RESUMO
A large amount of Reactive red 198 (RR198) is released yearly into the environment. RR198 is toxic for human and aquatic creatures; therefore, it should be removed from wastewater before releasing into the environment. In this study, the nano ZnO-Nd -photo-catalyst for the first time was synthesized by the combustion method. First, the physical characteristics of the generated nano photocatalyst were evaluated using FESEM, XRD, Bandgap calculation, and FTIR analysis. Then, the ZnO-Nd nano-photocatalyst was suspended into the contaminated water with RR198 dye in a falling-film photocatalytic reactor. The effects of parameters such as the amount of H2O2, catalyst dose, pH, and initial concentration of dye were investigated during the experiments. Finally, the decolorization process with the falling-film photocatalytic reactor was optimized using response surface methodology (RSM). The physical characteristics showed that the average particle size of the synthesized ZnO-Nd was 40 nm. Doping ZnO with Nd reduced the photocatalyst energy bandgap by 14%. The results indicated that the optimum amount of catalyst dose and pH level was 0.1 g/L and 5, respectively. The simultaneous usage of H2O2 and ZnO-Nd with an H2O2/dye ratio of two increased dye removal performance by 90%. The results demonstrated that the developed equations can be applied to predict the performance of the falling-film photoreactor. This study showed that using the nano ZnO-Nd photocatalyst in a falling-film photocatalytic reactor under optimum operating conditions is an appropriate way to remove RR198 from water.
RESUMO
Biofilms are organized communities, adherent to the surface and resistant to adverse environmental and antimicrobial agents. So, its control is very important. Staphylococcus aureus is an opportunistic pathogen with the biofilm-forming ability that causes numerous problems in the medicine and food industry. Therefore, this study aimed to investigate the effect of pH, ethanol and NaCl concentrations after 24 and 48 h incubation times at 37 °C, also modeling the results with artificial neural network (ANN). For this purpose, after both incubation times, the effect of each parameter was studied, separately and also in combination at the levels in which the highest biofilm was formed. All results were modeled using multiple ANN and compared in terms of R-value and MSE. The highest biofilm formation ability was in neutral pH. Adding the ethanol and NaCl stimulated biofilm formation, but the inhibitory effect was observed at high concentrations of ethanol and NaCl and very acidic or highly alkaline pH levels. The more incubation time also led to an increase in biofilm formation. Eventually, the Feed-Forward, Back-Propagation Neural Network model with the Levenberg-Marquardt training algorithm and 4-12-1 topology was chosen (R-value = 0.995 and validation MSE = 0.011467). This ANN had high modeling ability because there was a high correlation between experimental data and modeling data. Therefore, it was concluded that pH, ethanol, NaCl, and time are effective parameters in the biofilm formation and there is a nonlinear relationship between these factors that the ANN is capable of modeling them.
