Effect of EGR on Performances and Emissions of DI Diesel Engine Fueled with Waste Plastic Oil: CDF Approach
Bibliographic record
Abstract
Recently, there is an increasing interest in the pyrolysis of waste plastic into usable fuel as a friendly environment method for waste plastic disposal. The existing literature from various studies stated that the major problem related to the use of WPO in diesel engines is the high NOx emissions level. This paper aims to remedy this problem by suggesting the best EGR percentage with the advanced optimum injection timing. Primary, 5 EGR percentage fractions are considered: 0%, 5%, 15%, 20% and 25% percent. The results showed that 25% is the best percentage regarding emissions. However, a significant reduction in mean in-cylinder pressure, temperature, and heat release rate was depicted with the EGR fraction increase. Injection timing is advanced to recoup the decrease in performance. The results showed that 25% of EGR and advanced injection timing by 5 degrees would be better for performances and emissions of DI diesel engine while running with waste plastic oil as an alternative fuel.
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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".