In this work, a regression model obtained from response surface methodology (RSM) was proposed for the electrocoagulation (EC) treatment of textile wastewater. The Reactive Black 5 dye (RB5) was used as a model dye to evaluate the performance of the model design. The effect of initial solution pH, applied current and treatment time on RB5 removal was investigated. The total number of experiments designed by RSM amounted to 27 runs, including three repeated experimental runs at the central point. The accuracy of the model was evaluated by the F-test, coefficient of determination (R2), adjusted R2 and standard deviation. The optimum conditions for RB5 removal were as follows: initial pH of 6.63, current of 0.075 A, electrolyte dose of 0.11 g/L and EC time of 50.3 min. The predicted RB5 removal was 83.3% and the percentage error between experimental and predicted results was only 3–5%. The obtained data confirm that the proposed model can be used for accurate prediction of RB5 removal. The value of the zeta potential increased with treatment time, and the X-ray diffraction pattern shows that iron complexes were found in the sludge.
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Research Article|
December 13 2016
Optimisation of Reactive Black 5 dye removal by electrocoagulation process using response surface methodology
W. T. Mook;
W. T. Mook
1Department of Chemical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
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M. K. Aroua;
1Department of Chemical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
E-mail: mk_aroua@um.edu.my
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M. Szlachta;
M. Szlachta
2Department of Environmental Engineering, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, Wrocław 50-370, Poland
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C. S. Lee
C. S. Lee
1Department of Chemical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
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Water Sci Technol (2017) 75 (4): 952–962.
Article history
Received:
July 27 2016
Accepted:
November 18 2016
Citation
W. T. Mook, M. K. Aroua, M. Szlachta, C. S. Lee; Optimisation of Reactive Black 5 dye removal by electrocoagulation process using response surface methodology. Water Sci Technol 24 February 2017; 75 (4): 952–962. doi: https://doi.org/10.2166/wst.2016.563
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