Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: Impact of operational parameters and artificial neural network modeling

Amirhosein Ramazanpour Esfahani; Saeid Hojati; Amin Azimi; Meysam Farzadian; Alireza Khataee

doi:10.1016/j.jtice.2014.11.011

Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: Impact of operational parameters and artificial neural network modeling

Amirhosein Ramazanpour Esfahani^*, Saeid Hojati, Amin Azimi, Meysam Farzadian, Alireza Khataee

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

42 Citations (Scopus)

Abstract

The efficiency of zero-valent iron nanoparticles (ZVINs) for the removal of chromium Cr(VI) from solutions is strongly decreased due to particle agglomeration. To solve this problem, a sepiolite-stabilized ZVIN (S-ZVIN) composite was made using a liquid-phase method and then characterized employing scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometer (EDS). Batch experiments were also conducted to (1) investigate the influence of various experimental variables on the removal efficiency of Cr(VI), (2) compare the removal efficiency of bare ZVIN and S-ZVIN treatments and (3) evaluate the capability of the artificial neural network (ANN) technique to model the Cr(VI) removal. The Cr(VI) removal efficiency was enhanced by increasing S-ZVIN dosage while a considerable decrease was observed by increasing the initial Cr(VI) concentration. The acidic and neutral pH values were appropriate for Cr(VI) removal. The enhancement was observed in Cr(VI) removal by increasing chloride concentration. Additionally, pseudo first-order showed better performance than pseudo second-order kinetic model to fit the experimental data of Cr(VI) removal. The ANN model could predict the experimental data of Cr(VI) removal with a determination coefficient of 0.9803. The relative significance of each input variable on the removal of Cr(VI) was calculated.

Original language	English
Pages (from-to)	172-182
Number of pages	11
Journal	Journal of the Taiwan Institute of Chemical Engineers
Volume	49
DOIs	https://doi.org/10.1016/j.jtice.2014.11.011
Publication status	Published - 2015
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2014 Taiwan Institute of Chemical Engineers.

Keywords

ANN modeling
Chromium
Nanocomposite
Sepiolite
Zero-valent iron

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10.1016/j.jtice.2014.11.011

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Ramazanpour Esfahani, A., Hojati, S., Azimi, A., Farzadian, M., & Khataee, A. (2015). Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: Impact of operational parameters and artificial neural network modeling. Journal of the Taiwan Institute of Chemical Engineers, 49, 172-182. https://doi.org/10.1016/j.jtice.2014.11.011

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title = "Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: Impact of operational parameters and artificial neural network modeling",

abstract = "The efficiency of zero-valent iron nanoparticles (ZVINs) for the removal of chromium Cr(VI) from solutions is strongly decreased due to particle agglomeration. To solve this problem, a sepiolite-stabilized ZVIN (S-ZVIN) composite was made using a liquid-phase method and then characterized employing scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometer (EDS). Batch experiments were also conducted to (1) investigate the influence of various experimental variables on the removal efficiency of Cr(VI), (2) compare the removal efficiency of bare ZVIN and S-ZVIN treatments and (3) evaluate the capability of the artificial neural network (ANN) technique to model the Cr(VI) removal. The Cr(VI) removal efficiency was enhanced by increasing S-ZVIN dosage while a considerable decrease was observed by increasing the initial Cr(VI) concentration. The acidic and neutral pH values were appropriate for Cr(VI) removal. The enhancement was observed in Cr(VI) removal by increasing chloride concentration. Additionally, pseudo first-order showed better performance than pseudo second-order kinetic model to fit the experimental data of Cr(VI) removal. The ANN model could predict the experimental data of Cr(VI) removal with a determination coefficient of 0.9803. The relative significance of each input variable on the removal of Cr(VI) was calculated.",

keywords = "ANN modeling, Chromium, Nanocomposite, Sepiolite, Zero-valent iron",

author = "{Ramazanpour Esfahani}, Amirhosein and Saeid Hojati and Amin Azimi and Meysam Farzadian and Alireza Khataee",

note = "Publisher Copyright: {\textcopyright} 2014 Taiwan Institute of Chemical Engineers.",

year = "2015",

doi = "10.1016/j.jtice.2014.11.011",

language = "English",

volume = "49",

pages = "172--182",

journal = "Journal of the Taiwan Institute of Chemical Engineers",

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Ramazanpour Esfahani, A, Hojati, S, Azimi, A, Farzadian, M & Khataee, A 2015, 'Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: Impact of operational parameters and artificial neural network modeling', Journal of the Taiwan Institute of Chemical Engineers, vol. 49, pp. 172-182. https://doi.org/10.1016/j.jtice.2014.11.011

Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: Impact of operational parameters and artificial neural network modeling. / Ramazanpour Esfahani, Amirhosein; Hojati, Saeid; Azimi, Amin et al.
In: Journal of the Taiwan Institute of Chemical Engineers, Vol. 49, 2015, p. 172-182.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite

T2 - Impact of operational parameters and artificial neural network modeling

AU - Ramazanpour Esfahani, Amirhosein

AU - Hojati, Saeid

AU - Azimi, Amin

AU - Farzadian, Meysam

AU - Khataee, Alireza

PY - 2015

Y1 - 2015

N2 - The efficiency of zero-valent iron nanoparticles (ZVINs) for the removal of chromium Cr(VI) from solutions is strongly decreased due to particle agglomeration. To solve this problem, a sepiolite-stabilized ZVIN (S-ZVIN) composite was made using a liquid-phase method and then characterized employing scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometer (EDS). Batch experiments were also conducted to (1) investigate the influence of various experimental variables on the removal efficiency of Cr(VI), (2) compare the removal efficiency of bare ZVIN and S-ZVIN treatments and (3) evaluate the capability of the artificial neural network (ANN) technique to model the Cr(VI) removal. The Cr(VI) removal efficiency was enhanced by increasing S-ZVIN dosage while a considerable decrease was observed by increasing the initial Cr(VI) concentration. The acidic and neutral pH values were appropriate for Cr(VI) removal. The enhancement was observed in Cr(VI) removal by increasing chloride concentration. Additionally, pseudo first-order showed better performance than pseudo second-order kinetic model to fit the experimental data of Cr(VI) removal. The ANN model could predict the experimental data of Cr(VI) removal with a determination coefficient of 0.9803. The relative significance of each input variable on the removal of Cr(VI) was calculated.

AB - The efficiency of zero-valent iron nanoparticles (ZVINs) for the removal of chromium Cr(VI) from solutions is strongly decreased due to particle agglomeration. To solve this problem, a sepiolite-stabilized ZVIN (S-ZVIN) composite was made using a liquid-phase method and then characterized employing scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometer (EDS). Batch experiments were also conducted to (1) investigate the influence of various experimental variables on the removal efficiency of Cr(VI), (2) compare the removal efficiency of bare ZVIN and S-ZVIN treatments and (3) evaluate the capability of the artificial neural network (ANN) technique to model the Cr(VI) removal. The Cr(VI) removal efficiency was enhanced by increasing S-ZVIN dosage while a considerable decrease was observed by increasing the initial Cr(VI) concentration. The acidic and neutral pH values were appropriate for Cr(VI) removal. The enhancement was observed in Cr(VI) removal by increasing chloride concentration. Additionally, pseudo first-order showed better performance than pseudo second-order kinetic model to fit the experimental data of Cr(VI) removal. The ANN model could predict the experimental data of Cr(VI) removal with a determination coefficient of 0.9803. The relative significance of each input variable on the removal of Cr(VI) was calculated.

KW - ANN modeling

KW - Chromium

KW - Nanocomposite

KW - Sepiolite

KW - Zero-valent iron

UR - http://www.scopus.com/inward/record.url?scp=84947030861&partnerID=8YFLogxK

U2 - 10.1016/j.jtice.2014.11.011

DO - 10.1016/j.jtice.2014.11.011

M3 - Article

AN - SCOPUS:84947030861

SN - 1876-1070

VL - 49

SP - 172

EP - 182

JO - Journal of the Taiwan Institute of Chemical Engineers

JF - Journal of the Taiwan Institute of Chemical Engineers

ER -

Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: Impact of operational parameters and artificial neural network modeling

Abstract

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Keywords

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