engleski

Treatment of simulated industrial wastewater by photo-Fenton process: Part I. The optimization of process parameters using design of experiments (DOE)

The study reported the application of the photo-Fenton process for the treatment of simulated industrial wastewater upon pretreatment by the dark-Fenton process. The process efficiency which depends on several important process parameters such as initial pH, iron catalyst and oxidant concentration, and the UV irradiation type, was investigated. To consider the combined effects of studied process parameters, three-factor three-level Box-Behnken experimental design combined with response surface modeling was applied. The quadratic models predicting the mineralization of simulated industrial wastewater by the applied photo-Fenton processes were developed. ANOVA was applied to evaluate the significance of models and models’ components. The obtained results indicated that both models can be characterized as highly accurate and predictive for the mineralization of simulated industrial wastewater. Our results show that both photo-Fenton processes can be successfully applied for the treatment of the studied simulated industrial wastewater. The presence of oxalates in simulated industrial wastewater strongly influenced the conditions for the maximal efficiency of applied photo-Fenton processes regarding the type of UV irradiation. The optimal conditions were determined to be: initial pH 3.88, [Fe2+]=5.01 mM (along with [Fe3+]= 8.0 mM) and [H2O2]=30 mM in the case of photo-Fenton process operated with UV-C irradiation ; and pH 1.9, [Fe2+]=8.39 mM (along with [Fe3+]= 8.0 mM) and [H2O2]=30 mM in the case of UV-A light application. The “pseudo”-second order mineralization kinetic of simulated industrial wastewater was determined in both cases with the calculated rate constants at determined optimal conditions: k1obs=12.94×10-2 M-1s-1 and k2obs=9.71×10-2 M-1s-1 for UV-C/Fe/H2O2 and UV-A/Fe/H2O2 processes, respectively.

industrial wastewater; photo-Fenton; ferrioxalate; process optimization; design of experiments; statistical analysis

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