Abstract:
In this work, the photochemical treatment of a real municipal wastewater using a persulfate driven photo-Fenton-like process was studied. The wastewater treatment efficiency was evaluated in
terms of total carbon (TC), total organic carbon (TOC) and total nitrogen (TN) removal. Response
surface methodology (RSM) in conjunction Box-Behnken design (BBD) and multilayer artificial neural
network (ANN) have been utilized for the optimization of the treatment process. The effects of four
independent factors such as reaction time, pH, K2S2O8 concentration and K2S2O8/Fe2+ molar ratio
on the TC, TOC and TN removal have been investigated. The process significant factors have been
determined implementing Analysis of Variance (ANOVA). Both RSM and ANN accurately found
the optimum conditions for the maximum removal of TOC (100% and 98.7%, theoretically), which
resulted in complete mineralization of TOC at the reaction time of 106.06 min, pH of 7.7, persulfate
concentration of 30 mM and K2S2O8/Fe2+ molar ratio of 7.5 for RSM and at the reaction time of
104.93 min, pH of 7.7, persulfate concentration of 30 mM and K2S2O8/Fe2+ molar ratio of 9.57 for
ANN. On the contrary, the attempts to find the optimal conditions for the maximum TC and TN
removal using statistical, and neural network models were not successful.