TY - JOUR
T1 - BTPC-Based DES-Functionalized CNTs for A s 3 + Removal from Water
T2 - NARX Neural Network Approach
AU - Fiyadh, Seef Saadi
AU - Alsaadi, Mohammed Abdulhakim
AU - Alomar, Mohamed Khalid
AU - Fayaed, Sabah Saadi
AU - Mjalli, Farouq S.
AU - El-Shafie, Ahmed
N1 - Publisher Copyright:
© 2018 American Society of Civil Engineers.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - 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.
AB - 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.
KW - Arsenic ions
KW - Carbon nanotubes
KW - Deep eutectic solvents
KW - NARX neural network
KW - Water treatment
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U2 - 10.1061/(ASCE)EE.1943-7870.0001412
DO - 10.1061/(ASCE)EE.1943-7870.0001412
M3 - Article
AN - SCOPUS:85048642540
SN - 0733-9372
VL - 144
JO - Journal of Environmental Engineering (United States)
JF - Journal of Environmental Engineering (United States)
IS - 8
M1 - 04018070
ER -