Predicting the conditions for gas hydrate formation

Gas hydrate is a crystalline mixture obtained from gas molecules trapped in the cavity of hydrogen bonding water. To date, an essential step in the development of natural gas industry has been the acquisition of knowledge in the operation and handling of gas under high pressure without hydrate formation. Since there are several ways to predict hydrate formation, this study investigates predicting hydrate formation using the Katz method. In addition, several new models for accurate estimation of gas hydrate formation conditions will be provided. These models are based on artificial neural network (ANN) requirements. To create the model, predictive experimental data published in books and journals, as well as data extracted from Katz graph (Katz chart), estimate the formation conditions of gas hydrate. We validate the model created with the use of various statistical parameters such as mean squared error (MSE) and R²-value. The result of these parameters in models created to predict the formation of hydrates accurately and efficiently is evaluated. In this study, our goals are to use an artificial intelligence neural network to predict the formation of gas hydrates.