Efficient nitrogen removal from ammonia rich wastewater using aerobic granular sludge (AGS) reactor: Selection and enrichment of effective microbial community
Bibliographic record
Abstract
Anaerobically digested sludge supernatant, characterized by its high ammonia and low biodegradable chemical oxygen demand (COD) content, has raised concerns when returned to mainstream treatment lines due to potential impacts on effluent quality. Addressing this, an aerobic granular sludge (AGS) reactor adopted nitritation/denitritation with external COD addition was utilized and achieved a considerable nitrogen treatment capacity of 4.2 kg N/m3/d, reaching over 90% removal efficiencies for both ammonia and total inorganic nitrogen. This study applied progressively increased nitrogen loading to select for a microbial community that exhibited high nitrogen oxidation and reduction rates, demonstrating peak rates of 0.5 g N/g VSS/d and 3 g N/g VSS/d, respectively. The enrichment of highly efficient microbial community was achieved along with the increased biomass density peaked at 17 g/L MLVSS, with the system retaining small-sized granular sludge at 0.5 mm. The primary ammonia oxidizing bacteria was Nitrosomonas, while Thauera was the dominated denitrifiers. Quantitative polymerase chain reaction analyses reinforced the enhanced nitrogen removal capacity based on the progressively increased abundance of nitrogen cycling functional genes. The high nitrogen treatment capacity, synergistic attributes of high specific microbial activities and the substantial biomass retention, suggest the AGS's efficacy and capacity in ammonia rich wastewater treatment.
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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) | 0.000 | 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 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".