Mathematical modeling and simulation of hexane degradation in fungal and bacterial biofilters: effective diffusivity and partition aspects
Bibliographic record
Abstract
Mathematical modeling in the biofiltration of volatile organic compounds is a valuable tool for performance prediction and in scaling up. Majority of the published models include parameters obtained from fitting experimental data, thus masking their real influence as they are lumped generally. The present work aims to evaluate experimentally some of the most relevant parameters including kinetic constant, partition coefficient in the biofilm, biofilm thickness, superficial area, and effective diffusivity. For the fungal biofilm, all the parameters mentioned above were obtained experimentally; and for the bacterial biofilm, the biofilm thickness and some intrinsic parameters used to obtain the first-order kinetic constant were taken from the literature. These parameters were then incorporated in a mathematical model to describe the steady-state degradation of hexane in bacterial and fungal biofilters operating under continuous mode. Experimentally, the dimensionless partition coefficients (m G ) indicated that hexane was 4 and 35 times more soluble in the bacterial (m G = 9·14) and fungal (m G = 0·88) biofilters, respectively, than in water (m G = 30·4). Comparison of model estimates with experimental concentration profiles of the pollutant along the height of the biofilters proves that the first-order limited by reaction model was appropriate to interpret the experimental results with a small error of ∼1%.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".