Biodiesel production unpacked: Data-driven analysis of key operational factors
Bibliographic record
Abstract
Biodiesel is a promising renewable alternative to depleting fossil fuels. However, variabilities in the operational factors that affect biodiesel yield make it difficult to ascertain which parameters mostly affect biodiesel production yield. The objective of the current study was to statistically evaluate the commonly reported major operational parameters that affect biodiesel yield using a wide dataset collected from the literature. CART@Regression results implied that feedstock type and temperature were the most and least important factors, respectively, for biodiesel production from both used and neat oils. Excluding temperature, all factors were above 73 % as important as feedstock type for used oils, suggesting that optimization of all five operational parameters (i.e., feedstock type, alcohol-to-oil-molar ratio, reaction time, catalyst type, and catalyst quantity) will increase biodiesel yield from used oils. In contrast, catalyst quantity, alcohol-to-oil molar ratio, and temperature were below 50 % as important as feedstock for neat oils, which implied that their control does not have much effect on biodiesel yield for the current dataset. Basic statistics (outlier test, normality check and correlation analysis), principal component analysis and two-dimensional surface contour plots were conducted. Multiple criteria analysis showed that potassium oxide is the best catalyst type for used oils, while muhua oil is the best feedstock for biodiesel production from neat oils. The present study offers fundamental knowledge regarding operational parameters which could support large-scale biodiesel production and hence boost the biorefinery sector.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".