Production of Isopropyl and Methyl Esters from Yellow Mustard Oil/IPA Miscellas
Bibliographic record
Abstract
Abstract Using an isopropyl alcohol (IPA):flour [volume:weight (ml:g)] ratio of 1.5:1 per stage of extraction resulted in an oil yield of 86.3%. The combined miscella (IPA + oil), which contained 90.6 wt% IPA, 9.8 wt% oil, and 2.1 wt% water, was used as a feedstock for biodiesel production by transesterification. Transesterification of the IPA/oil miscella dehydrated using adsorption on 4Å molecular sieves with 1.2 wt% (based on oil) potassium hydroxide for 2 h at 72 °C converted only 29% of the feed to esters. The addition of methanol (MeOH) resulted in an ester yield of 87%, consisting of 79% methyl ester and 7% isopropyl ester when starting with an IPA:oil:MeOH molar ratio of 146:1:30. By increasing the KOH catalyst to 3 wt%, the ester yield increased to 94%. To increase the ester yield, the miscella was pretreated with sulfuric acid. This resulted in a reduction of the IPA content, the removal of other impurities such as phospholipids, and reduction of the water mass fraction to less than 1%. When IPA was used as a cosolvent with methanol in the transesterification process, a very high ester conversion (>99%) was achieved. The biodiesel produced was compliant with ASTM standards, showing that IPA can be used as a solvent for oil extraction from yellow mustard flour.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".