Recent advances in membrane aerated biofilm reactors
Bibliographic record
Abstract
Membrane aerated biofilm bioreactors (MABRs), a relatively new innovation in biological wastewater treatment technology, have received much attention in recent years. In the past two decades, the emphasis has focused on exploring and verifying the advantages of MABRs for wastewater treatment through experimental and modeling studies. In-depth fundamental understanding of MABRs and their design have been achieved. Pilot-scale studies and full-scale applications of MABRs have been reported. MABR technology has been successfully applied for high strength industrial wastewater treatment and refractory pollutant removal, simultaneous removal of chemical oxygen demand (COD) and nitrogen (N) in municipal wastewater treatment, and retrofitting of existing activated sludge plants. The advantages of MABRs include high oxygen transfer efficiency, effective COD/N removal, improved energy efficiency, and the relative ease in scale-up. The importance of biofilm thickness control, potential for new applications, and design of low-cost and high efficient membrane materials and modules call for further studies to advance MABR technology. Recent advances in physico-chemical properties of membranes, factors affecting MABR performance, microbial communities, and modeling in MABRs are systematically reviewed. A number of important challenges and unexplored opportunities remain pointing in the direction of future research and development needs.HighlightsMABR technology has reached to pilot-scale and full-scale applications for wastewater treatment.Significant processes in fundamental understanding of process design and applications of MABR has achieved.Process, microbiological, and membrane factors affecting MABR performance are reviewed and discussed.Biofilm thickness control, new membrane materials and module design, and new applications of MABRs call for further studies.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".