Heated Aeration for Nitrite-Oxidizing Bacteria (NOB) Control in Anammox-Integrated Membrane-Aerated Biofilm Reactors (MABR)
Bibliographic record
Abstract
Nutrient removal in conventional wastewater treatment systems is expensive due to the high aeration costs. An alternative method for effective and sustainable nitrogen removal in wastewater treatment is anaerobic ammonium oxidation (Anammox) implemented with other innovative technologies, such as membrane-aerated biofilm reactors (MABRs). A major challenge associated with the Anammox process is effective control of nitrite-oxidizing bacteria (NOB). High temperature operation in wastewater treatment systems can promote Anammox bacterial growth and inhibit NOB activity. This research aims to investigate the feasibility of integrating Anammox processes with a lab-scale MABR and to examine the effects of high temperature aeration supplied to MABR systems on Anammox bacterial growth and NOB suppression. Experimental results indicate that the membrane’s air permeability was a critical parameter for the successful operation of Anammox-integrated MABR systems due to its influence on the system’s dissolved oxygen concentration (0.41 ± 0.39 mg O2/L). The ammonia removal by AOB and Anammox bacteria was determined to be 7.53 mg N/L·d (76.5%) and 2.12 mg N/L·d (23.5%), respectively. High temperature aeration in MABRs with the Anammox process shows a promising potential for improving energy consumption and sustainable nitrogen removal in wastewater treatment systems.
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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 | 0.001 |
| 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 | 0.001 | 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".