Modeling of UV-Induced Photodegradation of Naphthalene in Marine Oily Wastewater by Artificial Neural Networks
作者： Liang Jing , Bing Chen , Baiyu Zhang
DOI: 10.1007/S11270-014-1906-0
关键词: Photodegradation 、 Naphthalene 、 Sensitivity (control systems) 、 Biological system 、 Artificial neural network 、 Polycyclic aromatic hydrocarbon 、 Salinity 、 Environmental chemistry 、 Transfer function 、 Materials science 、 Backpropagation
摘要: 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 rate used as output and simulated function five independent input variables, including fluence rate, salinity, temperature, initial concentration reaction time. configuration ANN optimized three-layer feed-forward Levenberg–Marquardt backpropagation network with log-sigmoid linear transfer functions at hidden (12 neurons) layers, respectively. By considering goodness-of-fit cross validated predictability, trained provide good overall agreement experimental results slope 0.97 correlation determination (R2) 0.943. Sensitivity analysis revealed that temperature were most influential followed time, salinity concentration. findings study showed modeling could effectively behavior photo-induced PAH degradation process.
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