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Optimization of the Catalytic Ozonation Process Using Copper Oxide Nanoparticles for the Removal of Benzene From Aqueous Solutions Publisher



Mohammadi L1 ; Bazrafshan E1 ; Noroozifar M2 ; Ansari Moghaddama AR1 ; Khazaei Feizabad AR3 ; Mahvi AH4
Authors
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Authors Affiliations
  1. 1. Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
  2. 2. Analytical Research Laboratory, Department of Chemistry, University of Sistan and Baluchestan, Zahedan, Iran
  3. 3. English Department, Zahedan University of Medical Sciences, Zahedan, Iran
  4. 4. Department of Environmental Health, Graduate School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Source: Global Journal of Environmental Science and Management Published:2017


Abstract

The current study aimed to examine the overall feasibility of the use of copper oxide nanoparticles as a catalyst in ozonation process for the removal of benzene from aqueous solutions under experimental conditions. This experimental study was conducted on a laboratory scale reactor in a semibatch mode. The effect of critical operating parameters such factors as pH, concentration of benzene, reaction time and nano-catalyst dose on the removal of benzene was investigated. The samples included with benzene concentrations (10-200 mg/L), pH (3-13), catalyst dose (0.1-0.5 mg), and ozonation time (5- 50 min). Findings indicated that the removal of benzene depended on various utilization parameters. The highest efficiency was achieved at reaction time of 50 min, pH of 12, initial benzene concentration of 10 mg/L and catalyst dose of 0.5 g. Among the studied factors, the maximum and the minimum contributions were made by the dose of nanoparticles (83%) and the reaction time (~73%). The software predicted that use of 0.13 g of the catalyst at pH of 12 and ozonation time of 5 min would lead to a removal efficiency of 68.4%. The catalytic ozonation process was able to remove benzene, and addition of copper oxide nanoparticles as a catalyst together with the ozonation process increased the benzene removal efficiency. The values of R2 = 0.9972, adjusted R2= 0.9946, and predicted R2 =0.9893 indicated that the model was acceptably predicted by the software and fitted the data obtained in the experiments. © 2017 Iran Solid Waste Association.
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