Filtration charaterization methods in MBR systems: A practical comparison
Bibliographic record
Abstract
Three different methods for fi ltration characterization in Membrane Bioreactor (MBR) systems were compared. These were the Delft Filtration Characterization Method (DFCm), the Berlin Filtration Method (BFM) and an ex situ side-stream fi ltration test cell for the determination of the critical fl ux. The ex situ fi ltration test cell and the DFCm fi lter activated sludge from a tank, while the BFM works in situ with a test cell directly submerged into the biological tank at similar operational conditions to a typical MBR plant. The mixed liquor of four different MBR units was characterised several times with the three fi ltration methods. The three tested methods seemed to agree in the classifi cation of the tested mixed liquors in terms of fi lterability except for one of the tested activated sludges. Additionally, three critical fl ux protocols were studied using the BFM fi ltration test cell. The fi rst consisted in the classical fl ux-step method, the second included relaxation between fi ltration steps and in the third protocol, 2 min fi ltration at a fi xed fl ux were performed before every fi ltration step. The last protocol was selected as the most representative of full scale MBR operation and the most interesting one for giving valuable information about the irreversibility of the fouling.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".