Journal
JOURNAL OF HAZARDOUS MATERIALS
Volume 168, Issue 2-3, Pages 1274-1279Publisher
ELSEVIER
DOI: 10.1016/j.jhazmat.2009.03.006
Keywords
Denitrifying sulfide removal systems; Artificial neural networks; Modeling; EGSB
Categories
Funding
- National Science Foundation of China [50678049, 50878062]
- Program for New Century Excellent Talents in University [NCET-2005]
Ask authors/readers for more resources
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) < 7 h, influent sulfide concentration markedly affects reactor performance. (C) 2009 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available