The Effect of Supported Liquid Membrane and Liquid Membrane Phase on the Extraction Efficiency of Coal Gasification Wastewater
Bibliographic record
Abstract
The extraction and recovery of phenol from high concentration coal gasification wastewater has been investigated using polypropylene (PP) hollow fiber membranes and polyvinylidene fluoride (PVDF) hollow fiber membranes as supported liquid membrane and the mixture of extraction agent and paraffin as liquid membrane phase, with sodium hydroxide as stripping agent. The effect of the composition of the liquid membrane phase made of N-503-kerosene, tributyl phosphate (TBP)-kerosene or trialkyl phosphine oxide (TRPO)-kerosene on extraction efficiency has been studied. The supported liquid membrane give novel extraction result for the extraction of phenol from high concentration coal gasification wastewater when proper liquid membrane phases and liquid membrane support body materials are used in supported liquid membrane system. Under the operating conditions of waste water temperature at 20 ˚C pH at 8.4, waste water and stripping phase flow rate at 5 L/h, the supported liquid membrane using PP as supported liquid membrane and the extraction agent of TBP with volume proportion of 20% could operate continuously and stablely more than 24 hours and the extraction efficiency be 4% higher than that of PVDF-TBP supported liquid membrane systems.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".