Engine Performance and Emission Characteristics of a Direct Injection Diesel Engine Fuelled with 1- Hexanol as a Fuel Additive in Mahua Seed Oil Biodiesel Blends
Bibliographic record
Abstract
The increasing industrialization and motorization of the world has led to a steep rise for the demand of petroleum products. Petroleum based fuels are obtained from limited reserves. In the wake of this situation, there is an urgent need to promote use of alternative fuel which must be technically feasible, economically competitive, environmentally acceptable and readily available. In the present study, Mahua seed oil methyl esters (MSOME) were prepared through transesterification and evaluation of important physico-chemical properties was carried and the properties were found within acceptable limits. A compression ignition engine was fuelled with three blends of MSOME with diesel (10, 20 and 30% on volume basis) and various performance and emission characteristics were evaluated and results compared with baseline data of diesel. The results suggest the BTE was higher for MSOME blends and BSFC, HC and smoke opacity were lower as compared to diesel fuel. This may be attributed to improved combustion for MSOME are oxygenated fuels and have higher cetane number. The values of NOx were found almost nearer for all blends as compared to diesel. Addition of 1-hexanol (Ignition improver) 0.5%, 1% volume ratios to the optimum blend (MSOME30) for evaluating the engine performance and emissions parameters and the main purpose of ignition improver is to improve combustion process and reduction in engine emissions. Finally results shows that performance and emissions have been to justify the potentiality of the mahua seed oil methyl esters as alternative fuel for compression ignition engines without any modifications
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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".