Biofiltration of methane: effect of temperature and nutrient solution
Bibliographic record
Abstract
Methane is a greenhouse gas (GHG) 21 times more contributing to global warming than carbon dioxide (CO 2 ) and originates mainly from the energy, agriculture and landfill sectors.Methane can be valorized via combustion or transformed by catalytic processes into products like methanol.When valorization cannot be applied because of inappropriate flow rates or methane concentrations, biofiltration is a biotechnology well adapted to control the methane emissions.Biofiltration is a triphasic biotechnology, which uses microorganisms to reduce pollutants like volatile organic compounds (VOCs) or volatile inorganic compounds (VICs) or GHG like methane.Several studies have been published over the last three decades about VOC and VIC biofiltration, but fewer studies are available about methane control.At the Université de Sherbrooke, research is being conducted to control methane emissions originating from landfills or livestock productions.The biofilter used in this study is a laboratory-scale bioreactor of 0.018 m 3 divided into 3 sections.An inorganic packing material is used as the filter bed and a nutrient solution is supplied to irrigate the biofilter once daily.The objective of the present study is to determine the operating conditions to obtain high removal efficiencies at methane inlet concentrations around 7000 ppmv.The biofilter is operated under a nitrogen concentration of 0.5 gN/L and an inlet flow rate of air/methane mixture of 0.25 m 3 /h.The parameters tested are the temperature of the bed filter and the amount of nutrient solution supplied to the biofilter.Better performances are obtained in the temperature range of 28-30°C with an elimination capacity of 39 gCH 4 /m 3 /h for an inlet load of 67 gCH 4 /m 3 /h.Reducing the daily amount of nutrient solution from 1500 to www.witpress.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".