Prediction of CO_2 absorption by nanofluids using artificial neural network modeling

摘要

This study focused on providing a model for prediction of CO_2 absorption by nanofluids in closed vessel absorber system, for the first time. A 6-input artificial neural network model was presented over 165 extracted experimental data related to CO_2 absorption by nanofluids. The used nanofluids were containing spherical nano-particles of SiO_2, Al_2O_3, Fe_3O_4 and TiO_2 dispersed in water, diethanolamine solution, propylene carbonate and sulfinol as base fluids, respectively. The effective parameters of temperature (T), initial pressure of CO_2 (p), time (t), density of nanoparticles (ρ_(np)), average diameter of nanoparticles (d_(np)) and mass concentration of nano-particles (Φ) were considered as input variables of the network, and the amount of CO_2 absorption (α) was chosen as target. The optimal ANN model was obtained in neuron 9. The mean square error (MSE), mean absolute error (MAE), and correlation coefficient (R) were found to be 0.0000236, 0.326 and 0.9996, respectively, for all data. These results showed a good accuracy and performance of developed ANN model in predicting of CO_2 absorption.