Effect of Methanol on pH and Stability of Inorganic Biofilters Treating Dimethyl Sulfide
Bibliographic record
Abstract
The biofiltration of dimethyl sulfide (DMS) and other reduced sulfur compounds (RSC) results in acidification of biofilters due to the accumulation of the sulfuric acid in packing material. This may lead to a decrease in biofilter performance due to a drop in pH. Results obtained from continuous experiments using three bench-scale biofilters packed with inorganic material mixed with limestone show that methanol (MeOH) alleviates the pH drop and enhances the stability of biofilter performance and DMS removal. The pH drop in the biofilters treating DMS with MeOH is 4 fold slower than that in the control biofiler treating DMS only. For the biofilters with MeOH addition, the pH of the biofilters drops more gradually (0.044 pH units per day) when compared to the MeOH suspension periods when MeOH is not added (0.23 pH units per day). MeOH addition consumes oxygen and results in a lower conversion ratio of sulfide to sulfuric acid due to the formation of elemental sulfur, reducing acidification in the biofilters. Nitrification was found to be actively taking place in the control biofilter treating DMS without MeOH addition, contributing to the significant pH drop in the reactor. It is proposed that MeOH prevents acid production from nitrification likely by limiting oxygen and nutrients to nitrifying bacteria in the MeOH-fed biofilters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".