Anaerobic Membrane Bioreactors: Applications and Research Directions
Bibliographic record
Abstract
Membranes provide exceptional suspended solids removal and complete biomass retention that can improve the biological treatment process, but their commercial application to anaerobic treatment has been limited. This review summarizes the state of the art with respect to anaerobic membrane bioreactors (AnMBRs), determines the types of wastewaters for which AnMBRs would be best suited, and identifies the research required to increase implementation. AnMBRs have been tested with synthetic, food processing, industrial, high solids content, and municipal wastewaters at laboratory, pilot, and full scale. Chemical oxygen demand removal ranges from 56% to 99%, while the reported design membrane fluxes range from 10 to 40 L/m2/h. AnMBRs should be immediately applicable to highly concentrated, particulate waste streams like municipal sludges where the membrane can decouple the solids and hydraulic retention times. Opportunity for application to dilute wastewaters also appears strong, while application to highly concentrated soluble wastewaters is likely limited. Greater assessment of vacuum-driven immersed membranes, combining external or immersed membranes with retained biomass reactor designs, control of membrane fouling, and economic feasibility are the key research areas to be addressed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".