Response surface methodology and artificial neural network modeling of reactive red 33 decolorization by O3/UV in a bubble column reactor

Behin, Jamshid; Farhadian, Negin

doi:10.22104/aet.2016.361

Response surface methodology and artificial neural network modeling of reactive red 33 decolorization by O3/UV in a bubble column reactor

Document Type : Research Paper

Authors

Department of Chemical Engineering, Faculty of Engineering, Razi University, Kermanshah, Iran

10.22104/aet.2016.361

Abstract

In this work, response surface methodology (RSM) and artificial neural network (ANN) were used to predict the decolorization efficiency of Reactive Red 33 (RR 33) by applying the O₃/UV process in a bubble column reactor. The effects of four independent variables including time (20-60 min), superficial gas velocity (0.06-0.18 cm/s), initial concentration of dye (50-150 ppm), and pH (3-11) were investigated using a 3-level 4-factor central composite experimental design. This design was utilized to train a feed-forward multilayered perceptron artificial neural network with a back-propagation algorithm. A comparison between the models’ results and experimental data gave high correlation coefficients and showed that the two models were able to predict Reactive Red 33 removal by employing the O₃/UV process. Considering the results of the yield of dye removal and the response surface-generated model, the optimum conditions for dye removal were found to be a retention time of 59.87 min, a superficial gas velocity of 0.18 cm/s, an initial concentration of 96.33 ppm, and a pH of 7.99.

Keywords

Main Subjects

water and wastewater treatment

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