Artificial Neural Networks for Accurate Prediction of Physical Properties of Aqueous Quaternary Systems of Carbon Dioxide (CO2)-Loaded 4-(Diethylamino)-2-butanol and Methyldiethanolamine Blended with Monoethanolamine

Abstract

Physical and heat transport properties such as density, viscosity, refractive index, heat capacity, thermal conductivity, and thermal diffusivity of aqueous carbon dioxide (CO2)-loaded and unloaded 4-(diethyl amino)-2-buthanol (DEAB) and methyldiethanolamine (MDEA) as single amines and each blended with a primary amine (MEA) were measured at different ranges of temperature (25–60 °C), amine concentrations (0.5–2 M for tertiary amine and 5 M for primary amine), and CO2 loading (0–0.6 mol/mol amine). Results showed an increasing trend of CO2 loading on density, viscosity, and refractive index and a decreasing trend on the heat transport properties. Two artificial neural network techniques, back propagation neural network (BPNN) and radial basis neural network (RBFNN) as well as some well-known semi-empirical correlations from literature, were applied to correlate and predict these physical properties for two quaternary systems: MEA+DEAB+water+CO2 and MEA+MDEA+water+CO2. Results from the correlation showed t...