The Effect of Off-Spec Canola Biodiesel Blending on Fuel Properties for Cold Weather Applications
Bibliographic record
Abstract
Biodiesel is a renewable and reduced-emission alternative fuel produced mainly from the alcoholysis of vegetable oils and/or animal fats. It is mainly used in blends with diesel fuel to reduce emissions, enhance lubrication and lower sulfur content. Being able to accurately determine the physicochemical properties of blended fuel is important for optimal injection, combustion, and lubricating performance in diesel engines. Also, fuel properties vary as the ratio of biodiesel-diesel changes, affecting the final fuel quality. In this study, a wide range and narrow intervals of (0, 2, 4, 6, 8, 10, 12, 15, 18, 20, 25, 35, 50, 75 and 100% by volume) off-quality canola-based biodiesel blends were prepared at ambient conditions and used to study the blended fuel properties (density, kinematic viscosity, flash point, cloud point and pour point). This is particularly important for examining the effect of a biodiesel content of more than 20%—the industry maximum blend content—on cold flow properties, fuel stability, energy value, and emissions. It was found that the kinematic viscosity and density increased linearly as the concentration of the biodiesel in the blend increases. The pour point and cloud point temperature showed a small increase up to 35% blending ratio and a rapid increase in temperature for biodiesel concentrations higher than 35%. Also, the flash point remained almost constant at an average value of 73 °C for blends less than 20%, above which the values for the flash point increased exponentially with biodiesel concentration. Furthermore, predictive correlations were developed for all tested fuel properties from regressing corresponding experimental data. All models exhibited excellent agreement with experimental data with an average absolute deviation of less than 5%.
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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) | 0.001 | 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 | 0.000 |
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".