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In relation to this article, we declare that there is no conflict of interest.
Publication history
Received May 19, 2015
Accepted August 28, 2015
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Process modeling and optimization of Rhodamine B dye ozonation in a novel microreactor equipped with high frequency ultrasound wave

CFD Research Center, Chemical Engineering Department, Razi University, Kermanshah, Iran
Korean Journal of Chemical Engineering, March 2016, 33(3), 922-933(12), 10.1007/s11814-015-0188-6
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Abstract

This paper reports the effect of 1.7MHz ultrasound wave on decolorization efficiency of Rhodamine B (RB) solution by ozone in a T-type microreactor. Response surface methodology using central composite design (CCD) was used for analysis and optimization of the reaction conditions. The effective parameters such as solution pH, dye initial concentration, liquid volumetric flow rate, ozone dosage and the length of microreactor on decolorization process were investigated. Rhodamine B removal from solution was determined in presence of and without sonication. The results indicate that for both modes, the decolorization efficiency of RB increased with increase of the ozone dosage as well as the length of employed microreactor. However, with increase of RB initial concentration and liquid flow rate, the decolorization efficiency was decreased. The comparison between the reactors with and without sonication shows that the application of ultrasound wave is effective more than 15% on removal efficiency of RB at various conditions. At optimum conditions, the experimental RB removal yield of 97.3% and 95.8 was obtained for with and without irradiation layouts, respectively. The statistical analyses and the agreement of the experimental results with model predictions showed the reliability of the regression model.

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