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Prediction via Neural Networks of the Residual Hydrogen Peroxide used in Photo‐Fenton Processes for Effluent Treatment
Author(s) -
Guimarães O. L. C.,
Aquino H. O. Q.,
Oliveira I. S.,
Villela Filho D. N.,
Izario Filho H. J.,
Siqueira A. F.,
Silva M. B.
Publication year - 2007
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.200700113
Subject(s) - hydrogen peroxide , oxidizing agent , effluent , chemistry , residual , chemical oxygen demand , advanced oxidation process , pulp and paper industry , wastewater , catalysis , environmental engineering , environmental science , organic chemistry , computer science , engineering , algorithm
This communication proposes the use of neural networks in the prediction of residual concentrations of hydrogen peroxide from the treatment of effluents through Advanced Oxidative Processes (AOP's), in particular, the photo‐Fenton process. To verify the efficiency of the oxidative process, the Chemical Oxygen Demand (COD) parameter, the values of which may be modified by the presence of oxidizing agents such as residual hydrogen peroxide, is frequently taken in account. The analysis of the H 2 O 2 interference was performed by spectrophotometry at 450 nm wavelength, via the monitoring of the reaction of ammonia with metavanadate. The results of the hydrogen peroxide residual concentration were modeled via a feedforward neural network, with the correlation coefficients between actual and predicted values above 0.96, indicating good prediction capacity.