Anaerobic oxidation of methane coupled to denitrification: fundamentals, challenges, and potential
Bibliographic record
Abstract
Anaerobic treatment of dilute wastewater (e.g., municipal sewage) is promising due to energy recovery and lessened operating costs, both of which can significantly improve the sustainability of wastewater management over traditional activated sludge treatment. Discharge of dissolved methane and the lack of nitrogen removal are challenges for widespread adoption of anaerobic wastewater treatment. Anaerobic oxidation of methane coupled to denitrification (AOM-D) can overcome both challenges in a complementary way, thus preserving the benefits of anaerobic treatment of dilute wastewater. This review first presents the principles of AOM-D, focusing on pathways and thermodynamics. Second, energy and economic benefits are assessed for anaerobic treatment of dilute wastewater. Third, the review addresses the technical challenges inherent to implementing AOM-D in suspended-growth systems and outlines the advantages of membrane biofilm reactors (MBfRs) for AOM-D applications. Finally, the review develops a model of AOM-D in an MBfR and uses the model to show the conditions that achieve fast denitrification kinetics simultaneously with a very low concentration of dissolved methane.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".