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

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 (2060 min), superficial gas velocity (0.060.18 cm/s), initial concentration of dye (50150 ppm), and pH (311) were investigated using a 3level 4factor central composite experimental design. This design was utilized to train a feedforward multilayered perceptron artificial neural network with a backpropagation 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 surfacegenerated 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.