A new correlation and artificial neural network for the surface tension of aqueous alkanolamine solutions

In this study, a comprehensive review of existing experimental surface tension values for aqueous solutions of eleven alkanolamines, including monoethanolamine, diethanolamine, triethanolamine, methyldiethanolamine, 2-methylaminoethanol, diisopropanolamine, 3-amino-1-propanol, 2-ethylaminoethanol, 3-dimethylamino-1-propylamine, 1-amino-2-propanol, and dimethylethanolamine was compiled over the temperature range of (293.15–348.15) K. Furthermore, two new equations based on the corresponding state theory were developed for the surface tension of these aqueous solutions. Given the specific constraints imposed on the model coefficients, a robust optimization algorithm was employed to determine the optimal parameters, ensuring superior accuracy. The first equation, with a simpler structure, achieved an AARD% of 6.37, whereas the second equation, incorporating more complex terms, yielded an AARD% of 4.82. Moreover, a feed-forward artificial neural network was developed by using temperature, amine mole fraction, critical temperature and dipole moment as input variables. The model gives an overall AARD% equal to 0.203 and shows a very good capability in representing the nonlinear changes of the surface tension for aqueous alkanolamine mixtures.