Prediction of Carbon Dioxide Equilibrium Solubility in Solution of Triethanolamine + Piperazine + Water using Artificial Neural Network Modeling

In this study, we developed artificial neural network-based model for prediction of equilibrium solubility of carbon dioxide in the amine solvent system of (triethanolamine + piperazine + water) for the purpose of carbon dioxide uptake. In the MLP model, the solubility data (CO2 loading in the amine solution) were investigated as functions of CO2 partial pressure, system temperature, and amine composition. The Levenberg–Marquardt back-propagation (LMP) algorithm was used to predict the partial pressure of carbon dioxide. The final ratio of training, validation, and test datasets was approximately 70:15:15. The optimum multilayer perceptron (MLP) structure in Levenberg-Marquardt algorithm for CO2 partial pressure is created with 20 neurons in the first hidden layer and 10 neurons in the second hidden layer. There was a 0.99546 correlation coefficient between the experimental results and the artificial neural network (ANN) calculations, demonstrating excellent compatibility between them. The best validation performance was 0.0043497 from epoch 13. In general, the results show that the applied model can provide an accurate prediction of partial pressure or solubility for different operating conditions.