Evaluate the performance of Fenton process for the removal of Methylene Blue from aqueous solution: experimental, neural network modeling and optimization
Mousavi, SA, Vasseghian, Y & Bahadori, A 2018, 'Evaluate the performance of Fenton process for the removal of Methylene Blue from aqueous solution: experimental, neural network modeling and optimization', Environmental Progress & Sustainable Energy.
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In this article, degradation of Methylene Blue by Fenton's oxidation process was investigated. The effect of, Fe2+ and H2O2 concentrations and reaction time in initial concentration of the dye = 10 mg/L, pH = 3 and lab temperature on the dye removal was studied. Also, Artificial Neural Networks (ANN) was applied to model the dye removal data obtained by Fenton oxidation process. A network consisting of two layers of eight neurons in the hidden layer was considered. Very low root mean squared error (RMSE) of 1.262 and high determination of coefficient (R2) of 0.995 in the network calculation verified validity of the acquired network for further analysis and optimization. © 2018 American Institute of Chemical Engineers Environ Prog, 2018