Removal of polycyclic aromatic hydrocarbons (PAHs) from produced water by Ferrate (VI) oxidation
Polycyclic aromatic hydrocarbons (PAHs) are mutagenic and carcinogenic contaminants made up of fused benzene rings. Their presence has been reported in several wastewater streams, including produced water (PW), which is the wastewater obtained during oil and gas extraction from onshore or offshore i...
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| Main Authors: | , , , , , , |
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| Format: | Article |
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MDPI AG
2020
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096363988&doi=10.3390%2fw12113132&partnerID=40&md5=a6075bcff8e0f088c20b515b6ded047f http://eprints.utp.edu.my/29825/ |
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| Summary: | Polycyclic aromatic hydrocarbons (PAHs) are mutagenic and carcinogenic contaminants made up of fused benzene rings. Their presence has been reported in several wastewater streams, including produced water (PW), which is the wastewater obtained during oil and gas extraction from onshore or offshore installations. In this study, ferrate (VI) oxidation was used for the first time for the treatment of 15 PAHs, with the total concentration of 1249.11 µg/L in the produced water sample. The operating parameters viz., ferrate (VI) dosage, pH, and contact time were optimized for maximum removal of PAHs and chemical oxygen demand (COD). Central composite design (CCD) based on response surface methodology (RSM) was used for optimization and modeling to evaluate the optimal values of operating parameters. PAH and COD removal percentages were selected as the dependent variables. The study showed that 89.73 of PAHs and 73.41 of COD were removed from PW at the optimal conditions of independent variables, i.e., ferrate (VI) concentration (19.35 mg/L), pH (7.1), and contact time (68.34 min). The high values of the coefficient of determination (R2) for PAH (96.50) and COD (98.05) removals show the accuracy and the suitability of the models. The results showed that ferrate (VI) oxidation was an efficient treatment method for the successful removal of PAHs and COD from PW. The study also revealed that RSM is an effective tool for the optimization of operating variables, which could significantly help to reduce the time and cost of experimentation. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. |
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