Variables Affecting the Production of Standard Biodiesel
Bibliographic record
Abstract
Abstract Biodiesel is composed of fatty acid methyl esters, currently made from vegetable oils using basic catalysts. The oils must be reacted two or three times with methanol, in the presence of sodium methoxide to make products which meet the ASTM and European biodiesel standards. It is also believed that sodium hydroxide can never be used as the catalyst because it causes soap formation, which either lowers the yield or raises the acid number and makes product isolation difficult. Methods for producing standard biodiesel from low‐acid‐number soybean oil, in one chemical reaction using sodium hydroxide and a cosolvent, were recently reported. This study reports the effects of variables on the acid numbers and chemically bound glycerol contents of the products which led to the methods. These variables were the molar ratio of alcohol to oil, catalyst concentration, cosolvent volume, and reaction time. The alcohol‐to‐oil molar ratio must be at least 14, and the sodium hydroxide concentration should be at least 1.2 wt% (based on oil), to meet the necessary acid number and glycerol contents of the biodiesel. The volume of tetrahydrofuran cosolvent used must be 90–130% of that required to just create complete miscibility at the beginning of the reactions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".