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Adsorption and kinetic studies using nano zero valent iron (nZVI) in the removal of chemical oxygen demand from aqueous solution with response surface methodology and artificial neural network approach
Journal of Environment & Biotechnology Research, Vol. 7, No. 2, Pages 12-22, Apr 2018
Rabie S. Farag, Maha M. Elshfai, Ahmed S. Mahmoud
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ABSTRACT
Magnetic nanosorbents such nZVI proved to be effective in different contaminant removal from aqueous solutions. The prepared nZVI was characterized using SEM, XRD, EDAX and UV-Vis scanning spectrum. This study explores different adsorption and kinetic models that can describe the adsorption process of COD into nZVI. The nZVI and standard COD solution were prepared in the laboratory. The effect of nZVI on COD removal was studied at different absorbent dose, contact time, temperature, stirring rate, pH, and initial COD concentration. The results indicated that nZVI is effective in the removal of COD from aqueous solution, where removal efficiencies of 66 and 92% were achieved for 800 ± 10.2 and 100 ± 1.27 mg/L initial COD concentration, respectively, after 20 min of contact time using dose 0.6 g/L at pH 6, temperature 35 ˚C with fixed stirring rate 200 rpm. The isotherm studies were determined using nine nonlinear models. The kinetic data were evaluated using five nonlinear models including pseudo first, second order, Elovich, Avrami and Intraparticulate models. The adsorption data of COD fitted well to Freundlich model with lowest summation of error of 0.842 and pseudo second order kinetic model with lowest summation of error of 0.057. Artificial Neural Network (ANN) with a structure of 7 - 2 - 1 was used to predict the COD removal efficiency. The proposed ANN was found to be effective in simulating the performance of nZVI for COD removal, where a sum of squares error 0.092 and 3.204 for testing and training, respectively with relative error 0.030 and 0.256, respectively. RSM result showed R2 0.898 indicated that model was significant with experimental data.
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Adsorption and kinetic studies using nano zero valent iron (nZVI) in the removal of chemical oxygen demand from aqueous solution with response surface methodology and artificial neural network approach
Journal of Environment & Biotechnology Research, Vol. 7, No. 2, Pages 12-22, Apr 2018
Rabie S. Farag, Maha M. Elshfai, Ahmed S. Mahmoud
VIEW FULL TEXT IN PDF
ABSTRACT
Magnetic nanosorbents such nZVI proved to be effective in different contaminant removal from aqueous solutions. The prepared nZVI was characterized using SEM, XRD, EDAX and UV-Vis scanning spectrum. This study explores different adsorption and kinetic models that can describe the adsorption process of COD into nZVI. The nZVI and standard COD solution were prepared in the laboratory. The effect of nZVI on COD removal was studied at different absorbent dose, contact time, temperature, stirring rate, pH, and initial COD concentration. The results indicated that nZVI is effective in the removal of COD from aqueous solution, where removal efficiencies of 66 and 92% were achieved for 800 ± 10.2 and 100 ± 1.27 mg/L initial COD concentration, respectively, after 20 min of contact time using dose 0.6 g/L at pH 6, temperature 35 ˚C with fixed stirring rate 200 rpm. The isotherm studies were determined using nine nonlinear models. The kinetic data were evaluated using five nonlinear models including pseudo first, second order, Elovich, Avrami and Intraparticulate models. The adsorption data of COD fitted well to Freundlich model with lowest summation of error of 0.842 and pseudo second order kinetic model with lowest summation of error of 0.057. Artificial Neural Network (ANN) with a structure of 7 - 2 - 1 was used to predict the COD removal efficiency. The proposed ANN was found to be effective in simulating the performance of nZVI for COD removal, where a sum of squares error 0.092 and 3.204 for testing and training, respectively with relative error 0.030 and 0.256, respectively. RSM result showed R2 0.898 indicated that model was significant with experimental data.
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Journal of Environment and Biotechnology Research
Volume 7, Number 2, 2018, pp. 12-22
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