Biodiesel Productions from Vegetable Oils Using Heterogeneous Catalysts and Their Applications as Lubricity Additives
Why this work is in the frame
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Bibliographic record
Abstract
Fatty acid methyl esters (FAME) are produced by transesterification of vegetable oil with methanol usually in presence of an alkaline catalyst. The purpose of this work is to compare the performance of heterogeneous (CaO, MgO, Ba(OH) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , Li/CaO, Zeolite) and homogeneous (KOH) catalyst for the transesterification of vegetable oil. The effect of stirring speed and addition of ethanol with methanol on ester yield was studied. This research showed that stirring speed has substantial effect on the ester yield both in homogeneous and heterogeneous catalyzed reaction. Addition of ethanol with methanol has improved the rate of formation of ester, thus helped in reducing the mass transfer limitations. Amongst all the heterogeneous catalysts examined, the performance of Ba(OH) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> catalyst was better which produced 99 wt% ester yield in 480 min and its performance was comparable to that of potassium hydroxide. Ester obtained from canola oil and methanol and ethanol mixture (3:3) {MEE (3:3)} acted as a good lubricity additive by reducing wear scar area by 16% and improving the lubricity number of base fuel by 20%.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it