Treatment of methanol vapours in biofilters packed with inert materials
Bibliographic record
Abstract
Abstract BACKGROUND: Methanol is a major pollutant emitted in Canada. Methanol is toxic to humans and it is associated with environmental problems such as smog generation. Biofiltration is a treatment method of considerable interest for controlling methanol emissions, because of its characteristics: no production of hazardous wastes, low energy consumption and low operating costs. The present study analyzed the effects of porous and non‐porous packing materials, the nitrogen concentration in nutrient solution and the methanol inlet load on biofilter performance and biofilm characteristics. RESULTS: The biofilter packed with porous material presented a removal efficiency up to 95%, which was higher than the 35% removal efficiency with the non‐porous material. Inlet load (IL) influenced the biomass and carbon dioxide production rates. The critical inlet load (ILcrit) occurred at 80 g m−3 h−1. The cellular densities of methylotrophs and non‐methylotrophs were affected by all operating variables examined. CONCLUSION: Biofiltration can be applied for controlling methanol emissions with high removal efficiency. The cellular density of methylotrophs is correlated with the performance of the biofilter. Copyright © 2008 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".