4.7 Article

Highlighting the cathodic contribution of an electrooxidation post-treatment study on decolorization of textile wastewater effluent pre-treated with a lab-scale moving bed-membrane bioreactor

Journal

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 28, Issue 20, Pages 25972-25983

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-021-12409-8

Keywords

Decolorization; Moving bed-membrane bioreactor; Cathodic electro-oxidation; TiO2/Graphite; TiO2/Platine; TiO2/TiO2; TiO2/RuO2

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This study compared the color removal efficiencies of four different cathodic electrode materials in the electro-oxidation process of textile wastewater. By optimizing parameters, the best treatment conditions for each electrode material were determined and operational costs were calculated.
This study is carried out to investigate the effect of the cathodic contribution in the performance of electro-oxidation process for decolorization of the textile wastewater effluent pre-treated with a lab-scale moving bed-membrane bioreactor. For this purpose, titanium dioxide (TiO2) was used as anode electrode and four different cathodic electrode materials: Graphite, TiO2, TiO(2-)coated Platine, and TiO(2-)coated ruthenium dioxide (RuO2) (namely RuO2) were tested and compared for their color removal efficiencies. Besides, the optimization parameters that affect color removal in correspondence to the electrode materials, such as applied current, electrolysis time, and pH were studied. In this context, the optimum parameters for each electrode material were selected, and the color removal percentages were found as 92.95%, 91.58%, 91.40%, and 89.17% for TiO2/Graphite, TiO2/Platine, TiO2/TiO2, and TiO2/RuO2, respectively. Finally, the operational cost for each of the tested cathodic electrode materials was calculated in each of the studied optimization parameters making it easier and practical for the selection and evaluation of the electrode materials by the readers. The correlation coefficients (R-2) were 81.2%, 87.1%, 86.7%, and 88.6% respectively as a result of the optimization study using the nonlinear regression modeling.

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