Investigation of canola oil-diesel blend with an antioxidant in a DI diesel engine: performance and emission analysis
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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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 | 0.001 |
| 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".