%0 Journal Article %T Reduction and prediction of N2O emission from an Anoxic/Oxic wastewater treatment plant upon DO control and model simulation %+ Shenzhen Univerisity [Shenzhen] %+ Institut des Sciences Chimiques de Rennes (ISCR) %A Sun, S. %A Bao, Z. %A Li, R. %A Sun, D. %A Geng, H. %A Huang, X. %A Lin, J. %A Zhang, P. %A Ma, R. %A Fang, L. %A Zhang, X.-H. %A Zhao, X. %Z 2017M612738, China Postdoctoral Science Foundation %Z 51708357, NSFC, National Natural Science Foundation of China %< avec comité de lecture %@ 0960-8524 %J Bioresource Technology %I Elsevier %V 244 %P 800--809 %8 2017 %D 2017 %R 10.1016/j.biortech.2017.08.054 %M 28830043 %K BP-ANN model %K DO %K N2O emission %K Simulation %K A/O process %Z Chemical SciencesJournal articles %X In order to make a better understanding of the characteristics of N2O emission in A/O wastewater treatment plant, full-scale and pilot-scale experiments were carried out and a back propagation artificial neural network model based on the experimental data was constructed to make a precise prediction of N2O emission. Results showed that, N2O flux from different units followed a descending order: aerated grit tank > oxic zone ≫ anoxic zone > final clarifier > primary clarifier, but 99.4% of the total emission of N2O (1.60% of N-load) was monitored from the oxic zone due to its big surface area. A proper DO control could reduce N2O emission down to 0.21% of N-load in A/O process, and a two-hidden-layers back propagation model with an optimized structure of 4:3:9:1 could achieve a good simulation of N2O emission, which provided a new method for the prediction of N2O emission during wastewater treatment. © 2017 %G English %L hal-01581229 %U https://univ-rennes.hal.science/hal-01581229 %~ UNIV-RENNES1 %~ CNRS %~ INSA-RENNES %~ ENSC-RENNES %~ ISCR %~ SCR_VC %~ STATS-UR1 %~ UR1-SPM %~ INC-CNRS %~ UR1-UFR-SPM %~ UR1-HAL %~ UR1-SDLM %~ IVER %~ TEST-UNIV-RENNES %~ TEST-UR-CSS %~ UNIV-RENNES %~ INSA-GROUPE %~ TEST-HALCNRS %~ UR1-MMS %~ TEST2-HALCNRS