citation: Zaitsev A.V., Saftli A. Prediction of parameters for boil-off gas in cryogenic tanks. Part 2. Analysis of modeling results. Journal of International Academy of Refrigeration. 2025. No 2. p. 14-22.DOI: 10.17586/1606-4313-2025-24-2-14-22

Abstract
This article reviews the results of theoretical and experimental studies by various authors concerning the processes of boil-off gas (BOG) accumulation during the storage of liquefied natural gas (LNG) in tanks of different types. Historically, modeling methods have evolved alongside the development of information technology, ranging from simple engineering approaches to modern numerical methods involving computational fluid dynamics (CFD). By consistently analyzing various quantitative and qualitative results related to changes in BOG parameters, conclusions can be drawn about the processes occurring in specific studied cases. Nevertheless, a unified approach to selecting modeling methods and interpreting their results has not yet been established. It is recognized that heat transfer plays a critical role in the process of thermal evaporation. A characteristic example of forecasting the quantity and rate of BOG generation is the thermal calculation of a Qmax tank module in Aspen HYSYS, applied to a 260000 m3 tank under phase equilibrium conditions at a pressure of 1,17 bar and ambient temperature of 25 °C, with various filling levels. The resulting boil-off rate (BOR) was 0,012 mass % per day at 80% fill level, increasing to 0,12 mass % per day at 10% fill level. To generalize the obtained results for BOG parameter prediction, it is necessary to continue accumulating and statistical processing the data.

Keywords: liquefied natural gas (LNG), boil-off gas (BOG), phase equilibrium, heat input, modeling, computational fluid dynamics (CFD).

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