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首页> 外文期刊>Advances in Environmental Technology >Response surface methodology and artificial neural network modeling of reactive red 33 decolorization by O3/UV in a bubble column reactor
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Response surface methodology and artificial neural network modeling of reactive red 33 decolorization by O3/UV in a bubble column reactor

机译：鼓泡塔反应器中O3 / UV对反应性红33脱色的响应面方法和人工神经网络建模

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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.
机译：在这项工作中，通过在鼓泡塔反应器中应用O3 / UV工艺，使用了响应面方法（RSM）和人工神经网络（ANN）来预测活性红33（RR 33）的脱色效率。使用3考察了四个独立变量的影响，包括时间（20-60分钟），表观气体速度（0.06-0.18 cm / s），染料的初始浓度（50-150 ppm）和pH值（3-11）。级四因子中央复合材料实验设计。该设计用于通过反向传播算法训练前馈多层感知器人工神经网络。将模型的结果与实验数据进行比较，可以得出较高的相关系数，并且表明这两个模型能够通过使用O3 / UV工艺来预测活性红33的去除。考虑到染料去除率的结果和响应表面生成的模型，发现染料去除的最佳条件是保留时间为59.87分钟，表观气体速度为0.18 cm / s，初始浓度为96.33 ppm ，pH为7.99。

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