The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP

Mustafa, Yasmen A.; Jaid, Ghydaa M.; Alwared, Abeer I.; Ebrahim, Mothana

The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP

2014

Mustafa, Yasmen A. | Jaid, Ghydaa M. | Alwared, Abeer I. | Ebrahim, Mothana

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H₂O₂/Fe⁺²) process. The reaction is influenced by the input concentration of hydrogen peroxide H₂O₂, amount of the iron catalyst Fe⁺², pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H₂O₂ = 400 mg/L, Fe⁺² = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72 %. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R² = 0.9949). The sensitivity analysis showed that all studied variables (H₂O₂, Fe⁺², pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6 %.

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