AI models for correlation of physical properties in system of 1DMA2P-CO₂-H₂O

In this work, the density, viscosity, and specific heat capacity of pure 1-dimethylamino-2-propanol (1DMA2P) as well as aqueous unloaded and CO₂-loaded 1DMA2P solution (with a CO₂ loading of 0.04–0.70 mol CO₂/mol amine) were measured over the 1DMA2P concentration range of 0.5–3.0 mol/L and temperature range of 293–323 K. The observed experimental results of these thermophysical properties of the 1DMA2P-H₂O-CO₂ system were correlated using empirical models as well as artificial neural network (ANN) models (namely, back-propagation neural network [BPNN] and radial basis function neural network [RBFNN] models). It was found that the developed BPNN and RBFNN models could predict the experimental results of 1DMA2P-H₂O-CO₂ better than correlations using empirical models. The results could be treated as one of the accurate and potential methods to predict the physical properties for aqueous amine CO₂ absorption systems.