AWWA WQTC62511

Rapid fluctuations of source water quality can upset routine water treatment plant operations. Several waterborne outbreaks have been caused by the co-occurrence of source water contamination and treatment upsets (Woo and Vicente, 2003; Fox and Lytle, 1996). Understanding source water fluctuations according to watershed activities increases the robustness of WTP operation. The main objectives of this project were to: identify the origins of source water turbidity fluctuations at the inlet of the Montreal water treatment plant (WTP); and, use this information to forecast turbidity peaks 24 hours in advance using an artificial neural network (ANN) methodology. The first step of this project was to become familiar with the phenomenon of interest, turbidity variations. For this purpose, daily turbidity data for a period of 40 months were collected and observed to characterize the major events and define any existing patterns. For the same period, data were collected for 43 variables possibly related to turbidity variations based on a literature review. From this list of variables, those presenting significant seasonal variation were conserved as potential independent variables. The main causes of turbidity variations were identified by superposing graphs of turbidity and potential indicators. This exercise also allowed observing time lags between the parameters. As a complement to the graphical method, a correlation matrix was produced between the indicators and the turbidity values for different time lags. Once it was felt that the main causes of turbidity fluctuations had been identified, artificial neural networks were selected as a modeling tool to forecast them. The methodology employed was elaborated using the approaches proposed by research teams working in the environmental and water resources fields (Baxter et al, 2002; Maier and Dandy, 2000). It includes six main steps: the identification of the needs; the choice of the performance criteria; the development and organization of the database; the construction of neural network models; and, the final model choice. Includes 7 references, tables, figures.