Simulation of Recovery of Aroma Compound from Aqueous Solutions by Batch Pervaporation Coupled with Permeate Decantation and Water Phase Recycle
Bibliographic record
Abstract
In this work, the dynamic (batch) models of recovery of low soluble aroma compounds from aqueous solutions by pervaporation coupled with permeate decantation and water phase recycle was derived and simulated. The model was run to simulate the pervaporation of propyl propionate-water separation using PEBA membrane. The effect of two parametric models (i.e. ratio of feed mass over membrane area (F0/Am) and the aroma solubility in water) on the aroma compound recovery was studied. As compared to the conventional pervaporation, the application of water phase recycle was known to be able to enhance the recovery of aroma compounds in various extents, depending on the operating time and aroma solubility. The larger membrane area used (or smaller (F0/Am)) did not affect the maximum aroma compound recovery, but only shortened the operating time. For the aroma solubility of 0.56, 1, and 5 wt.%, referring to the optimum operating time in the conventional mode, the extent of improvement in recovery can be around 7, 10 and 16%, respectively. The modified pervaporation process also offered another advantage that the process would not be strictly restricted by the operating time due to permanent increase in cumulative mass of aroma compound recovered in the product.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".