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Evaluate the Performance of Fenton Process for the Removal of Methylene Blue from Aqueous Solution: Experimental, Neural Network Modeling and Optimization
Author(s) -
Mousavi Seyyed Alireza,
Vasseghian Yasser,
Bahadori Alireza
Publication year - 2018
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.13126
Subject(s) - methylene blue , aqueous solution , mean squared error , artificial neural network , degradation (telecommunications) , process (computing) , chemistry , oxidation process , materials science , nuclear chemistry , chemical engineering , computer science , mathematics , organic chemistry , engineering , machine learning , catalysis , telecommunications , photocatalysis , operating system , statistics
In this article, degradation of Methylene Blue by Fenton's oxidation process was investigated. The effect of, Fe 2+ and H 2 O 2 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 ( R 2 ) 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, 39:e13126, 2020