Biodiesel Production using CaO/γ‐Al<sub>2</sub>O<sub>3</sub> Catalyst Synthesized by Sol‐Gel Method
Bibliographic record
Abstract
In this study, 40 % CaO/γ‐Al2O3 catalyst was used for biodiesel production from corn oil. A transesterification reaction was done for 5 h at a temperature of 65 °C in the presence of corn oil, methanol (methanol to oil molar ratio of 12:1), and CaO/γ‐Al2O3 catalyst (0.06 g/g (6 wt%)). Catalyst used in this study was synthesized using the sol‐gel method. In this method, two parameters of gelation temperature and nitric acid concentration were used as variables in the catalyst synthesis step, and experiments were designed using central composite design (CCD). The results indicate that the optimal point is achieved at a gelation temperature of 70 °C and nitric acid concentration of 0.050 mol/L; in such conditions the purity and yield of produced biodiesel are 87.89 % and 79.10 %, respectively. Moreover, 40 % CaO/γ‐Al2O3 catalyst was synthesized using the impregnation method and the same reaction conditions were used. The catalyst synthesized by the sol‐gel method in optimal conditions and catalyst synthesized by impregnation method both were reused five times each. Catalyst reuse reduces the purity and yield of the produced biodiesel, because in each case of catalyst use, some amount of CaO is extracted by methanol. In addition, the leaching rate of CaO in the catalyst synthesized by the impregnation method was greater than that of the catalyst synthesized by the sol‐gel method; consequently there is more reduction the activity of the catalyst synthesized by impregnation method.
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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 | 0.000 |
| 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 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".