Efficient recovery of phenol from phenolic wastewater by emulsion liquid membrane
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Bibliographic record
Abstract
Abstract The recovery of phenol from phenolic wastewater by emulsion liquid membrane (ELM) was investigated. The W/O emulsion was prepared with kerosene, Span−80, carrier, liquid paraffin, and NaOH solution. The effects of NaOH concentration, oil–internal solution ratio, shearing speed, Span−80 concentration, and carrier type and concentration on emulsion breakage were studied. In the single factor experiments of stability of W/O emulsion, the lowest percentages of emulsion breakage were achieved at a NaOH concentration of 0.03 g/ml, an oil–internal solution ratio of 2:1, a shearing speed of 1500 r/min, a Span−80 concentration of 8%, a tributyl phosphate (TBP) concentration of 0.8%, and an ethyl acetate concentration of 0.8%, respectively. Then, the effects of nine factors on extraction efficiencies of phenol were investigated. This indicated that the effects of shearing speed, oil‐internal solution ratio, emulsion‐external solution ratio, liquid paraffin concentration, and mixing speed on extraction efficiencies of phenol were limited. However, the carrier concentration, NaOH concentration, Span−80 concentration, and phenol concentration had important impacts on the extraction efficiency of phenol. The extraction efficiency of phenol could reach 99.7%. Besides, the results of orthogonal experiments indicated that during the extraction of phenol by ELM, the order of importance of factors was NaOH concentration > emulsion‐external solution ratio > volume fraction of Span−80 > volume fraction of TBP. After extraction, the recycled emulsion with Span−80 could not easily be effectively demulsified through heating, which only provided the highest demulsification efficiency of 18.2%. However, the recycled emulsion could be effectively demulsified through centrifugation, which could get the highest demulsification efficiency of 86% at a centrifugal rotational speed of 2000 r/min and a centrifugal time of 25 min.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it