Abstract
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. © 2009 Elsevier B.V. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 1274-1279 |
| Journal | Journal of Hazardous Materials |
| Volume | 168 |
| Issue number | 2-3 |
| DOIs | |
| Publication status | Published - 15 Sept 2009 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Artificial neural networks
- Denitrifying sulfide removal systems
- EGSB
- Modeling
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