Predicting saturated vapor pressure of LNG from density and temperature data with a view to improving tank pressure management

Determining the saturated vapor pressure(SVP)of LNG requires detailed thermodynamic calculations based on compositional data.Yet LNG compositions and SVPs evolve constantly for LNG stored in tanks.Moreover,the SVP of the LNG in a tank influences boil-off rates and tank pressure trends.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.Machine learning models that accurately estimate LNG SVP from density and temperature inputs offer the potential to provide such information.A dataset of five distinct,internationally traded LNG cargoes is compiled with 305 data records representing a range of temperature and density conditions.This can be used graphically to interpolate LNG SVP.However,two machine learning methods are applied to this dataset to automate the SVP predictions.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.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.When applied to infill unknown LNG compositions the superior TOB method achieves prediction accuracy of RMSE~3kPaA and R^(2)=0.996.Predicting LNG SVP to this level of accuracy is beneficial for tank-pressure management decision making.

Prediction and datamining of burned areas of forest fires:Optimized data matching and mining algorithm provides valuable insight

An optimized data-matching machine learning algorithm is developed to provide high-prediction accuracy of total burned areas for specific wildfire incidents.It is applied to a well-studied forest-fire dataset from Portugal Montesinho Natural Park considering 13 input variables.The total burned area distribution of the 517 burn events in that dataset is highly positively skewed.The model is transparent and avoids regressions and hidden layers.This increases its detailed datamining capabilities.It matches the highest burned-area prediction accuracy achieved for this datasetwith a wide range of traditionalmachine learning algorithms.The two-stage prediction process provides informative feature selection that establishes the relative influences of the input variables on burned-area predictions.Optimizing with mean absolute error(MAE)and root mean square error(RMSE)as separate objective functions provides complementary information with which to data mine each total burnedarea incident.Such insight offers potential agricultural,ecological,environmental and forestry benefits by improving the understanding of the key influences associated with each burn event.Data mining the differential trends of cumulative absolute error and squared error also provides detailed insight with which to determine the suitability of each optimized solution to accurately predict burned-areas events of specific types.Such prediction accuracy and insight leads to confidence in how each prediction is derived.It provides knowledge to make appropriate responses and mitigate specific burn incidents,as they occur.Such informed responses should lead to short-term and long-term multi-faceted benefits by helping to prevent certain types of burn incidents being repeated or spread.