Assessing irreversible fouling behavior of membrane foulants in the ultrafiltration of natural water using principal component analysis of fluorescence excitation-emission matrices
Bibliographic record
Abstract
Natural river water is comprised of different foulant components such as natural organic matter and colloidal/particulate matter. Both individual and combined contributions of these foulant components results in different fouling behaviour. The ability to characterize these contributions that lead to reversible and irreversible membrane fouling would be beneficial for the implementation of fouling monitoring and control strategies for membrane-based drinking water treatment operations. A fluorescence excitation-emission matrix and principal component analysis-based approach was able to qualitatively estimate the accumulation of humic substances (HS)-, protein- and colloidal/particulate matter-like foulant components in membranes during the ultrafiltration (UF) of natural river water. A bench-scale flat sheet UF cross-flow set-up and successive permeation and membrane backwashing cycles were used. Analysis of the accumulation of these foulant components revealed that the increased levels of colloidal/particulate matter accumulation in the membranes appeared to have increased the extent of irreversible fouling by HS-like matter whereas lower irreversible fouling by protein-like matter was observed with increased colloidal/particulate matter accumulation. The results also indicate that the combined contributions by these foulants are important in the fouling of membranes during the UF of river water.
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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 | 0.000 | 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 | 0.000 | 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".