Application of RBF-ANN in prediction of natural gas density in different operational conditions

The degree of accuracy in prediction of different processes of gas engineering is extensively dependent on gas properties. One of the dominant properties which has straight effects on calculation and performance of different parts of gas industries is natural gas density. Due to this fact, in this paper, radial basis function artificial neural network (RBF-ANN) was used as novel approach to estimate gas density in terms of molecular weight, critical pressure and critical temperature of gas, pressure and temperature. To prepare and validate RBF-ANN model, a large and reliable experimental data bank was gathered from literature. A comprehensive analysis which include statistical and different graphical analysis were done to evaluate the performance. The coefficients of determination (R2) were determined as 0.99995 and 0.99993 for training and testing phases respectively. The comparisons illustrate that the RBF-ANN has great potential in prediction of natural gas density at different operational conditions.