Water Supply Vol 4 No 5-6 pp 87–94 © IWA Publishing 2005
Neural networks: an efficient approach to predict on-line the optimal coagulant dose
S. Deveughèle* and Z. Do-Quang**
*Suez Environnement - CIRSEE, 38 rue du Président Wilson, 78230 Le Pecq-sur-Seine, France E-mail: stephane.deveughele@suez-env.com; zdravka.doquang@suez-env.com
**1Suez Environnement - CIRSEE, 38 rue du Président Wilson, 78230 Le Pecq-sur-Seine, France E-mail: stephane.deveughele@suez-env.com; zdravka.doquang@suez-env.com
ABSTRACT
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%.
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