Anaerobic membrane bioreactors for wastewater treatment: Challenges and opportunities
Bibliographic record
Abstract
Anaerobic membrane bioreactors (AnMBRs) have become a new mature technology and entered into the wastewater market, but there are several challenges to be addressed for wide applications. In this review, we discuss challenges and potentials of AnMBRs focusing on wastewater treatment. Nitrogen and dissolved methane control, membrane fouling and its control, and membrane associated cost including energy consumption are main bottlenecks to facilitating AnMBR application in wastewater treatment. Accumulation of dissolved methane in AnMBR permeate decreases the benefit of methane energy and contributes to methane gas emissions to atmosphere. Separate control units for nitrogen and dissolved methane add system complexity and increase capital and operating and maintenance (O & M) costs in AnMBR-centered wastewater treatment. Alternatively, methane-based denitrification can be an ideal nitrogen control process due to simultaneous removal of nitrogen and dissolved methane. Membrane fouling and energy associated with membrane fouling control are major limitations, in addition to membrane cost. More efforts are required to decrease capital and O & M costs associated with the control of dissolved methane nitrogen and membrane fouling to facilitate AnMBRs for wastewater treatment. PRACTITIONER POINTS: AnMBRs can accelerate anaerobic wastewater treatment including dilute wastewater. Nitrogen and dissolved methane control is detrimental for AnMBR application to wastewater treatment. Membrane biofilm reactors using gas-permeable membranes are suitable for simultaneous nitrogen and dissolved methane control. High capital and O & M costs from membranes are a major bottleneck to wide application of AnMBRs. Dynamic membranes could be an option to reduce capital and O & M costs for AnMBRs.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".