Facile estimation of viscosity of natural amino acid salt solutions: Empirical models vs artificial intelligence

Abstract

Natural amino acid salt solutions (NAASs) are paving the way for greener carbon capture. This study developed simple and precise methods for the viscosity modeling of NAASs. Two approaches, namely, empirical correlations and artificial intelligence, were assessed using a large databank (16 NAAs, 3 alkaline compounds, 25 NAASs, and 1582 data points). Two general correlations and a global equation were suggested. Benefitting from the input of single reference-point data, the modified global equation yielded the best results with a 2.28% deviation. The other empirical models represented viscosities with less than a 7.20% error. The second approach, employing artificial neural networks (ANNs) with different algorithms, was also proposed. The best ANNs were a single-layer perceptron network with tansig + trainlm functions, a double-layer perceptron network with logsig + tansig + trainlm functions, and a radial basis function network with the maximum neurons. They managed to calculate the viscosities with errors of 2.82%, 1.82%, and 0.47%, respectively.

Keywords

• Green solvents
• Natural amino acid
• Physical property
• Empirical model
• Machine learning