Synthesis of Biodiesel from Tobacco and Waste Frying Oil Using Heterogeneous KHCO3/Al2O3 Catalyst
Bibliographic record
Abstract
The transesterification of tobacco seed oil and used frying oil to methyl esters (biodiesel) was studied using potassium bicarbonate loaded on alumina as heterogeneous catalyst. Reaction parameters such as catalyst concentration, methanol to oil ratio, reaction time, and agitation speed on the conversion of tobacco seed and used frying oil were investigated. The catalyst loaded KHCO3 of 30 % m/m on Al2O3, after being calcined at 700°C for 6 h, was found to be the optimum catalyst. The quality of the methyl esters was tested according to the European standard EN 14214. The two types of biodiesel produced seemed to meet all the parameters of the European standard except the oxidation stability. In the case of used frying oil biodiesel, not only the oxidation stability was not met, but this biodiesel did not also meet the acid value and water content specifications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".