Prediction via Neural Networks of the Residual Hydrogen Peroxide used in Photo‐Fenton Processes for Effluent Treatment

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 H₂O₂ 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.