An in-depth comparative analysis of fuel evolution: tracking physico-chemical characteristics in conventional diesel, e-diesel, biodiesel, and aged biodiesel
Bibliographic record
Abstract
The global reliance on fossil fuels for transportation poses significant challenges due to dwindling reserves and the environmental impact of greenhouse gaseous emissions, which necessitates the exploration, and development of sustainable, renewable, and less polluting alternatives. This study investigates the development of sustainable fuels by evaluating e-diesel blends with ethanol concentrations ranging from 5 to 90% and producing biodiesel from castor oil via transesterification under optimized conditions (e.g. 6:1 alcohol-to-oil molar ratio, 60 °C, methanol, KOH catalyst), achieving an 86.55% biodiesel yield. Fuel properties of conventional diesel, 25% e-diesel, fresh biodiesel, biodiesel stored up to one year, long-stored biodiesel, and raw castor oil were systematically compared. Key parameters for fresh and stored biodiesel included density (0.898–0.891 g/ml), viscosity (8.83–10.8 cSt), flash point (152–155 °C), fire point (170–175 °C), pour point (4 °C), cloud point (10 °C), acid value (0.5–0.54 mg KOH/g), carbon residue (0.05–0.051 wt%), sulfur content (0.029–0.035 wt%), moisture (10–8 ppm), iodine value (85–80 g I2/100 g), saponification value (92–82 mg KOH/g), and calorific value (38 MJ/kg). These results were benchmarked against conventional diesel values including density (0.848 g/ml), viscosity (5.03 cSt), flash point (41 °C), fire point (46 °C), pour point (0 °C), cloud point (6 °C), acid value (1.12 mg KOH/g), carbon residue (0.084 wt%), sulfur content (0.019 wt%), moisture (10 ppm), iodine value (40 g I2/100 g), saponification value (0 mg KOH/g), and calorific value (45 MJ/kg). E-diesel blends with ethanol above 25% showed unfavorable fuel properties. The study also reviews recent findings on castor biofuel’s effects on engine performance and emissions. Overall, this work demonstrates the promise of castor biodiesel as a renewable fuel and highlights challenges in optimizing ethanol-diesel blends for practical application, contributing essential data for sustainable fuel advancement.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".