The effect of residual cationic polymers in swine wastewater on the fouling of reverse osmosis membranes
Bibliographic record
Abstract
Solid-liquid separation with flocculation can be used as pre-treatment for reverse osmosis (RO) filtration as it produces a liquid fraction (LF) low in suspended solids (SS). However, residual polymers in the LF may foul the membrane. Membrane fouling during RO filtration of swine wastewater containing polymers was investigated with respect to polymer charge density (CD), effluent SS concentration and membrane surface charge. Effluents with 765 mg/L SS and without SS were spiked with low and medium CD polymers (0-40 mg/L effluent) then processed with RO membranes having low and high negative surface charges. Fouling intensity was evaluated by comparing permeate flux and water flux recovery of fouled and cleaned membranes. For effluents containing SS, the presence of polymer reduced permeate flux by 4-16% and water flux recovery of the fouled membrane by 0-18%, relative to effluents without polymer. The extent of the fouling was higher with the low than the medium CD polymer. The fouling was mostly reversible as cleaning allowed for over 95% flux recovery, but the membrane with high negative surface charge was more susceptible to irreversible fouling. Adding the low CD polymer to feed without SS had no effect on permeate flux or flux recovery. Membrane fouling thus appeared to be caused by the polymer changing SS-membrane interaction. If flocculation is applied to pre-treat manure, a medium CD polymer should be used to optimize SS removal and a membrane with low surface charge should be selected to minimize 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 | 0.001 | 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 | 0.000 | 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".