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. The use of neural networks in the prediction of residual concentrations of hydrogen peroxide is proposed for the treatment of efflu‐ents via the photo‐Fenton process. The results of the residual concentration are modeled via a feedforward neural net‐work, with the correlation coefficients between actual and predicted values above 0.96, indicating good prediction capacity.