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.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 370 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 11 |
| Published : | 11/01/2005 |
