Response surface methodology and artificial neural network modeling of reactive red 33 decolorization by O3 /UV in a bubble column reactor
- سال انتشار: 1395
- محل انتشار: فصلنامه پیشرفت ها در فناوری محیط زیست، دوره: 2، شماره: 1
- کد COI اختصاصی: JR_AET-2-1_004
- زبان مقاله: انگلیسی
- تعداد مشاهده: 336
نویسندگان
Department of Chemical Engineering, Faculty of Engineering, Razi University, Kermanshah, Iran
چکیده
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 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 7.99کلیدواژه ها
Artificial neural network ،Bubble column،Ozone/Ultraviolet ،Response surface method ،Reactive red 33مقالات مرتبط جدید
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