ANN-Based Correlations for Excess Properties to Represent Density and Viscosity of Aqueous Monoethanol Amine (MEA) Mixtures.


  • Sumudu Karunarathne
  • Khim Chhantyal
  • Lars Øi



excess properties, ANN, density, viscosity


The applicability of Artificial Neural Networks (ANNs) to represent excess properties is discussed. The excess molar volume VE and excess free energy of activation for viscous flow ΔGE* were calculated from measured density and viscosity at different monoethanol amine (MEA) concentrations and temperatures. Different ANNs with multiple inputs and a single hidden layer were trained, validated and tested to represent molar volumes VE and the excess free energy ΔGE*. Developed ANN models show good accuracies in data fitting by giving R2 as 0.99 and 0.98 for VE and ΔGE* respectively for the test data. The calculated average absolute relative deviation (AARD) for VE and ΔGE* are 1.5% and 1.2% respectively for the test data that give better predictions for the density and viscosity using a Redlich and Kister polynomial for the regression. The density and viscosity models based on ANN for VE and ΔGE* give high accuracies, which is an advantage of many aspects in engineering applications.


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