Applying MLP-ANN as a novel and accurate method to estimate gas density

Abstract

In the gas industries, to increase the degree of accuracy of calculation and estimation in different processes, the importance of accurate prediction of gas properties is highlighted. The gas density, as one of the key properties in gas engineering, has a major effect in calculations. So, in the present paper, multi-layer perceptron artificial neural network (MLP-ANN) was used to predict the gas density based on molecular weight, critical pressure and critical temperature of gas, pressure, and temperature. To this end, a total number of 1240 reliable data of gas density were gathered from literature for the training and testing phases. The MLP-ANN outputs were compared with the actual data in different manners, such as statistical and graphical analyses. The coefficient of determination (R2), average absolute relative deviation (AARD), and root mean squared error (RMSE) for overall process were calculated as 1, 0.0088444, and 0.0259, respectively. The determined parameters and graphical analysis showed that the MLP-ANN has great potential and high degree of accuracy in gas density estimation.