Biodiesel production from waste cooking oil using KOH/HY-type nano-catalyst derived from silica sand
Bibliographic record
Abstract
The present study aimed to synthesize a Y-nanozeolite catalyst using the hydrothermal method and Iraqi sand-derived silica as a low-cost and readily available raw material. The catalyst was tested before and after loading with potassium hydroxide (KOH). The experiments were conducted in a batch reactor under different temperatures (40, 50, and 60 °C) and a 3-h reaction time, using the prepared Y-catalyst with three different particle sizes (75, 600, and 1000 μm). The results showed that increasing the temperature and/or reaction time generally resulted in increased conversion and yield when the catalyst was unpromoted with KOH, reaching a range of 55.56% and 33.33%, respectively. However, a significant increase in the conversion and yield was observed after promoting the catalyst with 10% KOH molecules. The optimal conditions for achieving the highest conversion and yield of biodiesel were determined to be 86.67% and 82.22%, respectively. These conditions involved a temperature of 60 °C, a reaction time of 2 h, and the use of a catalyst with a particle size of 75 μm loaded with 10% KOH. The use of a heterogeneous catalyst loaded with the base in a low percentage helps to dispense with the use of homogeneous catalysts with a high percentage of bases.
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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".