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

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.