UBC Theses and Dissertations
Modelling of drinking water treatment and disinfection by-product formation with artificial neural networks de Medeiros Paulino, Rafael
The source water for Coquitlam Water Treatment Plant (CWTP) originates from a watershed located in the mountains north of the City of Vancouver (BC, Canada), providing approximately 20% of the water demand for the metropolitan area. Treatment at CWTP consists of ozonation, followed by UV for primary disinfection and chlorination for secondary disinfection. Ozone is used to increase the UV transmittance (UVT) of the water, and to reduce the formation of chlorinated disinfection by-products (DBP) in the distribution system. Ozone addition at the CWTP is currently dosed proportionally to the flow being treated. This approach does not take into consideration the complex interactions that exist between varying raw water characteristics and ozone, and how these impact changes in UVT or DBP formation. Advanced numerical computational techniques, such as artificial neural networks (ANN), are increasingly being used to objectively identify optimal operating setpoints for complex systems. In the present study, two sets of ANN models were developed to optimize ozone addition for effective UV treatment and control of DBP formation. The first, the treatment system operation models, were used to predict pre-chlorination UVT, used as surrogate for the DBP formation potential, based on raw water characteristics and CWTP operational setpoints. The second, the distribution system models were used to predict the formation of total haloacetic acids (HAA), total trihalomethanes (THM) and two HAA fractions (DCAA and TCAA) in the distribution system based on raw and treated water characteristics. The treatment models could accurately predict pre-chlorination UVT. A moderate correlation was also observed between the measured and predicted DBP concentrations using the distribution system models, even though significant scatter was observed. This was likely due to the small available dataset and lack of reliable estimations of retention time and chlorine concentration in the distribution system. Scenario analyses with selected models were performed to investigate possible operational benefits of the implementation of these machine learning algorithms models to control ozone dosing. These suggested that savings could be achieved and high and constant level of pre-chlorination UVT could be maintained if ozone was dosed using these artificial neural network models.
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