Application of MFI-UF fouling index with NOM fouling under various operating conditions
Bibliographic record
Abstract
ABSTRACT Fouling research with polymeric membranes has demonstrated that various natural organic matter (NOM) fractions contribute differentially to membrane fouling behavior. However, limited studies exist analyzing the sensitivity of the MFI-UF to be used as a tool to differentiate NOM fouling components. The results here indicate that MFI-UF is a suitable tool for assessing NOM fouling. Specifically, NOM fouling potential was in the order of organic proteins (as BSA), polymers (alginate), and humic acid, respectively. Further, a mixed solution containing BSA, alginate and humic acid fouled similarly to the BSA solution indicating the high fouling potential of organic proteins in membrane systems. The MFI-UF value was found to increase by > 30% with increasing pressure (1–3 bar) and decreasing temperature (35°C–5°C). The filtered water volume was found to correlate with the MFI-UF values indicating the dependency of the method on testing conditions. Incorporating water viscosity and pressure values against normalized conditions (20°C and 2 bar) with the standard MFI-UF equation was found to be useful to estimate MFI-UF values at variable operating conditions, thus, enhances the potential application range of MFI-UF as a fouling index for NOM.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".