Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach

In this paper, the deep eutectic solvent-functionalized carbon nanotube was used for arsenic removal from water solution. The adsorbent used was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) and zeta potential. The effect of the parameters (adsorbent dosage, pH, initial concentration and contact time) was studied to find the optimum conditions for maximum adsorption capacity of the functionalized carbon nanotube. The pseudo-second-order, the pseudo first-order and intraparticle diffusion kinetic models were applied to identify the adsorption rate and mechanism, the pseudo-second-order model best described the adsorption kinetics of the system. The non-linear autoregressive network with exogenous inputs (NARX) neural network strategy was used for the modelling and predicting of the adsorption capacity of functionalized carbon nanotube. Different indicators were used to determine the efficiency and accuracy of the NARX neural network model which were mean square error (MSE), root mean square error (RMSE), relative root mean square error (RRMSE) and mean absolute percentage error (MAPE). The sensitivity study of the used parameters in the experimental work was completed. Comparison of the NARX model results with the experimental data confirmed that the NARX model was able to predict the arsenic removal from water