Simultaneous Heterotrophic Nitrification and Aerobic Denitrification of High C/N Wastewater in a Sequencing Batch Reactor
Bibliographic record
Abstract
Heterotrophic nitrification and aerobic denitrification (HN–AD) is an emerging biological process capable of achieving efficient nitrogen removal in a single reactor. This study investigates the HN–AD performance of a sequencing batch reactor (SBR) operated with a simple anaerobic–aerobic cycle for treating high C/N wastewater. Over a 220-day operation, the system achieved average removal efficiencies of 98.6% for COD, 93.3% for NH4+-N, and 87.1% for total nitrogen. Effluent concentrations of NO2−-N and NO3−-N remained negligible at the end of each aerobic phase. Concentration profiles of NH4+-N, NO2−-N, and NO3−-N throughout the operation cycles confirmed the occurrence of simultaneous nitrification and aerobic denitrification. The consistently high COD removal and robust nitrogen reduction highlight the stability of the HN–AD microbial consortia enriched from activated sludge. Phosphorus removal (average removal efficiency 66.3%) may be enhanced by increasing the activity of phosphate-accumulating organisms (PAOs) through process optimization. This study demonstrated effective HN–AD using activated sludge in SBRs. Future work will focus on evaluating the system with real wastewater and continuous-flow setups to further refine operational parameters for sustained HN–AD performance.
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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 | 0.000 |
| 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 | 0.000 | 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".