Biodosimetry testing of a simplified computational model for the UV disinfection of wastewater
Bibliographic record
Abstract
A simplified computational model of the ultraviolet (UV) disinfection dose delivered to wastewater was developed and the model outputs were compared with pilot-scale biodosimetry data. The model assumed plug flow, an assumption that may apply for some open channel UV reactors designed for wastewater disinfection and in which case may eliminate the need for involved and expensive computational fluid dynamics analyses of flow patterns. The reactor residence times derived from this assumption were combined with the output from a two-dimensional, point-source summation UV irradiance model to calculate UV dose. The output from the irradiance model was adapted to account for the cross-sectional distribution of UV irradiance within the reactor. Two UV reactor configurations were modeled and pilot-tested at two UV transmittance (UVT) values that are typical for wastewater. The simplified model predicted doses that fell within the range of the observed UV doses under some flow and UVT conditions, however it did so inconsistently and over-predicted the doses at the highest tested flow rates. This was concluded to be due to a breakdown of the simplified plug flow assumption due to deviations in the velocity field distribution at higher flows. Therefore, while this modeling approach may provide "back of the envelope" initial estimates of the UV doses supplied to wastewater and may allow a qualitative evaluation of the effect of adjusting UV reactor design parameters (e.g., channel width, lamp spacing) on the resulting UV dose, the precise quantitative prediction of UV dose must continue to rely on more sophisticated models. Key words: ultraviolet disinfection, computational modeling, wastewater treatment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".