A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice

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A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice Publisher

Heibati B^{1, 2} ; Rodriguezcouto S^{3, 4} ; Ozgonenel O⁵ ; Turan NG⁶ ; Aluigi A⁷ ; Zazouli MA⁸ ; Ghozikali MG² ; Mohammadyan M¹ ; Albadarin AB⁹

Show Affiliations

1. Faculty of Health and Health Sciences Research Center, Department of Occupational Health Engineering, Mazandaran University of Medical Sciences, Sari, Iran
2. Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
3. CEIT, Unit of Environmental Engineering, Paseo Manuel de Lardizabal 15, San Sebastian, 20018, Spain
4. IKERBASQUE, Basque Foundation for Science, Alameda de Urquijo 36, Bilbao, 48011, Spain
5. Electrical & Electronic Engineering Department, Ondokuz Mayis University, Samsun, 55139, Turkey
6. Environmental Engineering Department, Ondokuz Mayis University, Samsun, 55139, Turkey
7. CNR-ISOF (National Research Councilâ€”Institute of Organic Chemistry and Photoreactivity), via P. Gobetti 101, Bologna, 40129, Italy
8. Faculty of Health and Health Sciences Research Center, Department of Environmental Health Engineering, Mazandaran University of Medical Sciences, Sari, Iran
9. Department of Chemical and Environmental Sciences, University of Limerick, Limerick, Ireland

Source: Desalination and Water Treatment Published:2016

Abstract

In this study, the potential of natural pumice (NP) and iron-coated pumice stone (Fe-CP) as novel low-cost adsorbents to remove ethidium bromide (EtBr) from aqueous solutions was investigated. The operational parameters affecting removal efficiency and adsorption capacity such as adsorbent dose, initial EtBr concentration, pH, and contact time were studied in order to maximize EtBr removal. The maximum amount of adsorbed EtBr (qm) using NP and Fe-CP was 40.25 and 45.08 mg g‒1, respectively. It was found that EtBr adsorption followed the Freundlich isotherm model and fitted the pseudo-second-order kinetics equation for both adsorbents. In addition, the experimental system could be easily modeled by artificial neural network calculations. © 2015 Balaban Desalination Publications. All rights reserved.

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Style	Citing Format
MLA	Heibati B, et al.. "A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice." Desalination and Water Treatment, vol. 57, no. 29, 2016, pp. 13472-13483.
APA	Heibati B, Rodriguezcouto S, Ozgonenel O, Turan NG, Aluigi A, Zazouli MA, Ghozikali MG, Mohammadyan M, Albadarin AB (2016). A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice. Desalination and Water Treatment, 57(29), 13472-13483.
Chicago	Heibati B, Rodriguezcouto S, Ozgonenel O, Turan NG, Aluigi A, Zazouli MA, Ghozikali MG, Mohammadyan M, Albadarin AB. "A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice." Desalination and Water Treatment 57, no. 29 (2016): 13472-13483.
Harvard	Heibati B et al. (2016) 'A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice', Desalination and Water Treatment, 57(29), pp. 13472-13483.
Vancouver	Heibati B, Rodriguezcouto S, Ozgonenel O, Turan NG, Aluigi A, Zazouli MA, et al.. A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice. Desalination and Water Treatment. 2016;57(29):13472-13483.
BibTex	@article{ author = {Heibati B and Rodriguezcouto S and Ozgonenel O and Turan NG and Aluigi A and Zazouli MA and Ghozikali MG and Mohammadyan M and Albadarin AB}, title = {A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice}, journal = {Desalination and Water Treatment}, volume = {57}, number = {29}, pages = {13472-13483}, year = {2016} }
RIS	TY - JOUR AU - Heibati B AU - Rodriguezcouto S AU - Ozgonenel O AU - Turan NG AU - Aluigi A AU - Zazouli MA AU - Ghozikali MG AU - Mohammadyan M AU - Albadarin AB TI - A Modeling Study by Artificial Neural Network on Ethidium Bromide Adsorption Optimization Using Natural Pumice and Iron-Coated Pumice JO - Desalination and Water Treatment VL - 57 IS - 29 SP - 13472 EP - 13483 PY - 2016 ER -

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