Synthesis of Biodiesel from Canola Oil Using Heterogeneous Base Catalyst
Bibliographic record
Abstract
Abstract A series of alkali metal (Li, Na, K) promoted alkali earth oxides (CaO, BaO, MgO), as well as K2CO3 supported on alumina (Al2O3), were prepared and used as catalysts for transesterification of canola oil with methanol. Four catalysts such as K2CO3/Al2O3 and alkali metal (Li, Na, K) promoted BaO were effective for transesterification with >85 wt% of methyl esters. ICP‐MS analysis revealed that leaching of barium in ester phase was too high (~1,000 ppm) when BaO based catalysts were used. As barium is highly toxic, these catalysts were not used further for transesterification of canola oil. Optimization of reaction conditions such as molar ratio of alcohol to oil (6:1–12:1), reaction temperature (40–60 °C) and catalyst loading (1–3 wt%) was performed for most efficient and environmentally friendly K2CO3/Al2O3 catalyst to maximize ester yield using response surface methodology (RSM). The RSM suggested that a molar ratio of alcohol to oil 11.48:1, a reaction temperature of 60 °C, and catalyst loading 3.16 wt% were optimum for the production of ester from canola oil. The predicted value of ester yield was 96.3 wt% in 2 h, which was in agreement with the experimental results within 1.28%.
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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 | 0.000 |
| 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".