Modeling the Biofiltration of Dimethyl Sulfide in the Presence of Methanol in Inorganic Biofilters at Steady State
Bibliographic record
Abstract
The presence of methanol (MeOH) improves DMS removal (up to 11-fold) by enhancing biomass growth in inorganic biofilters. Although the overall effect is positive, prolonged growth on methanol also negatively affects DMS degradation as a result of competition with DMS. The objectives of this study were to explore the potential to optimize DMS removal with methanol addition and to develop and experimentally validate a mathematical model describing the biofiltration of DMS in the presence of MeOH. Continuous experiments using three bench-scale biofilters packed with inorganic material were performed to examine the removal of DMS under different MeOH addition rates ranging from 0 to 140 g/m3/h. For a constant DMS loading of 3.5 g/m3/h, a maximum DMS removal rate of 1.8 g/m3/h was achieved at a MeOH addition rate of 20 g/m3/h in the inorganic biofilters. A steady-state model incorporating the competitive and activation effects of MeOH on DMS biodegradation was developed, and the modeled results on DMS and MeOH removal were in close agreement with experimental data. Both the experimental data and model simulation suggest that there is an optimum MeOH addition rate for a given DMS loading. A step-feeding strategy for MeOH addition was proposed and tested by the model to optimize DMS removal. The model-predicted results demonstrate that six-step feeding of MeOH enhances DMS treatment by 46% in the biofilters when compared to conventional feeding (one-step) of MeOH at the same total mass loading.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".