An Experimental Study on performance and emission characteristics of multi cylinder CRDI diesel engine fueled with Ethanol, Acid oil based Biodiesel and Diesel blends
Bibliographic record
Abstract
This experimental investigation discusses the use of biodiesel as an additive for diesel and ethanol (diesohol) mixtures for diesel engine applications. The biodiesel was produced from Acid oil, a by-product of vegetable oil processing plant to obtain its ester called as acid oil methyl ester (AOME) and was consequently blended with diesel and ethanol respectively using 50% by volume of biodiesel and by varying 4%, 8%, 12% and 16% by volume of ethanol with 46%, 42%, 38% and 34% by volume of diesel respectively. The physical and chemical properties of these fuels and their blends were obtained to determine their applicability for the common rail direct injection (CRDI) engine. The foremost objective of current effort is the testing of compression ignition engine with increased proportion of biodiesel in diesel-ethanol blends and comparing the performance with diesel engine operation. The results showed that the fuel properties of the blends satisfy the requirement of regular diesel engine, although tendency of more carbon residue is observed with increased fraction of biodiesel. The blends were then tested in a four cylinder CRDI engine for different loads at different engine speeds of 1200, 1500 and1800rpm respectively. Performance, emission characteristics of the engine fuelled with the selected blends were then compared with standard diesel fuel operation. The Investigations revealed, the decrease in brake thermal efficiency, increase in brake specific fuel consumption, increased carbon monoxide and hydrocarbon emissions with increase in alcohol concentration in the blends, whereas NOx emissions were reduced. Further with increase in speed from 1200 to 1800 rpm, brake thermal efficiency increases but carbon monoxide and hydrocarbon emissions decreased while NOx emissions were increased significantly. Beyond 1800 rpm tendency of Knocking was observed for engine operation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".