Prediction of solid-vapor-liquid equilibrium in natural gas using ANNs

Abstract

In the last five decades, several studies have been performed on the measurement and predication of hydrate forming conditions. Many correlations were presented in the literature, but most of these correlations considered pure gases and their mixtures which leads to low accuracy. In addition, 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 comprehensive neural network model for predicting 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. Copyright 2011, International Petroleum Technology Conference.