A Review on Performance and Emissions of Compression Ignition Engine Fueled with Ethanol-diesel Blend
Bibliographic record
Abstract
CI engine is an important and widely used engine in the power generation industries generally fuelled by diesel.The use of CI engine vehicles is increasing continuously day by day which in turns diesel fuel consumption and engine emissions increases.Limitation of these conventional fuel as well as continuously increasing global warming by engine emissions has motivated to researchers in the field of alternative renewable fuel source like biofuel such as biodiesel, methanol, ethanol etc. Ethanol is the most commonly researched alcoholic fuel as alternative fuel.The objective of this study is to investigate the effect of ethanol-diesel fuel blend on engine performance such as BSFC, BTE, brake power, brake torque as well as engine emission parameters such as NOx, CO, HC, exhaust gas temperature etc. of a CI engine.Through this study it was found that ethanol significantly reduces HC, PM, NOx and exhaust gas temperature but slightly increases fuel consumption.Also, ethanol additive enhanced engine performance like BTE, brake power and brake torque.The outcomes of this study may help for researchers to understand the effect of ethanol addition on engine performances and emissions at different ethanol-diesel fuel blend concentration for CI engine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".