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  • 标题:Preliminary screening oxidative degradation methyl orange using ozone/ persulfate
  • 本地全文:下载
  • 作者:Nur Aqilah Razali ; Che Zulzikrami Azner Abidin ; Ong Soon An
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2018
  • 卷号:34
  • 页码:1-9
  • DOI:10.1051/e3sconf/20183402038
  • 出版社:EDP Sciences
  • 摘要:The present study focusing on the performances of advanced oxidation process by using ozonation method towards Methyl Orange based on the efficiency of colour removal and Chemical Oxygen Demand (COD) removal. Factorial design with response surface methodology (RSM) was used to evaluate the interaction between operational conditions, such as pH, initial concentration, contact time and persulfate dosage to obtain the optimum range conditions using a semi-batch reactor. The range of independent variables investigated were pH (3-11), initial concentration (100-500mg/L), contact time (10-50min) and persulfate dosage (20-100mM) while the response variables were colour removal and COD removal of Methyl Orange. The experimental results and statistical analysis showed all the parameters were significant. Thus, from this findings, optimization of operational conditions that had been suggested from the ozone/persulfate RSM analysis were (pH 3, 100 mg/L, 50min, 60mM) that would be produced 99% Colour Removal and 80% COD Removal and help in promoting an efficient ozonation process. The effect list data that showed the most contributed effects to increase the percentages of colour removal were pH and persulfate dosage whereas the contact time and initial concentration had the highest positive effects on the COD removal. Other than that, the interaction between pH, contact time and persulfate dosage were found to be the most influencing interaction. Therefore the least influencing interaction was interaction between persulfate dosage and pH. In this study, the correlation coefficient value R2 for colour removal and COD removal of Methyl Orange were R2= 0.9976 and R2= 0.9924 which suggested a good fit of the first-order regression model with the experimental data.
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