Influence of residual anaerobic granular sludge (AnGS) from anaerobically digested molasses wastewater in aerobic granular sludge reactor
Bibliographic record
Abstract
This study investigated the impact of residual anaerobic granular sludge (AnGS) from anaerobic digesters treating molasses wastewater on ammonium reduction in a downstream aerobic granular sludge (AGS) reactor. Two conditions were tested: raw (high AnGS concentration) and settled (low AnGS concentration) anaerobically digested molasses wastewaters were fed into the AGS reactor. With the introduction of raw wastewater, enhanced nitrite accumulation at 30 % and improved total inorganic nitrogen (TIN) removal at 11 % were observed compared to 1 % nitrite accumulation and 8 % TIN removal with the introduction of settled wastewater. However, AnGS adversely affected other aspects of reactor performance, increasing effluent solid content and decreasing soluble chemical oxygen demand removal efficiency from 20 % in the low AnGS condition to 11 % in the high AnGS condition. Despite the observed retention of AnGS in the reactor, no significant bioaugmentation effects on the microbial community of the AGS were observed. Aerobic granular sludge was consistently observed in both conditions. The study suggests that AnGS may act as a nucleus for granule formation, helping to maintain granule stability in a disturbed environment. This study offers a systematic understanding of the impact of AnGS on subsequent nitrogen removal process using AGS, aiding in the decision making in the treatment of high solid anaerobic digestate.
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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".