Abstract
Artificial neural network and response surface methodology have been used to develop a model for simulation and optimization of the removal of Nile blue sulfate by heterogeneous Fenton oxidation. Experimental data were used to train an artificial neural network model with linear transfer function at the output layer and a tangent sigmoid transfer function at the hidden layer. A Box–Behnken design was employed to assess the effects of input process parameters on the total organic carbon removal. First order kinetics and lumped kinetics models were used to describe the reaction; a high regression coefficient indicated that the latter fitted best. The formation of non-oxidizable compounds was shown by liquid chromatography–mass spectrometry.
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