The combustion of lemon peel oil/gasoline blends in spark ignition engine with high-insulation piston crown coating
Bibliographic record
Abstract
This study explored the recovery of oil from lemon peel biomass and then tested it in a spark ignition as a substitute for gasoline. The study adopted the micro-arc oxidation coating technique, intending to improve the engine performance of the lemon peel oil-gasoline blends. The oil was recovered from discarded lemon peel biomass using steam distillation and then tested in the engine as a fuel by blending it with gasoline at volume ratios of 10, 20, and 30%. An endoscopic visualization approach was employed in this research work to assess the combustion initiation and flame characteristics of gasoline and lemon peel oil blends under different test conditions. Compared to gasoline and blends comprising 20 and 30% lemon peel oil, the 10% lemon peel oil mix produced higher thermal efficiency and lower emissions. The optical analysis demonstrated that premixed combustion with the 10% blend was found to be the highest, resulting in improved combustion and subsequently increased cylinder pressure. To improve the engine performance of the lemon peel oil blends with higher substitution (20 and 30%), the piston was coated with a ceramic coating. A novel technique, namely the micro-arc oxidation technique, was utilized for the coating. The coated piston engine fueled with a 20% lemon peel oil blend showed a 3% and 4.69% increase in thermal efficiency compared to the uncoated piston fueled with a 20% blend and sole gasoline, respectively. The hydrocarbon and carbon monoxide emissions of the engine with a coated piston fueled by the 20% lemon peel oil blend were reduced by 12.7% and 12%, respectively, as compared to gasoline operation in the engine with an uncoated piston.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".