Efficient recovery of phenol from phenolic wastewater by emulsion liquid membrane
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".