Interrelated Effects of Aeration and Mixed Liquor Fractions on Membrane Fouling for Submerged Membrane Bioreactor Processes in Wastewater Treatment
Bibliographic record
Abstract
The interactions of mixed liquor fractions and their impacts on membrane fouling were examined at different sparging aeration intensities for submerged hollow-fiber membrane bioreactors (MBR) in wastewater treatment. The mixed liquor samples were fractioned by size into MLSS, colloids quantified by colloidal TOC, and dissolved solutes. The experimental results showed that their significance in membrane fouling was strongly related to aeration intensity. In the absence of sparging aeration, both MLSS and colloids contributed to membrane fouling which was further enhanced by their interactions. For the tested membrane module operated at the vigorous aeration intensity typically employed in practice, however, the deposition of colloids was identified as the most important mechanism controlling membrane fouling rates. In contrast, much fewer effects were exerted by MLSS: the overall fouling rates were increased initially, and then reduced with increasing concentration of MLSS. Thus, the aeration-induced turbulence should be considered for properly assessing the mixed liquor fouling potential for wastewater MBR processes. Finally, little difference in fouling rates was observed with the use of cyclic aeration mode as compared to continuous aeration mode.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".