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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Research Article| December 01 2004
Neural networks: an efficient approach to predict on-line the optimal coagulant dose
Water Supply (2004) 4 (5-6): 87–94.
S. Deveughèle, Z. Do-Quang; Neural networks: an efficient approach to predict on-line the optimal coagulant dose. Water Supply 1 December 2004; 4 (5-6): 87–94. doi: https://doi.org/10.2166/ws.2004.0096
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