Current Status and Future Prospects of Membrane Bioreactors (MBRs) and Fouling Phenomena: A Systematic Review
Bibliographic record
Abstract
Abstract Membrane bioreactors (MBRs) have been widely used for municipal and industrial wastewater treatment around the world due to their advantages, which include higher efficiency, smaller footprint, and lower sludge production over other conventional activated sludge (CAS) processes. However, membrane fouling that results from physicochemical interactions between the membrane and the components of the mixed liquor still remains the most challenging matter preventing the broad application of MBR technology. Recently, a considerable number of experimental and modelling investigations have been conducted concerning MBRs and membrane fouling. Despite the development of low‐fouling membrane systems, more research and engineering activities with a focus on surface modification, wastewater specifications, pre‐treatment and treatment conditions, and efficient fouling control and remedy strategies are still needed to minimize the probability of the occurrence of fouling. It is vital to investigate important aspects of the characterization and mechanisms of the fouling phenomenon to find reliable and long‐term solutions. This review provides a detailed survey of the main aspects of the MBR processes, configurations, advantages and disadvantages, fouling phenomenon, and fouling control strategies in MBRs. Past research and engineering activities in this area are critically reviewed such that pros and cons of recent developments in fouling inhibition and mitigation approaches are also discussed. The main practical and theoretical challenges for the effective utilization of MBRs in various municipal and industrial sectors are then addressed. At the end, we offer useful practical guidelines and recommendations for the better design and operation of MBRs in industrial and public communities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".