Prediction of carbon dioxide loading capacity in amino acid salt solutions as new absorbents using artificial neural network and Deshmukh–Mather models

Abstract

The aim of this work is solubility prediction of carbon dioxide (CO2) in amino acid salt solutions as new absorbents over wide ranges of operating conditions, utilizing Artificial Neural Network (ANN) and Deshmukh–Mather models. pH of solutions, overall molar concentration, partial pressure of CO2, apparent molecular weight and temperature was picked as input variables of the proposed ANN. A group of 1364 literature experimental data points for CO2 solubility have been congregated from the literature to build the network. The best architecture of the developed ANN including the numbers of hidden layer, transfer function and number of neurons were attained by utilizing these literature data points. Also CO2 solubility in amino acid salt solution was modeled using Deshmukh–Mather model. Results show that proposed ANN has better performance compared to Deshmukh–Mather model. The ANN was trained by the Levenberg–Marquardt back-propagation algorithm including two hidden layers with 8 and 7 neurons and Tan-sigmoid transfer function for the hidden and output layers. The model results show that proposed ANN that developed with amino acid salt solutions data points has ability to predict accurately the solubility of CO2 and H2S in dissimilar solutions with correlation coefficient (R2) of 0.9982 and Average Relative Deviation (ARD) value of 3.2976.