Full-Scale Test of a Biotrickling Fitler for the Treatment of Exhaust Air from Swine Buildings
Bibliographic record
Abstract
Swine housing facilities can emit substantial amounts of aerial contaminants, such as ammonia, dust, odour and bioaerosols. These emissions can have a significant impact on the environment as well as human and animal health. It is also known that by reducing odour emissions, producers can improve their relationship with their neighbors. An air treatment unit (ATU) using a biotrickling filter was developed by the Research and Development Institute for the Agri-Environment (IRDA) to reduce these air contaminants. The system was first tested at a laboratory-scale to better understand the operating parameters before a pilot-scale unit was built to evaluate the technology under real barn conditions. The objectives of this study were to optimize the design and produce a commercial-scale system to be tested over an extended period of time. Two full-scale ATUs were built using refrigerated shipping containers and were installed at a commercial research barn housing 375 finisher pigs in Deschambault Quebec. The tests were carried out over a full year. Operating conditions as well as the ammonia concentrations were measured every 15 minutes, while odours and dust were measured on a weekly and bi-weekly basis. After the start-up period, the system was able to perform over a wide range of flow rates, temperatures and gas concentrations reflecting actual barn conditions. Results show that the ATUs removed up to 95% of ammonia emissions, but the performance was highly dependant on the air flow rate. Good removal efficiencies were obtained for dust and odours, but results were variable over time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".