title: The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP.
abstract: 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 (R (2) = 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%.
authors.affiliation: Department of Environmental Engineering, University of Baghdad, P.O. Box 47121, Jadria, Baghdad, Iraq, yasmen.mustafa@gmail.com.
authors.forename: Yasmen A
authors.initials: YA
authors.lastname: Mustafa
authors.forename: Ghydaa M
authors.initials: GM
authors.lastname: Jaid
authors.forename: Abeer I
authors.initials: AI
authors.lastname: Alwared
authors.forename: Mothana
authors.initials: M
authors.lastname: Ebrahim
mesh_terms: D000465:Algorithms; D006861:Hydrogen Peroxide / Q000737:chemistry; D006863:Hydrogen-Ion Concentration; D007501:Iron / Q000737:chemistry; D016571:Neural Networks, Computer; D010084:Oxidation-Reduction; D010578:Petroleum / Q000032:analysis*; D011237:Predictive Value of Tests; D013696:Temperature; D062065:Wastewater / Q000737:chemistry*
chemical_list: D006861:Hydrogen Peroxide; D007501:Iron; D010578:Petroleum; D062065:Wastewater