Prediction model of DnBP degradation based on BP neural network in AAO system

Ma, Y; Huang, M; Wan, J; Wang, Y; Sun, X; Zhang, H

HERO ID

1332780

Reference Type

Journal Article

Year

2011

Language

English

PMID

21277773

HERO ID 1332780
In Press No
Year 2011
Title Prediction model of DnBP degradation based on BP neural network in AAO system
Authors Ma, Y; Huang, M; Wan, J; Wang, Y; Sun, X; Zhang, H
Journal Bioresource Technology
Volume 102
Issue 6
Page Numbers 4410-4415
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.
Doi 10.1016/j.biortech.2011.01.004
Pmid 21277773
Wosid WOS:000288356300018
Url https://search.proquest.com/docview/858424586?accountid=171501
Is Certified Translation No
Dupe Override No
Is Public Yes
Language Text English
Keyword Aerobiosis; Algorithms; Anaerobiosis; Biodegradation, Environmental; Computer Simulation; Dibutyl Phthalate/isolation & purification; Kinetics; Neural Networks (Computer); 2286E5R2KE
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