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Kinetics study and neural network modeling of degradation of Naphtol Blue Black by electro-Fenton process: effects of anions, metal ions, and organic compound

机译:电芬顿法降解萘酚蓝黑的动力学研究和神经网络建模:阴离子,金属离子和有机化合物的影响

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In the present work, the degradation of azo dye Naphtol Blue Black (NBB) in aqueous solution by electro-Fenton process was investigated. The results indicated that the degradation of NBB by electro-Fenton process followed the second-order reaction kinetics. The experimental results were also modeled by artificial neural network (ANN) with mean squared error of 10~5. This model was developed in Matlab using a feed forward back propagation network; multi-layered perceptron. The input variables to the feed-forward neural network were as follows: initial Fe~(3+) concentration, initial pH, concentration of Na_2SO_4, temperature, applied current, and initial dye concentration. The degradation efficiency and rate constant were chosen as the experimental responses or output variables. The findings indicated that ANN provided reasonable predictive performance (R~2>0.99). Effects of additives such as anions, metal ions, and organic compound on the efficiency and on the rate constant of NBB degradation were also studied under optimum conditions.
机译:在本工作中,研究了通过电子芬顿法降解水溶液中偶氮染料萘酚蓝黑(NBB)的过程。结果表明,电芬顿法降解NBB遵循二级反应动力学。用人工神经网络(ANN)对实验结果进行建模,均方误差为10〜5。该模型是在Matlab中使用前馈传播网络开发的;多层感知器。前馈神经网络的输入变量如下:初始Fe〜(3+)浓度,初始pH,Na_2SO_4浓度,温度,施加电流和初始染料浓度。选择降解效率和速率常数作为实验响应或输出变量。结果表明,人工神经网络提供了合理的预测性能(R〜2> 0.99)。在最佳条件下,还研究了阴离子,金属离子和有机化合物等添加剂对NBB降解效率和速率常数的影响。

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