Investigation of canola oil-diesel blend with an antioxidant in a DI diesel engine: performance and emission analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With fossil fuel reserves declining and environmental concerns growing, the search for renewable, cleaner alternatives to diesel fuel is increasingly important. In Canada, the widespread availability of canola oil makes it a viable bio-based feedstock for fuel applications. Due to its physical and chemical similarity to diesel, small amounts of canola oil can be blended directly with diesel for use in engines with little or no modification. This study investigates the performance and emission characteristics of a HATZ 2G40 two-cylinder, light-duty direct-injection (DI) diesel engine fueled with diesel–canola oil blends (2% and 5% by volume), combined with the antioxidant 2,6-Di-tert-butyl-4-Methoxyphenol (DBMP) at concentrations of 0.1%, 0.5%, and 1% by volume. Engine tests were performed at three speeds (1000, 2100, and 3000 rpm) under three load conditions (20%, 50%, and 80%). Results showed that DBMP-treated blends improved brake thermal efficiency (BTE), significantly reduced smoke emissions, and lowered nitrogen oxides (NOx) compared to pure diesel. These findings highlight the potential of using small amounts of canola oil, enhanced with antioxidants, as a renewable, cleaner-burning partial substitute for diesel fuel – especially in canola-producing regions like Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it