Modelling of drinking water treatment and disinfection by-product formation with artificial neural networks
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".