A Review on the Effects of Biodiesel Blends on Compression Ignition Engine NOx Emissions
Bibliographic record
Abstract
Biodiesels are produced by the transesterification of corresponding triglyceride feedstocks of vegetable (example: soybean, canola, palm, karanja) or animal fat sources.Currently, the leading feedstocks are soybean oil in the U.S., canola oil and rapeseed oil in Canada and Europe, and palm, karanja, jatropha and other oils in Asia.Due to the cost and production considerations of these biodiesels, blending biodiesels with the petroleum fuels appears to be a prudent option in the near-term.The use of biodiesels in compression ignition engines results generally in a reduction in the emissions of carbon monoxide (CO), hydrocarbons (HC), and particulate matter (PM), but a slight increase in the oxides of nitrogen (NO x ) emission.The reported NO x emissions do not exhibit definitive trends and the results are significantly influenced by many factors, including engine type and design, test cycle, start of injection, ignition delay, fuel composition, adiabatic flame temperature, radiative heat transfer, fluid dynamics and combustion phasing.Due to appreciable variations in the physical properties and the highly nonlinear nature of the combustion process, the NO x emission with biodiesel blends does not vary monotonically with the percentage of biodiesel in the blend.Hence, the intricate dependence of NO x on biodiesel and its blending effect cannot be completely explained under all engine type and operating conditions.Although the literature contains several studies on the performance and emissions of compression ignition engines fueled with neat biodiesels, the information on the effects of blends is scattered and has not yet achieved a definitive status to explain the blending effect on NO x .Hence, this work was motivated to review the available data with respect to the NO x emission from engines fueled with the petroleum diesel/biodiesel blends.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".