Systematic evaluation of biofilm models for engineering practice: components and critical assumptions
Bibliographic record
Abstract
Biofilm models are valuable tools for the design and evaluation of biofilm-based processes despite several uncertainties including the dynamics and rate of biofilm detachment, concentration gradients external to the biofilm surface, and undefined biofilm reactor model calibration protocol. The present investigation serves to (1) systematically evaluate critical biofilm model assumptions and components and (2) conduct a sensitivity analysis with the aim of identifying parameter subsets for biofilm reactor model calibration. AQUASIM was used to describe submerged-completely mixed combined carbon oxidation and nitrification IFAS and MBBR systems, and tertiary nitrification and denitrification MBBRs. The influence of uncertainties in model parameters on relevant model outputs was determined for simulated scenarios by means of a local sensitivity analysis. To obtain reasonable simulation results for partially penetrated biofilms that accumulated a substantial thickness in the modelled biofilm reactor (e.g. 1,000 microm), an appropriate biofilm discretization was applied to properly model soluble substrate concentration gradients and, consistent with the assumed mechanism for describing biofilm biomass distribution, biofilm biomass spatial variability. The MTBL thickness had a significant impact on model results for each of the modelled reactor configurations. Further research is needed to develop a mathematical description (empirical or otherwise) of the MTBL thickness that is relevant to modern biofilm reactors. No simple recommendations for a generally applicable calibration protocol are provided, but sensitivity analysis has been proven to be a powerful tool for the identification of highly sensitive parameter subsets for biofilm (reactor) model calibration.
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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 | 0.053 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".