Predicting LNG SVP to this level of accuracy is beneficial for tank-pressure management decision making. When applied to infill unknown LNG compositions the superior TOB method achieves prediction accuracy of RMSE ∼3kPaA and R 2 = 0.996. The transparent open-box learning network (TOB), a regression-free optimized data matching algorithm predicts SVP of the dataset with RMSE = 0.59 kPaA and R 2 = 0.999. A simple multi-layer perceptron artificial neural network (MLP-ANN) predicts SVP of the dataset with root mean square error (RMSE) = 6.34 kPaA and R 2 = 0.975. 9 After a short time (up to a few minutes) the software will be automatically activated. However, two machine learning methods are applied to this dataset to automate the SVP predictions. Enter the 18-character Product Activation Code found on the Code Sheet (or provided by e-mail if you purchased online). This can be used graphically to interpolate LNG SVP. A dataset of five distinct, internationally traded LNG cargoes is compiled with 305 data records representing a range of temperature and density conditions. Machine learning models that accurately estimate LNG SVP from density and temperature inputs offer the potential to provide such information. In order to make improved tank pressure control decisions it would be beneficial for LNG tank operators to be made more constantly aware of the SVP of the LNG in a tank. Moreover, the SVP of the LNG in a tank influences boil-off rates and tank pressure trends. Yet LNG compositions and SVPs evolve constantly for LNG stored in tanks. Determining the saturated vapor pressure (SVP) of LNG requires detailed thermodynamic calculations based on compositional data.

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