An artificial neural network (ANN) was implemented for modeling phenol mineralization in aqueous solution using the photo-Fenton process. The experiments were conducted in a photochemical multi-lamp reactor equipped with twelve fluorescent black light lamps (40 W each) irradiating UV light. A three-layer neural network was optimized in order to model the behavior of the process. The concentrations of ferrous ions and hydrogen peroxide, and the reaction time were introduced as inputs of the network and the efficiency of phenol mineralization was expressed in terms of dissolved organic carbon (DOC) as an output. Both concentrations of Fe(2+) and H2O2 were shown to be significant parameters on the phenol mineralization process. The ANN model provided the best result through the application of six neurons in the hidden layer, resulting in a high determination coefficient. The ANN model was shown to be efficient in the simulation of phenol mineralization through the photo-Fenton process using a multi-lamp reactor.
CuO/ZnO coupled oxide films were electrodeposited onto an aluminum substrate and tested as photocatalysts in degradation of phenol molecules in aqueous solution under sunlight. The obtained films were characterized by X-ray diffraction, scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). The results showed that the photocatalytic activity of films was significant, especially to coupled oxide film with a CuO/ZnO ratio equal to 0.697, which presented about 70% degradation of the aromatic molecules and 42% of total organic carbon (TOC) removal at 300 min under solar irradiation. Therefore, this work highlights the potential application of CuO/ZnO coupled oxide films obtained by electrodeposition onto aluminum substrate in the field of photocatalysis.
The photo-Fenton process was applied to degrade non-ionic surfactants with different numbers of ethoxy groups, seven (E7), ten (E10) and twenty-three (E23). The effects of H2O2 concentration, Fe(II) concentration and number of ethoxy groups on the mineralization of surfactants were investigated. The response surface methodology (RSM) was applied to determine optimal concentrations of Fenton's reagents for each surfactant. The efficiency of the photo-Fenton process reached 95% for all surfactants studied at 45 min in optimal conditions determined in this work. The analysis of results showed that the efficiency depends upon the number of ethoxy groups in the surfactant. The increase in ethoxy groups favoured the mineralization of surfactants. The analysis of variance (ANOVA) was applied, and according to the F-test the models for the mineralization of surfactants were considered significant and predictable. The photo-Fenton process has proven to be feasible for the degradation of ethoxylated surfactants in aqueous solution.
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