Artificial Neural Networks (ANNs) for Density and Viscosity Predictions of CO2 Loaded Alkanolamine + H2O Mixtures

Abstract

The physical properties, like density and viscosity, of alkanolamine + H 2 O (water) + CO 2 (carbon dioxide) mixtures receive a significant amount of attention as they are essential in equipment sizing, mathematical modelling and simulations of amine-based post-combustion CO 2 capture processes. Non-linear models based on artificial neural networks (ANNs) were trained to correlate measured densities and viscosities of monoethanol amine (MEA) + H 2 O, MEA + H 2 O + CO 2 , and 2-amino-2-methyl-1-propanol (AMP) + MEA + H 2 O + CO 2 mixtures and results were compared with conventional correlations found in literature. For CO 2 -loaded aqueous amine mixtures, results from the ANN models are in good agreement with measured properties with less than 1% average absolute relative deviation (AARD). The ANN-based methodology shows much better agreement (R 2 > 0.99) between calculated and measured values than conventional correlations.

Regression modelling for concentration dependent mass density and specific volume of aqueous surfactant solutions

“…Karunarathne et al performed artificial neural network modelling (ANN) for CO2 loaded aqueous amine mixtures. The results from the ANN models are in good agreement with measured properties with less than 1% average absolute relative deviation (AARD) [19].…”