Membrane Fouling Potential of Secondary Effluent Organic Matter (EfOM) from Conventional Activated Sludge Process
Bibliographic record
Abstract
Secondary effluent organic matter (EfOM) from a conventional activated sludge process was filtered through constant-pressure dead-end filtration tests with a sequential ultrafiltration (UF, molecular weight cut-off (MWCO) of 10k Dalton) and nanofiltration (NF, MWCO of 200 Dalton) array to investigate its membrane fouling potential. Advanced analytical methods including liquid chromatography with online carbon detection (LC-OCD) and fluorescent excitation-emission matrix (F-EEM) were employed for EfOM characterization. EfOM consisted of humic substances and building blocks, low molecular weight (LMW) neutrals, biopolymers (mainly proteins) and hydrophobic organics according to the sequence of their organic carbon fractions. The UF rejected only biopolymers and the NF rejected most humics and building blocks and a significant part of LMW neutrals. Simultaneous occurrence of cake layer and standard blocking during the filtration process of both UF and NF was identified according to constant-pressure filtration equations, which was possibly caused by the heterogeneous nature of EfOM with a wide MW distribution (several ten to several million Dalton). Thus the corresponding two fouling indices (kc for cake layer and ks for standard blocking) from UF and NF could characterize the fouling potential of macromolecular biopolymers and low to intermediate MW organics (including humics, building blocks, LMW neutrals), respectively. Compared with macromolecular biopolymers, low to intermediate MW organics exhibited a much higher fouling potential due to their lower molecular weight and higher concentration.
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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 | 0.000 |
| 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".