The effects of methanol on the biofiltration of dimethyl sulfide in inorganic biofilters
Bibliographic record
Abstract
Air emissions from the pulp and paper industry frequently contain reduced sulfur compounds (RSC), such as dimethyl sulfide (DMS) mixed with volatile organic compounds (VOC) (e.g., methanol, MeOH) and it is desirable to treat either one or both of these groups of compounds. The objective of this study was to assess the effects of VOC (MeOH) on the biofiltration of DMS. Results obtained from continuous experiments using three bench-scale biofilters packed with inorganic material clearly show that MeOH has a positive effect (11-fold increase) on the biofiltration of DMS. Further experiments indicate that MeOH addition enhances biomass concentration and viability (threefold) in the biofilters. However, a suspension of MeOH addition causes a rapid significant increase (twofold) in the removal rate of DMS, suggesting that the presence of MeOH also has a competitive effect on DMS biodegradation. This negative effect was also confirmed in batch experiments. The decrease of biofilter performance with time for a long-term suspension of MeOH addition indicates that MeOH addition is necessary to sustain a high removal rate of DMS in inorganic biofilters. Results on metabolic products of DMS biodegradation demonstrate that DMS is almost completely converted to sulfate in the absence of MeOH, while it is partially oxidized to elemental sulfur in the presence of MeOH. This study suggests that there exists an optimum mix of DMS and MeOH for the treatment of DMS emissions in inorganic biofilters.
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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".