Toward an operational dynamic model for tertiary nitrification by submerged biofiltration
Bibliographic record
Abstract
This work deals with the methodology put in place to fit and validate the parameters of a biofiltration model (BAF) in tertiary nitrification treatment and dynamic conditions. For an average loading rate of 0.65 kg NH4-N/m(3) media/d, different time loading rates are applied inside a filtration-backwash run using a semi-industrial pilot. Comparisons between predicted and observed values on the NH4-N, NO3-N and TSS in treated water and the total head loss deltaP are carried out firstly using default values of BAF parameters. Model predictions overestimate values measured but trends are well reproduced. A sensitivity analysis is carried out and the hierarchy of BAF parameters has been set up classifying them into strong and low influence on the effluent concentrations. Among parameters revealing the strongest influence are those of the filtration module and the mean density of biofilm for the TSS effluent and the total AP, the specific autotrophic growth rate, the maximum biofilm thickness and the reduction coefficient of diffusivity in the biofilm for the NH4-N, NO3-N effluent. Finally, this classification leads to setting a calibration procedure, thanks to specific experimental tests directly measuring some BAF parameters.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".