Removal of COD and Color from Textile Wastewater by the Fenton and UV/H2O2 Oxidation Processes and Optimization

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作者
Nilay Tuncer
Gamze Sönmez
机构
[1] Aksaray University,Engineering Faculty, Department of Environmental Engineering, School of Engineering
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Fenton; Ultra-violet; Advanced oxidation processes (AOPs); Textile wastewater; Non-linear regression;
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摘要
Textile industry wastewaters are complex and hazardous because of the variety of dyestuffs and chemicals used in the textile fiber dyeing and finishing processes. Individual wastewater treatment through physical, biological, or chemical methods has its own advantages and disadvantages. Advanced oxidation processes are considered one of the most attractive methods for treating water and wastewater containing toxic and non-biodegradable pollutants. In this study, color and COD removal efficiencies were investigated using advanced oxidation methods Fenton (Fe2+/H2O2) and ultraviolet/hydrogen peroxide (UV/H2O2) processes. The optimum values of operational parameters that affect the Fenton and UV/H2O2 treatment of wastewater were determined, such as iron dosage, hydrogen peroxide dosage, process time, and pH. The optimum conditions for the Fenton process were determined as H2O2 and Fe2+ concentrations of 1 mg L−1, process time of 30 min, and pH < 3.5. Under these conditions, removal efficiencies of 87.9%, 96.4%, and 98.4% were obtained for COD, color, and turbidity, respectively. In the UV/H2O2 process, optimum conditions were determined as 30 min of processing time, 1 mg L−1 H2O2 concentration, and pH = 3.5 value. Under these conditions, removal efficiencies of 86.7% and 96.5% were obtained for COD and color. The obtained experimental results were also modeled with the nonlinear regression method. When the results were evaluated, it was determined that advanced oxidation methods were very effective in removing color and COD from textile industry wastewater. In addition, based on the finding, the model presented can be used to predict the COD and color removal based on the dependent variables such as Fe2+, pH, time, and H202 concentration.
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