Biodiesel Feedstock from Emulsions Produced by Aqueous Processing of Yellow Mustard
Bibliographic record
Abstract
Abstract The multi‐stage treatment of stable oil‐in‐water emulsions produced during non‐enzymatic aqueous processing of dehulled yellow mustard flour with cyclic ethers [tetrahydrofuran (THF) and 1,4‐dioxane] was investigated to produce a single‐phase oil‐solvent‐water miscella suitable for biodiesel production. While the single‐stage treatment of yellow mustard emulsion recovered 97 % and 95 % of the oil by using 4:1 THF:oil and 9:1 dioxane:oil weight ratios, respectively, miscella phases containing more than 7 % water formed, which made them unsuitable as biodiesel feedstock. Multi‐stage treatments of the emulsion using lower THF:oil and dioxane:oil weight ratios were further developed to produce oil‐solvent‐water miscella phases with low water content. While three‐stage extraction of emulsions using 0.5:1, 1:1, 1.5:1, and 2:1 dioxane:oil weight ratios did not destabilize the emulsion, three‐stage extraction using 0.5:1 and 0.75:1 THF:oil weight ratios effectively recovered over 97 % of the oil, resulting in the production of oil‐rich miscella phases containing only 1 % and 1.5 % water, respectively. These miscella phases were analyzed for free fatty acid and phosphorus contents and proved to be excellent feedstocks for the preparation of high‐purity methyl esters through single‐phase base‐catalyzed transmethylation.
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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 | 0.000 | 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".