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• W2051110134 startingPage "179" @default.
• W2051110134 abstract "The application of advanced oxidation processes (AOPs) to the treatment of an effluent contaminated with hydrocarbon oils was investigated. The AOPs conducted were Fe2 +/H2O2 (Fenton's reagent), Fe2 +/H2O2/UV (Photo-Fenton's reagent) and UV-photolysis. These technologies utilize the very strong oxidizing power of hydroxyl radicals to oxidize organic compounds to harmless end products such as CO2 and H2O. A synthetic wastewater generated by emulsifying diesel oil and water was used. This wastewater might simulate, for example, a waste resulting from a hydrocarbon oil spill, onto which detergent was sprayed. The experiments utilising the Photo-Fenton treatment method with an artificial UV source, coupled with Fenton's reagent, suggest that the hydrocarbon oil is readily degradable, but that the emulsifying agent is much more resistant to degradation. The results showed that the COD (chemical oxygen demand) removal rate was affected by the Photo-Fenton parameters (Fe2+, H2O2 concentrations and the initial pH) of the aqueous solution. In addition, the applicability of the treatment method to a ‘real’ wastewater contaminated with hydrocarbon oil is demonstrated. The ‘real’ wastewater was sourced at a nearby car-wash facility located at a petroleum filling station and the experimental results demonstrate the effectiveness of the treatment method in this case. A statistical analysis of the experimental data using the Statistical Analysis System (SAS) and the response surface methodology (RSM) based on the experimental design was applied to optimize the Photo-Fenton parameters (concentrations of Fe2 +, H2O2 and initial pH) and to maximize the COD removal rate (more than 70%)." @default.
• W2051110134 created "2016-06-24" @default.
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• W2051110134 date "2009-01-05" @default.
• W2051110134 modified "2023-09-30" @default.
• W2051110134 title "Photo-catalytic degradation of an oil-water emulsion using the photo-fenton treatment process: Effects and statistical optimization" @default.
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• W2051110134 doi "https://doi.org/10.1080/10934520802539830" @default.
• W2051110134 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19130376" @default.
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