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 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 back-propagation algorithm. a comparison between the models results and experimental data gave the high correlation coefficients and showed that two models were able to predict reactive red 33 removal by o3/uv. considering the yield of dye removal and from response surface-generated model, the optimum conditions for dye removal are retention time of 59.87 min, superficial gas velocity of 0.18 cm/s, initial concentration of 96.33 ppm and ph of 7.99.