Novel approaches for the prediction of density of glycol solutions

Two new approaches for the accurate prediction of densities of the commonly used glycol solutions in the gas-processing industry are presented in the article. The first approach is based on developing a simple-to-use polynomial correlation for an appropriate prediction of density of glycol solutions as a function of temperature and weight percent of glycols in water, where the obtained results show very good agreement with the reported experimental data. The second approach, however, is based on the artificial neural networks (ANN) methodology, wherein the results demonstrate the ability of the introduced method to predict reasonably accurate densities of glycols under operating conditions. Comparisons of the two novel approaches indicated that the simple-to-use correlation appears to be superior owing to its simplicity and clear numerical back-ground, wherein the relevant coefficients can be retuned if new and more accurate data are available in the future. The average deviation of the new proposed polynomial correlation results from reported data is 0.64 kg/m³ whereas the average deviation of artificial neural networks (ANN) methodology from reported data is 1.1 kg/m³. © 2008 CAS/DICP.