Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks
Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks
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DOI：
10.1016/j.dyepig.2007.05.014
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发表时间：
2008-01-01
影响因子：
4.5
通讯作者：
Aleboyeh, H.
中科院分区：
文献类型：
--
作者：
Aleboyeh, A.;Kasiri, M. B.;Aleboyeh, H.
建立了人工神经网络模型，对紫外光与双氧水联合作用下C.I.酸橙7溶液的光化学脱色效果进行了预测。将染料和过氧化氢的初始浓度、溶液的pH值和紫外照射时间作为网络输入；该网络的输出为脱色效率。本研究使用的数据来自我们以前的论文。采用反向传播算法对114组输入输出模式的多层前馈网络进行训练；隐藏层中有8个神经元的三层网络给出了最佳结果。模型预测效果良好，相关系数高（R-2 = 0.996）。正如预期的那样，H2O2初始浓度是对脱色过程影响最大的参数，相对重要性为48.89%。(c) 2007年Elsevier Ltd.出版
An artificial neural network model was developed to predict the photochemical decolorization of C.I. Acid Orange 7 solution by a combination of UV and hydrogen peroxide. The initial concentrations of dye and hydrogen peroxide, the pH of the solution and time of UV irradiation were employed as input to the network; the output of the network was decolorization efficiency. The data used in this study were obtained from our previous papers. The multilayer feed-forward networks were trained by 114 sets of input-output patterns using a backpropagation algorithm; a three-layered network with eight neurons in the hidden layer gave optimal results. The model gave good predictions of high correlation coefficient (R-2 = 0.996). As expected, the initial concentration of H2O2 with a relative importance of 48.89%, appeared to be the most influential parameter in the decolorization process. (c) 2007 Published by Elsevier Ltd.