Effect of diffusion limitation and substrate inhibition on steady states of a biofilm reactor treating a single pollutant
Bibliographic record
Abstract
The occurrence of multiple steady states in a toluene biodegrading, diffusion-limited biofilm under aerobic conditions was investigated by computer models: one steady-state, and one nonsteady-state. Two stable and one unstable intermediate steady-state were identified in a narrow set of combinations of parameters values. The nonsteady-state model predicts conditions that evolve to a steady state that is within 0.02–1% of the solution of the steady-state model, depending on the number of grid points used, confirming the algorithms are valid. Multiple steady states occur if, (1) a biofilm is exposed to a constant gas-phase pollution concentration, which exceeds or undershoots a certain threshold, (2) in a narrow range of parameter values and (3) provided that the pollutant degradation follows Haldane kinetics. Such a biofilm displays half-saturation (i.e., Michaelis-Menten)-like apparent (“falsified”) kinetics from a concentration range starting at zero up to the occurrence of a second steady state. Multiple steady states and falsified kinetics can negatively affect a biofilter and the experimental determination of kinetic parameters, respectively.Implications: The occurrence of multiple steady states in a VOC treating biofilm, shows the significant impact of degradation kinetics and diffusion limitation on the biofilm behavior. Moreover, the implied possible sudden drop of removal efficiency of a biofilter, based on the occurrence of multiple steady states lead to possible bottle-necks in biofilter application and operation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".