A framework for the use of artificial neural networks for water treatment: development and application
Bibliographic record
Abstract
Abstract Artificial neural networks (ANNs) are increasingly being used in water treatment applications because of their ability to model complex systems. The present study proposed a framework to develop and validate ANNs for drinking water treatment and distribution system water quality applications. The framework was used to develop ANNs to identify the optimal ozone dose required for effective UV disinfection and to meet regulatory requirements for disinfection by-products (DBPs) in the distribution system. Treatment at a full-scale treatment plant was successfully modelled, with treated water UV transmittance as the output variable. ANNs could be used to identify operating setpoints that minimize operating costs for effective disinfection during drinking water treatment. However, because of the limited data available to train and validate the distribution system ANNs (i.e. n = 48; 15 years of quarterly measurements), these could not be used to reliably identify operating setpoints that also ensure compliance with DBP regulations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".