The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP |
| |
Authors: | Yasmen A. Mustafa Ghydaa M. Jaid Abeer I. Alwared Mothana Ebrahim |
| |
Affiliation: | 1. Department of Environmental Engineering, University of Baghdad, P.O. Box 47121, Jadria, Baghdad, Iraq 2. Ministry of Environment, Baghdad, Iraq
|
| |
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 %. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|