Predicting Chlorine and Trihalomethanes in a Full-Scale Water Distribution System under Changing Operating Conditions
Bibliographic record
Abstract
Predicting free chlorine residual and Trihalomethanes (THMs) in water distribution systems (DS) is challenging, given the variability and imprecise description of the chlorination conditions prevailing in full-scale systems. In this work, we used the variable reaction rate constant (VRRC) model, which offers the advantage of describing variable applied dosage and rechlorination conditions without the need for model recalibration. The VRRC model successfully predicted chlorine decay and THMs formation in ammonia-containing water at the lab scale. Comparing the goodness of fit results showed a better fit by the VRRC model than the 1st-order and an equally good fit compared to the parallel 1st-order model. However, the independence of the VRRC coefficients upon chlorine dosage made it a better choice for full-scale implementation than the parallel 1st-order model. Chlorine and THMs predictions in the DS were performed in 22 locations from a full-scale DS in southern Quebec (Canada). Chlorine predictions by VRRC were conducted in the spring and fall of 2021 under changing water quality conditions (temperature, DOC, dosage). With a prediction target of 0.1 mg/L absolute error, the VRRC model met this target in 77% of the points in the spring and 73% in the fall. While the predictions were comparable and slightly better than those of the 1st-order model, the main advantage of the VRRC was its applicability under variable dosage and rechlorination conditions (e.g., booster chlorination). THMs predictions in the DS were successfully performed in fall 2021. While 91% of the nodes had less than 5 μg/L of absolute prediction error with the VRRC model, the 1st-order model only met this target in 1 out of 22 points. In addition to its high precision, the VRRC can predict THMs using only the lab scale experiments for model parametrization. This enables small utilities with limited resources to predict the possibility of THMs non-compliances under changing water quality conditions with simple lab-based experiments. Changing climatic conditions can deteriorate drinking water quality, raise regulatory concerns for chlorine and THMs, and threaten public health. Water utilities can use the simple approach proposed in this work to assess the possibility of non-compliance under changing conditions. Moreover, the efficiency of different interventions or mitigation strategies to resolve or avoid non-compliance can be evaluated with this approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".