%P 146-156
%I Elsevier Ltd
%T Experimental data, thermodynamic and neural network modeling of CO2 solubility in aqueous sodium salt of l-phenylalanine
%J Journal of CO2 Utilization
%R 10.1016/j.jcou.2017.03.011
%V 19
%L scholars8693
%K Amino acids; Carbon; Deep neural networks; Equilibrium constants; Neural networks; Salts; Solubility; Temperature, Effect of temperature; Kent-Eisenberg; l-Phenylalanine; Modeling technique; Neural network model; Pressure ranges; Solubility data; Solvent concentration, Carbon dioxide
%X In this study, experimental CO2 solubility in aqueous sodium salt of l-phenylalanine (Na-Phe) was investigated at concentrations (w = 0.10, 0.20, and 0.25) mass fractions. The solubility was measured in a high-pressure solubility cell at temperatures 303.15, 313.15 and 333.15 K, over a CO2 pressure range of (2-25) bar. The effect of temperature, equilibrium CO2 pressure and Na-Phe concentration on CO2 loading were examined. Two different models namely modified Kent-Eisenberg and artificial neural network (ANN) were used to correlate the CO2 solubility data. Carbamate hydrolysis and amine deprotonation equilibrium constants were estimated as a function of temperature, pressure and solvent concentration from modified Kent-Eisenberg model. Also, the comparison of prediction results obtained from both modeling techniques was carried out. It was found that ANN model performed better than modified Kent-Eisenberg model. © 2017 Elsevier Ltd. All rights reserved.
%O cited By 59
%D 2017
%A S. Garg
%A A.M. Shariff
%A M.S. Shaikh
%A B. Lal
%A H. Suleman
%A N. Faiqa