Filtration is an important process in drinking water treatment to ensure the adequate removal of particle-bound pathogens (i.e. Giardia and Cryptosporidium). Filtration performance is typically monitored in terms of filtered water turbidity. However, particle counts may provide further insight into treatment efficiency, as they have a greater sensitivity for detecting small changes in filtration operation. To optimize the filtration process at the Elgin Area WTP in terms of post-filtration particle counts, artificial neural network (ANN) models were applied. Process models were successfully developed to predict settled water turbidity and particle counts. Additionally, two inverse process models were developed to predict the optimal coagulant dosage required to attain target particle counts. Upon testing each model, a high correlation was observed between the actual and predicted data sets. The ANNs were then integrated into an optimization application to allow for the transfer of real-time data between the models and the SCADA system.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/24250 |
| Date | 06 April 2010 |
| Creators | Griffiths, Kelly |
| Contributors | Andrews, Robert C. |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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