Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network
In this study, the artificial neural network (ANN) technique was employed to derive an empirical model to predict and optimize landfill leachate treatment. The impacts of H2O2:Fe2+ ratio, Fe2+ concentration, pH and process reaction time were studied closely. The results showed that the highest and l...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/79747/1/AnitaMaslahatiRoudi2018_PredictionandOptimizationoftheFenton.pdf http://eprints.utm.my/id/eprint/79747/ http://dx.doi.org/10.3390/w10050595 |
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| Summary: | In this study, the artificial neural network (ANN) technique was employed to derive an empirical model to predict and optimize landfill leachate treatment. The impacts of H2O2:Fe2+ ratio, Fe2+ concentration, pH and process reaction time were studied closely. The results showed that the highest and lowest predicted chemical oxygen demand (COD) removal efficiency were 78.9% and 9.3%, respectively. The overall prediction error using the developed ANN model was within -0.625%. The derived model was adequate in predicting responses (R2 = 0.9896 and prediction R2 = 0.6954). The initial pH, H2O2:Fe2+ ratio and Fe2+ concentrations had positive effects, whereas coagulation pH had no direct effect on COD removal. Optimized conditions under specified constraints were obtained at pH = 3, Fe2+ concentration = 781.25 mg/L, reaction time = 28.04 min and H2O2:Fe2+ ratio = 2. Under these optimized conditions, 100% COD removal was predicted. To confirm the accuracy of the predicted model and the reliability of the optimum combination, one additional experiment was carried out under optimum conditions. The experimental values were found to agree well with those predicted, with a mean COD removal efficiency of 97.83%. |
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