Predicting total trihalomethane formation in finished water using artificial neural networks
Bibliographic record
Abstract
This paper reports on the application of artificial neural network (ANN) techniques for predicting the concentration of trihalomethanes (THMs) in finished water at the E.L. Smith Water Treatment Plant (WTP) in Edmonton, Alberta, Canada. The formation of THMs in finished water involves many complex chemical reactions and interactions that are difficult to model using conventional methods. The formation of THMs has been found to be correlated to raw and treated water quality characteristics such as colour, pH, and temperature and chemical addition such as chlorine, alum, and powder activated carbon (PAC). Three models were derived using raw water, post clarification water, and a combination of raw and post clarification water parameter inputs. The model that most successfully predicted the concentration of THMs in finished water is the model that uses clarifier effluent parameter inputs. This model can be used at the E.L. Smith WTP for early detection of potentially high THM concentrations in finished water and gives plant operators enough advanced warning to reduce THM precursors. With an adequate understanding of water treatment plant processes and THM formation potential it will be fairly easy for any water treatment facility, which has a few years of historical plant data, to develop its own ANN model for predicting the formation of THM in finished water. Key words: artificial neural networks, water treatment process, water treatment modeling, trihalomethane formation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".