Optimization of semi‐pulsatile liquid‐liquid extraction operations in milli‐channels
Bibliographic record
Abstract
Abstract The work in this manuscript presents liquid‐liquid extraction augmentation and optimization due to flow pulsations on a continuous flow. The mass transfer is achieved via a transfer species (acetic acid) that diffuses in the aqueous phase (water), which is in a continuous flow, from the organic phase (toluene), exhibiting pulsed flow pattern. It is observed through experiments that the incorporation of pulsation leads to enhanced extraction/mass transfer compared to continuous flows. Also, an increase of the pulsation parameters, such as amplitude and frequency, increases the mass transfer, but when the process is evaluated in terms of economy, it is found that the rate of extraction per unit power is maximum for moderate frequencies and amplitudes. Based on the experiments, a Linton and Sherwood‐like correlation for determining extracted concentration at the exit of the test section in semi‐pulsatile flow conditions is proposed. During the course of experiments, it is found that the flow pattern changes from dispersed‐type flow pattern of the organic phase to slug and then slug dispersed with an increase of superficial velocity of toluene, at a particular superficial velocity of the water. Also, the total power consumed during the extraction process increases with an increase in the product of amplitude and frequency. With the experimental approach presented in this paper, one will be able to optimize semi‐pulsatile liquid‐liquid mass transfer operations.
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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 | 0.001 | 0.000 |
| 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".