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Arsenic Removal from Aqueous Solutions using Iron Oxide-modified Zeolite: Experimental and Modeling Investigations | ||
| AUT Journal of Mechanical Engineering | ||
| مقاله 8، دوره 5، شماره 1، خرداد 2021، صفحه 141-152 اصل مقاله (1.26 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2020.17214.5849 | ||
| نویسندگان | ||
| Leila Sanaei1؛ Maryam Tahmasebpour* 2؛ Masoumeh Khatamian3؛ Baharak Divband4 | ||
| 1Faculty of chemical and petroleum Engineering, University of Tabriz | ||
| 2chemical and petroleum engineering, university of Tabriz | ||
| 3faculty of chemistry, University of Tabriz | ||
| 4faculty of chemistry, university of tabriz | ||
| چکیده | ||
| Arsenic in drinking water has been recognized as a serious community health problem because of its highly toxic nature and therefore, its removal is considered as one of the most important areas of wastewater treatment. Iron oxide-modified zeolite nanocomposites with two different amounts of iron oxide nanoparticles (3 & 7 wt%) were synthesized, characterized by X-ray diffraction, scanning electron microscope, energy dispersive X-ray, and Brunauer-Emmett-Teller, and then used in a series of batch adsorption experiments to remove arsenic from aqueous system. The effective parameters on the removal of arsenic including adsorbent dose, arsenic initial concentration, contact time, and percentage of iron oxide nanoparticles, were investigated. Under optimum conditions, percentage of iron oxide nanoparticles 3%, adsorbent dose 0.05 g/l, arsenic initial concentration 400 𝜇g/l, and contact time 90 min, the iron oxide-modified zeolite could remove up to 87% of arsenic from contaminated water. The artificial neural network model was also developed from batch experimental data sets which provided reasonable predictive performance (R2=0.998) of arsenic adsorption. According to the results, iron oxide-modified zeolite appears to be a promising adsorbent for removing arsenic from water. | ||
| کلیدواژهها | ||
| Arsenic؛ Fe3O4–NaA zeolite؛ Water-treatment؛ Artificial neural network | ||
| مراجع | ||
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