Biofiltration of air polluted with methane at concentration levels similar to swine slurry emissions: Influence of ammonium concentration
Bibliographic record
Abstract
An evaluation of the effect of ammonium on the performance of two up-flow inorganic packed bed biofilters treating methane was conducted. The air flow rate was set to 3.0 L min(-1) for an empty bed residence time of 6.0 min. The biofilter was fed with a methane concentration of 0.30% (v/v). The ammonium concentration in the nutrient solution was increased by small increments (from 0.01 to 0.025 gN-NH(4) (+) L(-1)) for one biofilter and by large increments of 0.05 gN-NH(4) (+) L(-1) in the other biofilter. The total concentration of nitrogen was kept constant at 0.5 gN-NH(4) (+) L(-1) throughout the experiment by balancing ammonium with nitrate. For both biofilters, the methane elimination capacity, carbon dioxide production, nitrogen bed retention and biomass content decreased with the ammonium concentration in the nutrient solution. The biofilter with smaller ammonium increments featured a higher elimination capacity and carbon dioxide production rate, which varied from 4.9 to 14.3 g m(-3) h(-1) and from 11.5 to 30 g m(-3) h(-1), respectively. Denitrification was observed as some values of the nitrate production rate were negative for ammonium concentrations below 0.2 gN-NH(4) (+) L(-1). A Michalelis-Menten-type model fitted the ammonium elimination rate and the nitrate production rate.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".