title: Prediction model of DnBP degradation based on BP neural network in AAO system.
abstract: A laboratory-scale anaerobic-anoxic-oxic (AAO) system was established to investigate the fate of DnBP. A removal kinetic model including sorption and biodegradation was formulated, and kinetic parameters were evaluated with batch experiments under anaerobic, anoxic, oxic conditions. However, it is highly complex and is difficult to confirm the kinetic parameters using conventional mathematical modeling. To correlate the experimental data with available models or some modified empirical equations, an artificial neural network model based on multilayered partial recurrent back propagation (BP) algorithm was applied for the biodegradation of DnBP from the water quality characteristic parameters. Compared to the kinetic model, the performance of the network for modeling DnBP is found to be more impressive. The results showed that the biggest relative error of BP network prediction model was 9.95%, while the kinetic model was 14.52%, which illustrates BP model predicting effluent DnBP more accurately than kinetic model forecasting.
authors.affiliation: College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006, China.
authors.forename: Yongwen
authors.initials: Y
authors.lastname: Ma
authors.forename: Mingzhi
authors.initials: M
authors.lastname: Huang
authors.forename: Jinquan
authors.initials: J
authors.lastname: Wan
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiaofei
authors.initials: X
authors.lastname: Sun
authors.forename: Huiping
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000332:Aerobiosis; D000465:Algorithms; D000693:Anaerobiosis; D001673:Biodegradation, Environmental; D003198:Computer Simulation; D003993:Dibutyl Phthalate / Q000302:isolation & purification*; D007700:Kinetics; D016571:Neural Networks, Computer
chemical_list: D003993:Dibutyl Phthalate