BTPC-Based DES-Functionalized CNTs for Removal from Water: NARX Neural Network Approach
Publication: Journal of Environmental Engineering
Volume 144, Issue 8
Abstract
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, . 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 of 0.9818.
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Acknowledgments
The authors express their thanks to the University of Malaya for funding this research (UMRG RP044D-17AET and RP025A-18SUS).
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Published In
Journal of Environmental Engineering
Volume 144 • Issue 8 • August 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Sep 23, 2017
Accepted: Feb 23, 2018
Published online: Jun 14, 2018
Published in print: Aug 1, 2018
Discussion open until: Nov 14, 2018
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