论文信息 - Neural networks: an efficient approach to predict on-line the optimal coagulant dose

Neural networks: an efficient approach to predict on-line the optimal coagulant dose

The problem under study was the on-line prediction of the optimal coagulant dose from raw water parameters; it has been tackled by using powerful modeling tools: Artificial Neural Networks (ANNs). Such tools do not rely on physico-chemical relationships; the model is built by using an historical dataset available on the plant (raw water parameters and Jar-tests data). A prototype has been implemented on a full-scale water treatment plant in France. The approach is explained, some relevant results are shown and the industrial benefits are discussed. The expected OPEX reduction (coagulant) is about 10%.

S. Deveughèle | Z. Do-Quang

[1] Thierry Denoeux,et al. Neural networks for process control and optimization: two industrial applications. , 2003, ISA transactions.

[2] Thierry Denoeux,et al. A Neuro-Fuzzy model for missing data reconstruction , 1998 .

[3] Thierry Denoeux,et al. A neural network-based software sensor for coagulation control in a water treatment plant , 2001, Intell. Data Anal..

[4] Thierry Denoeux,et al. An hybrid neural network based system for optimization of coagulant dosing in a water treatment plant , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).