Detailed study of performance and emission characteristics of diesel engine fuelled with biodiesel and additive
Bibliographic record
Abstract
The use of biofuels, namely alcohols and biodiesel, leads to lower efficiency and higher nitric oxide (NO) emissions compared with the use of diesel. The objective of this study was to reduce the emissions and enhance the performance from neat coconut oil biodiesel. To begin, the diethyl ether (oxygenated additive, OA) was blended with transesterified coconut oil biodiesel (CBD100). The OA was varied in CBD at 5% and 10% on a volume basis. The results revealed that the addition of the OA to CBD resulted in no phase separation in all working conditions. Further, adding 10% OA to CBD resulted in lower carbon monoxide (1.7%), smoke (2.1%), NO (2.7%), and hydrocarbon (1.9%) emissions than neat CBD. In addition, efficiency increased (1.2%) and fuel consumption lowered (1.3%) by blending the OA with CBD at all loads.
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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".