Mass transfer performance of pulsed disk and doughnut column ( <scp>PDDC</scp> ) in the extraction of active ingredients in peppermint extract obtained by hot percolation
Bibliographic record
Abstract
Abstract The active ingredients in peppermint are widely used in the pharmaceutical, food, cosmetic, and other industries. The traditional methods for producing peppermint extract through hydrodistillation (HD) and percolation are long process with low efficiency. The aim of this study is the improvement of the extraction performance and the efficiency of the extract obtained from hot percolation via pulsed disk and doughnut columns (PDDC), which is one of the most important pieces of liquid–liquid extraction (LLE) equipment. Peppermint extract was used as the continuous phase in the PDDC. n ‐Hexane was considered as the dispersed phase in this column. In this study, the effects of three operating parameters were investigated, including the pulse intensity, continuous phase height, and dispersed phase flow rate, on the extraction efficiency of active ingredients in peppermint. The response surface method (RSM) was used to investigate the effect of these parameters on the extraction efficiency. The efficiency of this column in the extracting of active ingredients from peppermint was measured by UV–vis spectrophotometry, via the absorption rate of the main active ingredients of peppermint (menthol) in the samples, and the efficiency was 75% on average. The experimental and theorical of dispersed phase mass transfer coefficient were evaluated and compared. The results showed that the PDDC improved the extraction and mass transfer. By increasing the flow rate of the dispersed phase and the height of the continuous phase, the extraction efficiency and the mass transfer coefficient were increased.
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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) | 0.001 | 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 | 0.001 | 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".