Treatment of Volatile Organic Compounds in a Biotrickling Filter under Thermophilic Conditions
Bibliographic record
Abstract
The objectives of this research were to investigate the potential to biologically treat volatile organic compounds emitted by the forest products industry at thermophilic conditions and to examine the microbial community developed at high temperatures. Three biotrickling filters were run in parallel at temperatures ranging from 40 degrees C (mesophilic control) to 70 degrees C. The first phase involved treatment of methanol, for a 3-month run, and the second phase involved a 260-day run on the treatment of alpha-pinene. Methanol removal rates over 100 g m(-3) h(-1) where achieved at temperatures up to 70 degrees C. Alpha-pinene removal was achieved at temperatures up to 60 degrees C with optimal treatment occurring at 55 degrees C at rates up to 60 g m(-3) h(-1). The time for acclimation increased with increasing temperature and was longer for pinene than for methanol. Filter performance was also able to quickly recover from a shutdown period of up to 2 weeks due to the robustness of the microbial communities as determined by DNA fingerprinting analysis. The high-temperature communities treating methanol or pinene were more similar to each other than the mesophilic communities (i.e., 40 degrees C). The mesophilic methanol community had a high degree of functional redundancy, while the mesophilic pinene community was more unique and very distinct from the others. These results show that biofiltration at high temperatures is achievable and opens up a range of possibilities for applying biofiltration to hot gas streams.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".