Establishing Stable Nitritation in MABR through Aeration Control
Bibliographic record
Abstract
Nitrogen removal from municipal wastewater through partial nitritation-denitrification (nitritation) is challenging to accomplish in a membrane-aerated biofilm reactor (MABR) due to the reactor configuration, which potentially interferes with nitrite-oxidizing bacteria inhibition. This study investigated the impact of intermittent aeration on the development and sustenance of nitritation in a lab-scale MABR for the treatment of municipal wastewater. The study was accomplished in four phases (Phases I–IV) using a combination of continuous and intermittent aeration modes with aerated and nonaerated cycles of 10 min (5 on/5 off), 20 min (10 on/10 off), and 25 min (10 on/15 off), respectively, and a constant hydraulic retention time of 2.5 h. Biofilm development and stabilization were completed using a continuous aeration condition (Phase I). Nitrite accumulation rate, nitrate production rate, and ammonium nitrogen removal efficiency achieved in Phases II–IV were, 35%, 12%, and 99%; 76%, 3.4%, and 98%; and 94%, 1%, and 98%, respectively. Intermittent aeration significantly improved total inorganic nitrogen removal efficiency by ∼20%. Between the initiation of intermittent aeration and termination of the study, ammonia-oxidizing bacteria activities within the reactor increased by >150% from 4.53 to 12.6 mgN/h·g volatile suspended solids (VSS). In contrast, nitrite-oxidizing bacteria activities declined by >60% from 1.17 to 0.46 mgN/h·gVSS. The consistent lagging of nitrate production rate behind nitrite accumulation rate, increase in ammonia-oxidizing bacteria activities, and decline in nitrite-oxidizing bacteria activities over the operation period indicates the establishment of nitritation. This study demonstrates that using intermittent aeration, nitritation can be developed and sustained in MABR under mainstream conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".