<scp>AI</scp> models for correlation of physical properties in system of <scp>1DMA2P‐CO₂‐H₂O</scp>

Abstract

Abstract In this work, the density, viscosity, and specific heat capacity of pure 1‐dimethylamino‐2‐propanol (1DMA2P) as well as aqueous unloaded and CO 2 ‐loaded 1DMA2P solution (with a CO 2 loading of 0.04–0.70 mol CO 2 /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‐H 2 O‐CO 2 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‐H 2 O‐CO 2 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 CO 2 absorption systems.