Modeling denitrifying sulfide removal process using artificial neural networks.
The denitrifying sulfide removal (DSR) process has complex interactions between autotrophic and heterotrophic denitrifers; thus, constructing a detailed mechanistic model and proper control architecture is difficult. Artificial neural networks (ANNs) are capable of inferring the complex relationships between input and output process variables without a detailed characterization of the mechanisms governing the process. This work presents a novel ANN that accurately predicts the steady-state performance of an expended granular sludge bed (EGSB)-DSR bioreactor for nitrite denitrification and the complete DSR process. The proposed ANN shows that at a threshold hydraulic retention time (HRT)<7h, influent sulfide concentration markedly affects reactor performance.
State Key Laboratory of Urban Water Resource and Environment (HIT), Harbin, China. waj0578@hit.edu.cn
Aijie
A
Wang
Chunshuang
C
Liu
Hongjun
H
Han
Nanqi
N
Ren
Duu-Jong
DJ
Lee
D019149:Bioreactors; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000737:chemistry*; D012722:Sewage; D013440:Sulfides / Q000737:chemistry / Q000302:isolation & purification*
D012722:Sewage; D013440:Sulfides; D009584:Nitrogen