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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".