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.
Gas hydrates are ice-like crystalline compounds, inclusions. Their stability occur through occupation of suitable size gas components (guest molecules) into cavities formed by water molecules (host molecules). In 1810 hydrate was firstly discovered by Sir Humphrey Davy. During the first 100 years after the gas hydrates discovery, the interest in these compounds was highly concerned with the identification of (a) the species that can form hydrate and (b) the pressure and temperature conditions at which the formation occurs. Recently, naturally occurring clathrates hydrate in the earth which contain mostly methane are regarded as a future energy resource . Clathrate components are mainly two structures structure I and structure III, although structure II has also been known. Most studies of gas hydrates have concentrated upon measuring the three-phase dissociation pressure however; few data are available for (H-Lw) phase equilibria . In 1934, it was recognized that the plugging of natural gas pipelines was due to formation of hydrate of natural gas.
Typical operating problems include the fouling of heat exchangers and other vessels, erosion of expanders, in addition to plug gage of transmission lines with the solid hydrate . The best method for determining conditions of hydrate formation is to measure the formation experimentally at the temperature, pressure and composition of interest. It is impossible to satisfy the infinite numbers of conditions for which measurement are needed. Hydrate formation prediction methods are needed to interpolate between measurements. However, such experimental endeavors are both time consuming and expensive relative to industrial needs for a number of hydrate formation conditions. Therefore some means of interpolation between the experimental results are needed, and ideally the interpolation could be extrapolated beyond the condition of the data. The petroleum industry spends millions of dollars to combat the formation of hydrate. So, the accuracy of estimating the natural gas hydrate is extremely important for optimizing the cost of piping systems and processing units.