Membrane fouling remediation in ultrafiltration of latex contaminated water and wastewater
Bibliographic record
Abstract
The goal of the present study was to remediate membrane fouling of latex effluent by altering the membrane surface charge or the ionic strength of simulated latex effluent either through the pH change or using anionic surfactants. Hydrophilic Polysulfone and Ultrafilic flat membranes, with MWCO of 60,000 and 100,000, respectively, as well as hydrophobic Polyvinylidene Difluoride membrane with MWCO of 100,000, were used under a constant flow rate and cross-flow mode in ultrafiltration of latex solution. The effect of Linear Alkyl Benzene Sulfonate (LAS) on the ionic strength of the latex solution and the zeta potential of latex particles at different LAS concentrations was investigated. LAS was also used, at different concentrations and various treatment times in order to improve the antifouling properties of membrane surface. The results obtained indicate that increasing the ionic strength of latex effluent was achieved by increasing its pH from 7 to 12, resulted in an increase of the zeta potential negativity of the latex particles from -26.61 to -42.66 mV, while LAS had an opposite effect even at high concentration and for long treatment times. The optimum enhancement of membrane surface hydrophilicity occurred in the LAS treatment at a concentration of 1x10-4 g/L. However, the optimum treatment time was different for each membrane. Increasing the ionic strength of latex effluent or enhancing the membrane surface hydrophilicity caused a significant increase in the cumulative permeate flux, a substantial decrease in the total mass of fouling, and a noticeable decrease in the specific power consumption.
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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".