Modeling of UV-Induced Photodegradation of Naphthalene in Marine Oily Wastewater by Artificial Neural Networks
2014
Jing, Liang | Chen, Bing | Zhang, Baiyu
In this study, an artificial neural networks (ANN) model was developed to predict the removal of a polycyclic aromatic hydrocarbon (PAH), namely, naphthalene from marine oily wastewater by using UV irradiation. The removal rate was used as model output and simulated as a function of five independent input variables, including fluence rate, salinity, temperature, initial concentration and reaction time. The configuration of the ANN model was optimized as a three-layer feed-forward Levenberg–Marquardt backpropagation network with log-sigmoid and linear transfer functions at the hidden (12 hidden neurons) and output layers, respectively. By considering goodness-of-fit and cross validated predictability, the ANN model was trained to provide good overall agreement with experimental results with a slope of 0.97 and a correlation of determination (R ²) of 0.943. Sensitivity analysis revealed that fluence rate and temperature were the most influential variables, followed by reaction time, salinity and initial concentration. The findings of this study showed that neural network modeling could effectively predict the behavior of the photo-induced PAH degradation process.
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