Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks

A feed-forward artificial neural network with 19 input variables (temperature, gas hydrate structure, gas composition and inhibitor concentration in aqueous phase) and 35 neurons in single hidden layer has been developed for estimating hydrate dissociation pressures of natural gases in the presence/absence of inhibitor aqueous solutions. The model has been developed using 3296 hydrate dissociation data gathered from the literature. The reliability of the method has been examined using independent experimental data (not used in training and developing the model). It is shown that the results of predictions are in acceptable agreement with experimental data indicating the capability of the artificial neural network for estimating hydrate stability zones of natural gases.

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