Particulate Emissions From Karanja Biodiesel Fuelled Turbocharged CRDI SUV Engine
Bibliographic record
Abstract
The use of biodiesel substantially reduces particulate matter (PM), hydrocarbon (HC) and carbon monoxide (CO) emissions, slightly reduces power output; increases fuel consumption and marginally increases oxides of nitrogen (NOx) emission in an unmodified common rail direct injection (CRDI) diesel engine. Lower blends of biodiesel demonstrated lower emissions, while easing pressure on scarce petroleum resources, without significantly sacrificing engine power output and fuel economy. However due to adverse health effects of smaller size particulate matter (PM) emitted by internal combustion (IC) engines, most recent emission legislations restrict the PM mass emissions in addition to total particle numbers emitted. It is an overwhelming argument that usage of biodiesel leads to reduction in PM mass emissions. In this paper, experimental results of PM emissions using Karanja biodiesel blends (KB20 and KB40) in a modern CRDI transportation engine (maximum fuel injection pressure of 1600 bar) have been reported. This study also explores comparative effect of varying engine speed and load on PM emissions for biodiesel blends vis-à-vis baseline mineral diesel. Particulate size-number distribution, particle size-surface area distribution and total particulate number concentrations were experimentally determined under varying engine operating conditions and compared with baseline mineral diesel. KB20 showed highest particulate number concentration upto 80% rated engine loads, however at rated load, KB40 emitted highest number of particulates.
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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 | 0.000 |
| 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".