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The degradation of methyl tert-butyl ether (MTBE) was investigated in the aqueous solution of coated ZnO onto magnetite nanoparticale based on an advanced photocatalytic oxidation process. The photocatalysts were synthesized by coating of ZnO onto magnetite using precipitation method. The sample was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and vibration sample magnetometer (VSM). Besides, specific surface area was also determined by BET method. The four effective factors including pH of the reaction mixture, Fe3O4/ZnO magnetic nanoparticles concentration, initial MTBE concentration and molar ratio of [H2O2]/ [MTBE] were optimized using response surface modeling (RSM). Using the four-factor-three-level Box–Behnken design, 29 runs were designed considering the effective ranges of the influential factors. The optimized values for the operational parameters under the respective constraints were obtained at PH of 7.2, Fe3O4/ZnO concentration of 1.78 g/L, initial MTBE concentration of 89.14 mg/L and [H2O2]/ [MTBE] molar ratio of 2.33. Moreover, kinetics of MTBE degradation was determined under optimum condition. The study about core/shell magnetic nanoparticles (MNPs) recycling were also carried out and after about four times, the percentage of the photocatalytic degradation was about 70%.

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In this study the optimum conditions for preparing the iron-doped TiO2 nanoparticles were investigated. Samples were synthesized by sol–gel impregnation method. Three effective parameters were optimized using Taguchi method, consisted of: (i) atomic ratios of Fe to Ti; (ii) sintering temperature; (iii) sintering time. The characterization of samples was determined using X-ray diffraction, BET- specific surface area, UV- Vis reflectance spectra (DRS) and scanning electron microscope (SEM). The XRD patterns of the samples indicated the existence of anatase crystal phase in structure. UV- Vis reflectance spectra showed an enhancement in light absorbance in the visible region (wavelength > 400 nm) for iron-doped samples. The photocatalytic activity of samples was investigated by the degradation of RO 16 (RO 16) dye under UV irradiation. The results illustrated that the photocatalytic activity of iron-doped TiO2 was more than pure TiO2, because of the smaller crystal size, grater BET surface area and higher light absorption ability.

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In this study, poly ethyleneimine (PEI)/Titania (TiO 2 ) multilayer film on quartz tubes have been successfully fabricated via a layer-by-layer (LbL) self-assembly method. Scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) surface area analysis were carried out for characterization of the layers on quartz tube. The SEM pictures showed that the film surface is smooth and uniform. The BET characterization verified the formation of multilayer thin film. The photocatalytic activity of the PEI/TiO 2 multilayer deposited on the quartz tubes was evaluated in the treatment of raw petroleum refinery wastewater (PRW) under UV light irradiation in three annular photocatalytic reactors. This study examined the impact of initial chemical oxygen demand (COD) concentration, H 2 O 2 concentration, pH and reaction time on the PRW treatment and the results were used to generate both a response surface methodology (RSM) model and an artificial neural network (ANN) model. Maximum COD removal (98 %) was achieved at the optimum conditions (initial COD concentration of 300 mg/l, hydrogen peroxide concentration of 8.8 mM, pH of 5 and reaction time of 120 min). A comparison between the model results and experimental data gave a high correlation coefficient (R ANN 2 = 0.9632, R RSM 2 = 0.943) and showed that two models were able to predict COD removal from PRW by PEI/TiO 2 /UV process. However, ANN model was superior to RSM model with higher value of coefficient of determination (0.9632 ANN [ 0.94 RSM ) and the lower root mean square error (RMSE) (3.377 AAN \ 3.569 RSM ). The average percentage error for ANN and RSM models was 0.18 and 0.73, respectively, indicating the superiority of ANN in capturing the nonlinear behavior of the system. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.