Kasiri M B, Aleboyeh H, Aleboyeh A
Laboratoire de Génie des Procédés Traitement des Effluents, Ecole Nationale Superieure de Chimie de Mulhouse, Université de Haute Alsace, 3 rue Alfred Werner, 68093 Mulhouse, France.
Environ Sci Technol. 2008 Nov 1;42(21):7970-5. doi: 10.1021/es801372q.
In this study, estimation capacities of response surface methodology (RSM) and artificial neural network (ANN) in a heterogeneous photo-Fenton process were investigated. The zeolite Fe-ZSM5 was used as heterogeneous catalyst of the process for degradation of C.I. Acid Red 14 azo dye. The efficiency of the process was studied as a function of four independent variables, concentration of the catalyst, molar ratio of initial concentration of H2O2 to that of the dye (H value), initial concentration of the dye and initial pH of the solution. First, a central composite design (CCD) and response surface methodology were used to evaluate simple and combined effects of these parameters and to optimize process efficiency. Satisfactory prediction second-order regression was derived by RSM. Then, the independent parameters were fed as inputs to an artificial neural network while the output of the network was the degradation efficiency of the process. The multilayer feed-forward networks were trained by the sets of input-output patterns using a backpropagation algorithm. Comparable results were achieved for data fitting by using ANN and RSM. In both methods, the dye mineralization process was mainly influenced by pH and the initial concentration of the dye, whereas the other factors showed lower effects.
在本研究中，研究了响应面法（RSM）和人工神经网络（ANN）在非均相光芬顿过程中的估算能力。采用沸石Fe-ZSM5作为该过程中降解C.I.酸性红14偶氮染料的非均相催化剂。研究了该过程的效率与四个独立变量的函数关系，即催化剂浓度、H2O2初始浓度与染料初始浓度的摩尔比（H值）、染料初始浓度和溶液初始pH值。首先，采用中心复合设计（CCD）和响应面法来评估这些参数的单一和综合效应，并优化过程效率。RSM得出了令人满意的预测二阶回归方程。然后，将这些独立参数作为输入输入到人工神经网络中，而网络的输出则是该过程的降解效率。使用反向传播算法通过输入-输出模式集对多层前馈网络进行训练。使用ANN和RSM进行数据拟合时得到了可比的结果。在这两种方法中，染料矿化过程主要受pH值和染料初始浓度的影响，而其他因素的影响较小。
