Fast treatment for refinery wastewater via a new wet nano-catalytic oxidation process: experimental study and ANN model

This study was focused on the elimination of phenol from refinery wastewater via wet catalytic oxidation using novel nano-catalyst ( MnO2/Fe2O3) and H2O2 as oxidant. The new nano-catalyst was prepared with different amounts of MnO2 active metals (0, 2, and 5%) over magnetic Fe2O3. The results proved that the loading of MnO2 over Fe2O3 has a positive impact on the performance of phenol removal. The efficiency of phenolic removal was increased via enhancing MnO2 loading over the nano-catalyst. The characterization of catalyst proves that satisfactory dispersion of MnO2 over Fe2O3 was achieved. The surface area and pore dimensions of catalysts were explained that after the enhancement of the amount of MnO2, the surface area was decreased significantly because of the occupation the pores of the magnetic Fe2O3 via MnO2. Phenol removal efficiency was enhanced via increasing the temperature and time. The optimal performance of phenol removal was 93.94% under the best conditions as follows: In the presence of the 5% MnO2/Fe2O3 catalyst for a reaction time of 120 min and a temperature of 75 degrees C. Several methodologies were used for assessing the effectiveness of phenol removal from wastewater. However, they are based on the uncertainty of the assumptions used to build the model and required a lot of real experimental data to suit the actual response and lower the prediction error. In this study, neural networks as a new correlation model using MATLAB ' s toolbox can be developed with enhanced predictive abilities, allowing for a more accurate estimation of the degree to which phenolic component removal would occur. The results display excellent agreement between the experimental and predicted results, with regression coefficient (R-2) = 99.56, 99.67, and 99.81% and mean square error (MSE) = 4.01*10-2, 6.26*10-2, and 9.13*10-3 for 0% MnO2/ Fe2O3, 2% MnO2/ Fe2O3, and 5% MnO2/ Fe2O3, respectively. As compared to previously models for evaluating phenol elimination, the uniqueness and quality of the current ANN model have maximum accuracy. This interactive model generated a strong basis for the new oxidation process behavior.

Automated summary

This study focused on eliminating phenol from refinery wastewater using a new nano-catalyst and H2O2 as oxidant. The results showed that loading MnO2 over Fe2O3 has a positive impact on phenol removal. Increasing temperature and time enhanced phenol removal efficiency. The optimal performance was achieved at 93.94% removal under specific conditions.