predicting the hydrate formation temperature by a new correlation and neural network

gas hydrates are a costly problem when they plug oil and gas pipelines. the best way to determine the hft and pressure is to measure these conditions experimentally for every gas system. since this is not practical in terms of time and money, correlations are the other alternative tools. there are a small number of correlations for specific gravity method to predict the hydrate formation. as the hydrate formation temperature is a function of pressure and gas gravity, an empirical correlation is presented for predicting the hydrate formation temperature. in order to obtain a new proposed correlation, 356 experimental data points have been collected from gas-gravity curves. this correlation is programmed and assessed with respect to its capabilities to match experimental data published in the literature under varying system conditions (i.e. temperature, pressure, and composition).the spss software has been employed for statistical analysis of the data. in order to establish a method to predict the hydrate formation temperature, a new neural network has also been developed with the bp(back propagation) method. this neural network model enables the user to accurately predict hydrate formation conditions for a given gas mixture, without having to do costly experimental measurements.