Aeration effect on the efficiency of swine manure treatment in a trickling filter packed with organic materials
Bibliographic record
Abstract
Effect of aeration rate on the removal of organic matter and nitrogen and on the formation of NH3, N2O and N2 was studied for an extensive biofiltration system packed with an organic media, which was used to treat pig manure. The results show high removal of BOD5 and TSS (99 and > or = 98%), independently of the four aeration rate tested (3.4-34 m3/m2 x h). Aeration rate > or = 4.4 m/h resulted in high ammonia stripping during start-up (> or = 1.0 kg NH3-N/m3 of swine manure treated), while using 3.4 m/h only 0.3 kg NH3-N/m3 were stripped. Complete nitrification was achieved after day 100 of operation, except in the biofilter with the lowest aeration rate. Simultaneous denitrification established in all the biofilters. Applying an aeration rate of 9.4 m/h up to 1.2 kg nitrogen was removed in the form of N2 for each m3 of swine manure treated. Contrary to the expectations, N2 formation and release increased with the aeration rate. This particular behaviour seems to be related to the punctual accumulation of water layers inside the biofilters, caused by the air force flowing in the opposite direction to the water flux. N2O production was quite similar in all biofilters (between 0.25-0.36 kg N2O-N/m3 of swine manure treated).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".