Biofiltration of concentrated mixtures of hydrogen sulfide and methanol
Bibliographic record
Abstract
Abstract To date, little research has addressed biofiltration of volatile organic compound (VOC) and reduced sulfur compound (RSC) mixtures at the relatively high concentrations (0 to 450 ppmv) of interest to the pulp and paper industry. The objectives of this study were to assess the impact of cotreatment on the biofiltration of air emissions containing mixtures of RSCs and VOCs, and to develop models of the processes. Experiments were conducted at various concentrations (16 s EBRT) using hydrogen sulfide as a model RSC (0–450 ppmv) and methanol as a model VOC (0–400 ppmv). Reaction‐limited and biofilm models showed that hydrogen sulfide degradation followed Monod kinetics, while methanol removal was first order. The maximum hydrogen sulfide removal rate observed during the first three months of operation was 144 g H2S/m3 bed/h. However, this declined to 85–95 g H2S/m3 bed/h in subsequent months. The methanol removal rate was 70–80% of the applied load (maximum 480 g methanol/m3 bed/h) for single component and mixture treatment. The results indicate that methanol and hydrogen sulfide removal in biofilters are independent and that co‐treatment is an attractive option.
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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) | 0.001 | 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".