A Review of Biofouling and its Control in Membrane Separation Bioreactors
Bibliographic record
Abstract
Membrane separation technology is increasingly becoming an important innovation in biological wastewater treatment. Biofouling of the membrane is a major factor affecting the efficient and economic operation of membrane separation bioreactors (MBRs). This review summarizes the state-of-the-art progress in understanding the mechanisms and factors affecting membrane biofouling and the strategies for biofouling control. Biofouling mechanisms include the adsorption of soluble and suspended extracellular polymers on membrane surfaces and in membrane pores, the clogging of membrane pore structure by fine colloidal particles and cell debris, and the adhesion and deposition of sludge cake on membrane surfaces. Design and operating conditions of membrane modules and materials, hydrodynamic conditions in MBRs, process and environmental conditions of activated sludge systems, and the physicochemical properties of the wastewater are the dominant factors determining membrane biofouling. Current strategies to control biofouling include periodic relaxation, backwashing, chemical cleaning, and possible manipulation of hydrodynamic conditions and sludge properties. Achieving full integration of MBRs in wastewater treatment technology requires further research and development. Fundamental information on the bacteria, colloid, and membrane interaction, developed through multimethod and multiscale approaches, is particularly needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".