Influence of nitrogen on the degradation of toluene in a compost‐based biofilter
Bibliographic record
Abstract
Abstract Two identical laboratory‐scale bioreactors were operated simultaneously, each treating an input air flow rate of 1 m3 h−1. The biofilters consisted of multi‐stage columns, each stage packed with a compost‐based filtering material, which was not previously inoculated. The toluene inlet concentration was fixed at 1.5 g m−3 of air. Apart from the necessary carbon, the elements nitrogen, phosphorus, sulfur, potassium and other micro‐elements are also essential for microbial metabolism. These were distributed throughout the filter bed material by periodic ‘irrigations’ with various test nutrient solutions. The performance of each biofilter was quantified by determining its toluene removal efficiency, and elimination capacity. Nutrient solution nitrogen levels were varied from 0 to 6.0 g dm−3, which led to elimination capacities of up to 50 g m−3 h−1 being obtained for a toluene inlet load of 80 g m−3 h−1. A theoretical analysis also confirmed that the optimum nitrogen solution concentration lays in the range 4.0–6.0 g dm−3. Validation of the irrigation mode was achieved by watering each biofilter stage individually. Vertical stage‐by‐stage stratification of the biofilter performance was not detected, ie each filter bed section removed the same amount of pollutant, the elimination capacity per stage being about 16 g m−3 h−1 per section of column. © 2001 Society of Chemical Industry
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".