Performance and Emission of Small Diesel Engine Using Diesel-Crude Palm Oil-Water Emulsion as Fuel
Bibliographic record
Abstract
Diesel and crude palm oil (CPO) emulsion was drop-in replaced of diesel oil in a small diesel engine to test the engine performance and emission. In the study, the compositions of diesel/CPO/water of 95/0/5, 90/0/10, 90/5/5, 85/5/10, 85/10/5 and 80/10/10 by volume were used in a four-stroke single cylinder diesel engine having a pre-combustion chamber. The engine speed was in a range of 1000 – 2000 rpm. From the results, it could be found that the torque and the engine power for the emulsion of 90/5/5 were close to those of from the diesel oil and the performance were poorer when the percentages of CPO and water were increased. The specific fuel consumption of the emulsified oil with the composition of 90/5/5 was quite close to that of the diesel oil at low engine speed and higher consumption was needed with higher amount of CPO and water in the emulsion due to lower heating value in the emulsion. The emissions in terms of CO, NO x and black smoke for the emulsified oil could be reduced significantly. After 200h of the operation, the wear and the physical corrosions in the engine components for the emulsified fuel of 90/5/5 composition are not much different from those for the diesel oil. Key words : Palm –diesel emulsion; Diesel engine performance; Emission
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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".