Prediction of solubility of CO2 in ethanol–[EMIM][Tf2N] ionic liquid mixtures using artificial neural networks based on genetic algorithm

Abstract

This paper proposes artificial neural networks (ANNs) technique as a new approach to predict solubility of CO2 in ethanol–[EMIM][Tf2N] ionic liquid mixtures. The solubility equilibrium data of CO2 were measured at 0, 20, 50 and 80 and 100wt.% mixture of [EMIM][Tf2N] ionic liquid, temperatures of 313.2 and 333.2°C, and pressure range of 0–7MPa. A feed-forward multilayer perceptron (MLP) neural network with Levenberg-Marquardt learning algorithm was developed for prediction task. The ANN model was trained, validated and tested using 70%, 15% and 15% of all solubility data, respectively. An optimization procedure was performed based on genetic algorithm to select the best ANN architecture. Therefore, a three layer feed-forward neural network with Levenberg-Marquardt back-propagation training algorithm was developed and designed with four variables as inputs and one variable as output, 15 neurons in the hidden layer, log-sigmoid transfer function in the hidden layer and linear transfer function in the output layer. Based upon statistical analysis, results obtained demonstrated that there is a very little difference between predicted and experimental data of CO2 capture rate giving very low value of average absolute deviation (AAD) and high value of least square (R2) very close to one, indicating high accuracy of this model to predict output variable. The results also proved that the developed ANN model outperforms the Peng-Robinson model.