The modelling of arsenic removal from water by deep eutectic solvents functionalized CNTs: Artificial neural network (ANN) approach

In this work, novel adsorbent was developed by using deep eutectic solvent system as functionalization agent of carbon nanotubes for the removal of arsenic ions from water.Artificial neural network (ANN) approach was used to predict arsenic removal from water.The developed adsorbent was characterized using Raman spectroscopy, Zeta potential and FTIR.The experimental work was designed to study adsorption process parameters and they were initial concentration of arsenic, adsorbent dosage, pH and contact time.After using three models to identify the suitable kinetic model with different pH values, the pseudo-second order best described the adsorption kinetics of the system.Different indicators were used to determine the efficiency and accuracy of the (BP-ANN) model which are (MSE), (RMSE), (RRMSE), (MAPE).Moreover, the (FB-ANN) adequacy was checked by coefficient of correlation R 2 which found to be 0.9968.By conducting a comparative study for the experimental and the predicted results, it was found that the (FB-ANN) model was able to predict the adsorption capacity of arsenic removal satisfactorily.