Abstract
Coagulant control plays an important role in water treatment process. Considering the nonlinear and time-delaying property of coagulation, a model of coagulant dosage based on RBF neural network is developed. The Gaussian function is used for hidden note function, whose centers are adjusted by K-means clustering algorithm. The weights of output layer are obtained based on RLS. The model has been trained and checked by practical data of water plant. The results verify the feasibility of the proposed approach. With optimal algorithm, a closed-loop predictive control system can be established to realize the real time control of optimal coagulant dose rate.
| Original language | English |
|---|---|
| Pages | 2608-2611 |
| Number of pages | 4 |
| State | Published - 2004 |
| Event | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004 |
Conference
| Conference | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings |
|---|---|
| Country/Territory | China |
| City | Hangzhou |
| Period | 15/06/04 → 19/06/04 |
Keywords
- Coagulation
- K-means clustering algorithm
- Predictive control
- RBF neural network
- RLS