Jatropha oil production and an experimental investigation of its use as an alternative fuel in a DI diesel engine
Bibliographic record
Abstract
In this study, a non-edible vegetable oil was produced from jatropha fruits as a substitute fuel for diesel engines and its usability was investigated as pure oil and as a blend with petroleum diesel fuel. A direct injection (DI) diesel engine was tested using diesel,Jatropha oil, and blends of Jatropha oil and diesel in different proportions. A wide range of engine loads and Jatropha oil/diesel ratios of 5/95% (J5), 10/90% (J10), 20/80% (J20), 50/50% (J50), and 80/20% (J80) by volume were considered. The following performance parameters were measured; brake thermal efficiency, brake specific fuel consumption and CO and CO2 emissions. No significant change in brake thermal efficiency and brake specific fuel consumption was experienced up to J20 ratios. However, higher blends suffered from deterioration in efficiency and fuel consumption about 10 to 25%. At low load operations, CO2 emission with blends was lower than that of diesel, whereas, at high loads, CO2 emission became higher with a higher percentage of Jatropha oil in the blends. However, CO emission with blends was much higher than that of diesel; the higher the percentage of Jatropha oil in the blend, the higher the CO emission. Key words: Non-edible vegetable oil, Jatropha oil, diesel-Jatropha oil blends, viscosity and heating, DI diesel engine, performance, emissions
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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".