Simultaneous treatment of methane and swine slurry by biofiltration
Bibliographic record
Abstract
Abstract BACKGROUND: The piggery industry is important both worldwide and in Canada, but localized production of large quantities of swine slurry causes severe environmental problems such as aquatic pollution and greenhouse gas emissions. The main objective of this study was to determine whether it is possible to simultaneously treat methane (CH4) and swine slurry using an inorganic biofilter. RESULTS: A novel biofilter was designed to overcome the inhibition of CH4 biodegradation by swine slurry. The CH4 elimination capacity increased with the inlet load and a maximum value of 18.8 ± 1.0 g m−3 h−1 was obtained at an inlet load of 46.7 ± 0.9 g m−3 h−1 and a CH4 concentration of 3.3 g m−3. Four pure strains of fungi were used in an attempt to improve the removal of CH4, but no significant effect was observed. Between 0.35 and 3.4 g m−3, the CH4 concentration had no effect on swine slurry treatment with removal efficiencies of 67 ± 10% for organic carbon and 70 ± 7% for ammonium. The influence of the slurry supply was analyzed and the best results were obtained with a supply method of six doses of 50 mL per day. CONCLUSION: Even though the results were lower than those obtained for the biofiltration of CH4 alone, this study demonstrated the feasibility of treating CH4 and swine slurry with the same biofilter using a novel design. Copyright © 2012 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 | 0.000 |
| 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".