The Prediction of the Compressibility Factor of Sour and Natural Gas by an Artificial Neural Network System

摘要

In this study, a multilayer normal feed-forward artificial neural network with three layers has been developed for prediction of compressibility factors of gases with different compositions. This model was developed using 977 experimental data of compressibility factors obtained from the literature. In this model, some statistical criteria such as R , root mean square error (RMSE), and average absolute deviation (AAD) are obtained 0.991, 0.024, and 0.965, respectively. Model validity was obtained by comparison between the experimental results and the results of various equations of state, such as van der Waals (1910), Redlich-Kwong (1949), Lawal-Lake-Silberberg (Lawal, 1999), Peng-Robinson (1976), and pseudo-experimental correlations such as Dranchuk-Abu-Kassem (1975), Dranchuk-Purvis-Robinson (1974), Hall-Yarborough (1973), Brill-Beggs (1974), Shell Oil Company (2003), Gopal (1977) obtained result of this model. It can be inferred that the results of this model are compatible with the experimental data and the obtained result of this model is more accurate than other correlations.