Nondeterministic Computational Fluid Dynamics Modeling of <i>Escherichia coli</i> Inactivation by Peracetic Acid in Municipal Wastewater Contact Tanks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wastewater disinfection processes are typically designed according to heuristics derived from batch experiments in which the interaction among wastewater quality, reactor hydraulics, and inactivation kinetics is often neglected. In this paper, a computational fluid dynamics (CFD) study was conducted in a nondeterministic (ND) modeling framework to predict the Escherichia coli inactivation by peracetic acid (PAA) in municipal contact tanks fed by secondary settled wastewater effluent. The extent and variability associated with the observed inactivation kinetics were both satisfactorily predicted by the stochastic inactivation model at a 95% confidence level. Moreover, it was found that (a) the process variability induced by reactor hydraulics is negligible when compared to the one caused by inactivation kinetics, (b) the PAA dose required for meeting regulations is dictated equally by the fixed limit of the microbial concentration as well as its probability of occurrence, and (c) neglecting the probability of occurrence during process sizing could lead to an underestimation of the PAA dose required by as much as 100%. Finally, the ND-CFD model was used to generate sizing information in the form of probabilistic disinfection curves relating E. coli inactivation and probability of occurrence with the average PAA dose and PAA residual concentration at the outlet of the contact tank.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it