Full‐scale transition from denitrification to partial denitrification–anammox (PdNA) in deep‐bed filters: Operational strategies for and benefits of PdNA implementation
Bibliographic record
Abstract
Abstract This study shows for the first time more than 2 years of operation of a mainstream anammox application at full‐scale under temperate climate. This implementation of partial denitrification–anammox (PdNA) in deep bed filters at the HRSD York River treatment plant was demonstrated to achieve the benefits of shortcut nitrogen removal without nitrite oxidizing bacteria (NOB) out‐selection. The transition from denitrification to PdNA filters required bleeding ammonium to the filters using an optimized ammonium versus NOx (AvN) control in the upstream aeration tanks and maintaining a nitrate residual in the filter effluent through feedforward/feedback control. The latter actions led to savings of 85% in methanol, 100% in alkalinity, and 35% in capacity enhancement. Up to 6 mg NH 4 + ‐N/L with an average of 2.2 ± 0.98 mg NH 4 + ‐N/L was removed through the anammox pathway, which accounted for about 15% of the overall plant nitrogen removal. Anammox enrichment was confirmed by activity testing and molecular analysis. The large excess of AnAOB capacity present in the filters (5–10 times more than normal operation) resulted in stable and reliable operation through winter conditions and showed potential for further intensification. Practitioner Points For the first time, long‐term mainstream anammox was established full‐scale through PdNA implementation in deep‐bed filters. PdNA implementation required upstream aeration control optimization to provide a blend of ammonium and nitrate to the filters. Efficient anammox enrichment and retention resulted in reliable PdNA performance under different seasonal and influent conditions. PdNA implementation resulted in significant methanol and alkalinity savings and upstream capacity enhancement as ammonia removal depended less on aerobic nitrification. In the event of NOB out‐selection and presence of nitrite, carbon savings in PdNA polishing filters can be enhanced via partial nitritation–anammox.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".