Modeling and optimization of photocatalytic/photoassisted-electro-Fenton like degradation of phenol using a neural network coupled with genetic algorithm

Oxidation of phenol in aqueous media using supported TiO2 nanoparticles coupled with photoelectro-Fenton-like process using Mn2+ cations as catalyst of electro-Fenton reaction was studied. The influence of the basic operational parameters such as initial pH of the solution, applied current, initial Mn2+ concentration, initial phenol concentration and kind of ultraviolet (UV) light on the oxidizing efficiency of phenol was studied. An artificial neural network (ANN) model was coupled with genetic algorithm to predict phenol oxidizing efficiency and to find an optimal condition for maximum phenol removal. The findings indicated that ANN provided reasonable predictive performance (R2 = 0.949).

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