The Use of a Biofilter for Reducing Off-Gas Odour from an Industrial Fermentation Process
Bibliographic record
Abstract
This study evaluated the performance of a lab-scale, closed bed biofilter for the removal of odour in off-gas released from an industrial fermentation facility. This off-gas was emitted from the facility periodically after the sterilization of fermentation medium. The lab scale biofilter was operated for over two months, totalling 30 medium sterilizations. The biofilter was subjected to a shock odour load for each sterilization cycle and to two airflow conditions: the fermentation off-gas and compressed room air, which was cooler and drier than the fermentation off-gas. The biofilter was effective in removing odour under shock loading and variable operating conditions (temperature and relative humidity). An odour reduction rate of 72% was achieved immediately after medium sterilization when odour levels were highest (32 800 OU m(-3)). The filter had an odour removal efficiency of 61% and 67% for 24 h and 50 h after sterilization, respectively.
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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 | 0.000 |
| 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) | 0.000 | 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".