Aerobic granular sludge treating anaerobically pretreated brewery wastewater at different loading rates
Bibliographic record
Abstract
Abstract In this study, three different aerobic granular sludge (AGS) reactors fed with anaerobically pre-treated brewery wastewater were studied. The AGS reactors were operated under different conditions including organic loading rates (OLR) between 0.8 and 4.1 kg COD m−3 d−1, C:N:P ratios (100:10:1 and 100:6:1) and food to microorganism ratios (F/M) between 0.8 ± 0.6 and 1.2 ± 0.5 and 0.9 ± 0.3 kg-TCOD kg-VSS−1d−1. Stable granulation was achieved within two weeks and the size of the granules increased according to the OLR applied. The results indicated that low C:N:P and F/M ratios were favorable to achieve stable aerobic granules in the long term. The carbon removal rate was load-independent in the range examined (TCOD removal >80%), whereas TN removals were inversely proportional to the OLRs. Overall, a longer aeration reaction time with a lower OLR was beneficial to granular structure, which exhibited a compact and defined architecture. Performance results within the other conditions studied further indicated that the microbial community and its complex functionality in nutrient removal was efficient at operational parameters of OLR at 0.8 ± 0.2 kg-TCOD m−3d−1 and F/M ratio at 0.5 ± 0.2 kg-TCOD VSS−1d−1. Moreover, the protein to polysaccharide ratio increased as OLR decreased, leading to a stable granular structure.
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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".