Response of methane oxidation biosystems to controlled ingress of oxygen
Bibliographic record
Abstract
• O 2 /CH 4 ratio of 3 was the lowest to achieve high (99.5%) oxidation efficiency. • Extra oxygen is consumed by heterotrophic activity. • Degradation of the compost-wood chip mix increased the CO 2 produced. • Optimizing O 2 supply enhances biofilter efficiency and scalability. Methane (CH 4 ) emissions from landfills significantly contribute to global warming, requiring effective mitigation strategies. Methane oxidation biosystems (MOB) use methanotrophic bacteria to convert CH 4 into carbon dioxide (CO 2 ), offering a cost-effective and sustainable solution. Optimizing MOB performance depends, among other parameters, on adequate oxygen (O 2 ) supply. This study examines the impact of the O 2 /CH 4 ratio on methane oxidation efficiency using a compost-wood chip mixture as the oxidation medium. Six experimental conditions were tested, maintaining an empty bed residence time (EBRT) of 90 min, except for one case where the EBRT was 8880 min. Results show that a 3:1 ratio leads to the optimal removal efficiency (99.5%). Microbiological analysis and respiration tests indicate that heterotrophic respiration and organic matter degradation consume O 2 , requiring additional oxygen beyond the stoichiometric demand of 2 for methane oxidation. When O 2 availability relied on diffusion, the efficiency dropped by 30%, underscoring the importance of optimizing O 2 delivery mechanisms. These findings highlight the necessity of a precise O 2 /CH 4 ratio control to enhance MOB performance, enabling the reduction of EBRT while maintaining biofilter size or decreasing system size without compromising efficiency.
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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) | 0.000 | 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) | 0.001 | 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".