In this study, a novel adsorbent process was developed using a deep eutectic solvent (DES) system based on benzyltriphenylphosphonium chloride (BTPC) as a functionalization agent of carbon nanotubes (CNTs) for arsenic ion removal from water. The nonlinear autoregressive network with exogenous inputs (NARX) neural network strategy was used for the modeling and predicting the adsorption capacity of functionalized carbon nanotubes. The developed adsorbent was characterized using zeta potential, Fourier transform infrared (FTIR), and Raman spectroscopy. The effects of operational parameters such as initial concentration, adsorbent dosage, pH, and contact time are studied to investigate the optimum conditions for maximum arsenic removal. Three kinetic models were used to identify the adsorption rate and mechanism, and the pseudo-second order best described the adsorption kinetics. Four statistical indicators were used to determine the efficiency and accuracy of the NARX model, with a minimum value of mean square error, 6.37×10-4. In addition, a sensitivity study of the parameters involved in the experimental work was performed. The NARX model prediction was consolidated with the experimental result and proved its efficiency at predicting arsenic removal from water with a correlation coefficient R2 of 0.9818.
|دورية||Journal of Environmental Engineering (United States)|
|المعرِّفات الرقمية للأشياء|
|حالة النشر||Published - أغسطس 1 2018|
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