Investigation of micellar‐enhanced ultrafiltration ( <scp>MEUF</scp> ) using rhamnolipid for heavy metal removal from desalter effluent
Bibliographic record
Abstract
Abstract The removal of heavy metals from desalter effluent is an important issue due to the toxicity of these pollutants. In this study, micellar‐enhanced ultrafiltration (MEUF) is applied for the removal of heavy metals from synthetic desalter effluent. MEUF is a process in which surfactants above their critical micellar concentration (CMC) form micelles. Micelles bind compounds with low molecular weight; then, these are rejected by a semi‐permeable membrane. Rhamnolipid is an anionic biosurfactant that has advantages such as biodegradability and low CMC. Rhamnolipid complexation with synthetic desalter effluent demonstrates high efficiency for heavy metal removal. The highest overall removal efficiency was reached at a rhamnolipid concentration of 300 mg/L with 94.07%, 81.93%, 99.81%, 76.00%, and 41.91% for Zn +2 , Mg +2 , Cu +2 , Mn +2 , and Na + , respectively. Additionally, loading capacity on rhamnolipid micelles was evaluated to have better selectivity for Mn +2 > Cu +2 > Zn +2 > Mg +2 > Na + . Also, the results showed that phenol has a significant effect on heavy metal removal, decreasing CMC, which increases micelles. Finally, the permeate flux decreases after increasing the rhamnolipid concentration due to the formation of the gel layer, creating an additional resistance to the permeate flux that goes through the membrane. This work shows that MEUF with rhamnolipid is a reliable method for heavy metal removal from desalter effluent.
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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".