Prediction of Solid Vapor-liquid Equilibrium in Natural Gas using ANNs

In the last five decades, several studies have been performed on the measurement and prediction of hydrate forming conditions.

Many correlations were presented in the literature, but the most of these correlations considered pure gases and their mixtures which leads to low accuracy.

In additio, some of these correlations are presented mainly in graphical form, thus making it difficult to use them within general computer packages for simulation and design.

The purpose of this work is to present a comprehence neural network model for predicting the hydrate formation conditions for pure gases and gas mixtures. the neural network model enables the user to accurately predict hydrate formation conditions for a given gas mixture, without having to do costly experimental measurements.