Initial and hourly headloss modelling on a tertiary nitrifying wastewater biofiltration plant
Bibliographic record
Abstract
The headloss prediction capability of a wastewater biofiltration model is evaluated on data from a full-scale tertiary nitrifying biofilter unit located in the Paris conurbation (Achères, France; 6,000,000 population equivalent). The model has been previously calibrated on nutrient conversion and TSS filtration observations. In this paper the mass of extracted biofilm during biofilter backwash and the headloss value at the start of an operation cycle are first calibrated on sludge production estimations and relative pressure measurements over the year 2009. The calibrated model is then used on two one-month periods in 2012 for which hourly headloss measurements were acquired. The observed trends are correctly predicted for 2009 but the model exhibits some heavy daily variation that is not found in measurements. Hourly predictions stay close to observations, although the model error rises slightly when the headloss does not vary much. The global model shows that both nutrient conversion and headloss build-up can be reasonably well predicted at the same time on a full-scale plant.
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.
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 | 0.000 |
| 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 | 0.000 | 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".