Methane treatment in biotrickling filters packed with inert materials in presence of a non‐ionic surfactant
Bibliographic record
Abstract
Abstract BACKGROUND: The treatment of methane in bioreactors with an aqueous phase such as biofilters is limited by low methane water solubility. In the case of biotrickling filters (BTF), the continuous trickling water is a barrier to methane transfer. In a previous study, the use of non‐ionic surfactants improved the performance of biofilters treating methane. RESULTS: Three BTFs treating methane were operated for 1 year under fixed operating conditions of methane concentration of 4.8 g m−3 and air flow rate of 0.25 m−3 h−1. Three kinds of packing material were tested and a non‐ionic surfactant (Brij 35) was periodically added to the nutrient solution at a concentration of 0.5% w/w. Methane conversion was a function of the type of packing materials and the presence of Brij 35 in the nutrient solution. When Brij 35 was added, the methane conversion doubled with respect to the BTFs without surfactant. CONCLUSION: The addition of Brij 35 to the nutrient solution increased the performance of the BTF for the three packing materials tested. The non‐ionic surfactant also affected the carbon dioxide production. The BTFs were stable when the packed bed was washed to remove the excess of biomass. 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 | 0.000 | 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".