Name
Abstract | ||
|---|---|---|
Sludge recycling system is an important part of wastewater treatment plants. Because of the lack of control model and ensure water quality, the sludge recycle flow rate is controlled by high percentage of the influent to the wastewater treatment plants generally, which result in high energy consumption and decreasing of handling capacity. At present, the artificial intelligence modeling technique is considerable used in non-linear and time-varying system such as wastewater treatment plants. In this paper, to depict activated sludge recycle processes, a fuzzy neural model is constructed, relating to predict the sludge recycle flow rate (QR). Simulation studies show that activated sludge recycle model which based on this network have more strong adaptive ability, network structure is simple, learning velocity rapid, prediction effluent the sludge recycle flow rate effectively according to input, which proved high effectiveness of this method. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICNSC.2011.5874895 | ICNSC |
Keywords | Field | DocType |
fuzzy neural network,sludge recycle,wastewater treatment plants,mathematical model,artificial intelligence,nonlinear system,wastewater treatment,recycling,sludge treatment,predictive models,prediction model | Process engineering,Sludge,Activated sludge,Computer science,Effluent,Sewage sludge treatment,Waste management,Sewage treatment,Artificial neural network,Energy consumption,Volumetric flow rate | Conference |
Volume | Issue | Citations |
null | null | 1 |
PageRank | References | Authors |
0.48 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long Luo | 1 | 2 | 1.84 |
| Fei Luo | 2 | 10 | 6.44 |
| Li You Zhou | 3 | 1 | 0.48 |
| Hongtao Ye | 4 | 10 | 2.40 |
| Yuge Xu | 5 | 2 | 1.85 |