@article { author = {Behin, Jamshid and Farhadian, Negin}, title = {Response surface methodology and artificial neural network modeling of reactive red 33 decolorization by O3/UV in a bubble column reactor}, journal = {Advances in Environmental Technology}, volume = {2}, number = {1}, pages = {33-44}, year = {2016}, publisher = {Iranian Research Organization for Science and Technology}, issn = {2476-6674}, eissn = {2476-4779}, doi = {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 = {Artificial neural network,Bubble column,Ozone/Ultraviolet,Response surface method,Reactive red 33}, url = {https://aet.irost.ir/article_361.html}, eprint = {https://aet.irost.ir/article_361_6dc57439025d604fe2844c21c3d1f18c.pdf} }