Analysis of the effects of temperature, the amount of nutrient solution and the carbon dioxide concentration on methane biofiltration
Bibliographic record
Abstract
Landfi ll gas emissions contribute to the greenhouse effect due to the presence of methane (CH 4 ).CH 4 emissions from old and small landfi lls can be reduced by using biofi ltration.The objective of this study was to optimize parameters that control CH 4 removal in a biofi lter.Temperature is one of the important parameters as well as the amount of nutrient solution (NS) supplied.The effects of the carbon dioxide (CO 2 ) concentration on CH 4 biofi ltration were also studied.Four biofi lters using an inorganic fi lter bed were studied under similar conditions: an inlet CH 4 concentration of 7000 ppmv and an air fl ow rate of 0.25 m 3 /h.A NS was supplied daily.The temperature was varied from 4°C to 43°C.The highest performance was obtained in the range of 31-34°C with an elimination capacity (EC) of 30 g CH 4 /m 3 /h for an inlet load (IL) of 80 g CH 4 /m 3 /h.The effect of the amount of NS supplied to the biofi lter at ambient temperature was also analyzed.The EC was 23 g CH 4 /m 3 /h for both 101 L NS /m 3 V bed /d and 34 L NS /m 3 V bed /d, but it fell to 17 g CH 4 /m 3 /h at 17 L NS /m 3 V bed /d.CO 2 concentrations were varied from 650 to 18,500 ppmv and no effect was noticed on the EC which remained constant at 18 g CH 4 /m 3 /h for an inlet load of 72 g CH 4 /m 3 /h.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".