Membrane fouling remediation in ultrafiltration of latex contaminated wastewater
Bibliographic record
Abstract
The current study aimed to remediate membrane fouling by latex effluent by altering membrane surface charge or ionic strength of the effluent. Hydrophilic polysulfone and Ultrafilic flat membranes, with molecular weight cutoff (MWCO) of 60,000 and 100,000, respectively, and hydrophobic polyvinylidene difluoride membrane (PVDF; MWCO 100,000) were used under a constant flow rate and in cross-flow mode for ultrafiltration of latex effluent. The effect of linear alkyl benzene sulfonate (LAS) on the ionic strength of the effluent and the zeta potential of latex particles was investigated. LAS was also used to improve the anti-fouling properties of the membrane surface. The ionic strength of latex effluent was increased by raising its pH from 7 to 12, resulting in an increase of the zeta potential negativity of the latex particles from −26.61 to −42.66 mV. LAS was found to be an ineffective pretreatment for limiting the fouling propensity of latex effluent using hydrophilic membranes even at high concentration and long treatment times. It was concluded that LAS-treated membrane surface is much more favorable than pH changed feed pretreatment. The total mass of fouling decreased by 44.00 and 29.60%, when PVDF membrane surface was treated with LAS at a concentration of 1 × 10−4 g/L, and latex effluent at pH 11 was used, respectively.
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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".