Prediction and Optimization of Pentachlorophenol Degradation and Mineralization in Heterogeneous Catalytic Ozonation Using Artificial Neural Network
Tóm tắt
In this study, artificial neural network was used to predict pentachlorophenol (PCP) degradation in aqueous solution by catalytic ozonation process in a laboratory-scale semi-batch reactor. The catalyst used in this process was the alumina (γ-Al2O3). Results indicated that after 60 min optimal condition: 0.5 g/L of (γ-Al2O3), 0.5 L/min the flow rate of ozone, pH 8 and 100 mg/L PCP initial concentration, 96% of target pollutant was degraded in catalytic ozonation process. In artificial neural network evaluation, a comparison between the model data and laboratory results revealed a high degree of correlation that indicated the model was capable of defining the PCP elimination efficiency with high accuracy. Artificial neural network predicted results are very close to the experimental results with correlation coefficient (R2) of 0.989 and mean square error of 0.000421. The sensitivity analysis indicated that all studied variables (pH, dosage of catalyst and initial concentration of PCP) have strong influence on PCP degradation.
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