Scale-up of Biodiesel Synthesis in Chemical Interesterification Reaction of Rapeseed Oil with Methyl Formate and Methyl Acetate
Bibliographic record
Abstract
Production and use of biofuels is important to minimize carbon dioxide emissions in the world. One of biofuels -biodiesel -is obtained from vegetable oils in form of fatty acid methyl esters (FAME). Industrially production of biodiesel proceeds by transesterification with methanol and production of by-product glycerol. In order to exclude the glycerol production, we have researched biodiesel synthesis in potassium tert-butoxide catalyzed chemical interesterification reaction with methyl formate and methyl acetate. In order to ensure the scale-up of 50-times the synthesis has been made using flasks from 100 mL volume to 4 L batch reactor. Scale-up process of biofuel synthesis shows that it proceeds without considerable differences in synthesis procedure and composition of products, but the excess reagent evaporation step is more efficient for smaller volumes. Reactions with methyl acetate allow to make full conversion of oil to biofuel, however the FAME content is only 72%, following conditions were used: temperature 55 C, 60 minutes, methyl acetate to oil molar ratio 30, catalyst 1M tBuOK in THF, and catalyst to oil molar ratio 0.10. Reactions with methyl formate allow to obtain biofuel with lower yield but containing 93% of FAME, following conditions were used: temperature 30 C, 45 minutes, methyl formate to oil molar ratio 36, catalyst 1M tBuOK in tBuOH, and catalyst to oil molar ratio 0.15. Fuel properties were tested for both biofuels obtained in 4 L reactor. Biofuel obtained in reaction with methyl formate showed better properties, although both fuels could be used as diesel fuel additives. The area of using the methyl formate biofuel would be wider, therefore we propose that methyl formate is more promising reagent for synthesis of biodiesel. Obtained different glycerol formates could be a valuable by-product.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".