Dynamic analysis of a biofilter treating autothermal thermophilic aerobic digestion offgas
Bibliographic record
Abstract
There is little information to describe how a biofilter responds biologically and chemically to the dynamic loading of complex mixtures of contaminants that are present in autothermal thermophilic aerobic digestion (ATAD) offgases. This paper presents the results of a full-scale study that is the first stage in a longer project to evaluate the use of biofilters for treatment of ATAD offgases. In this study the composition and flow of offgases from an ATAD system located in McMinnville Oregon were characterized. The loading of reduced sulfur compounds (RSC) and ammonia (NH3) to a full-scale biofilter and the concentrations of these compounds in the treated air were measured on an hourly basis for a 24-h period, which was the feeding frequency for the ATAD system. The leachate from the biofilter was characterized for ammonium (NH4+), sulfate (SO42–), nitrate (NO3–), and nitrite (NO2–) concentrations to allow for mass balances on sulfur and nitrogen and to identify the biodegradation processes that were active in the biofilter. The removal efficiencies in the biofilter were more than 99% for hydrogen sulfide and methyl mercaptan and more than 94% for dimethyl sulfide and dimethyl disulfide. The total elimination rates of RSC and NH3 were 12.4 g-S m–3 d–1 and 6.5 g-N m–3 d–1, respectively. Almost 88% of the total mass of N in the leachate left from the biofilter as NH4+-N, while the remaining 12% was in the form of NO3-N and NO2-N. Key words: biofilter, ATAD, dynamic loading, hydrogen sulfide, methyl mercaptan, dimethyl sulfide, dimethyl disulfide, ammonia.
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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".