Thermodynamic and Thermo-Econmic Analysis of Preheated and Blended Castor Oil Methyl Ester in a Compression Ignition Engine
Bibliographic record
Abstract
In this paper, energy, exergy, suitability and economic evaluation of a diesel engine running with diesel fuel and five different types of preheated biodiesel blends were evaluated experimentally. The experiments were carried out at varying engine brake mean effective pressures (bmeps). The energy and exergy rate components of the engine were callcualted and compared for each operating conditions and blends of fuel. The fuel properties of the castor oil methyl ester (COME) at different preheating temeperatures have been tested with a consideration of different biodiesel international standards. The test results shows that the fuel properties of COME improve with increase of fuel inlet temeperatures. At 114°C, kinematic viscosity and density decreased to (5.74 mm2/s and 862 kg/m3), whcich is close to diesel fuel, and the brake specific fuel consumption (BSFC) and brake thermal efficiency (BTHE) was improved by 33.1% and 49.6% compared to the fuel preheated temeperature of 42°C. The input fuel energy and exergy rates of blends of fuel were seen to be improved than diesel fuel. The maximum energetic and exergetic efficiency for blended fuels in the test engine at 372 bmep were found in the range of 25−28 % and 23-26%, respectively. The blends of fuel are marginally less sustainable than diesel fuel at every bmeps. The cost analyses show that, all blends of fuel offer quite higher economic cost with respect to diesel fuel. The full economic analysis reveals that only up to 60% blends of fuel is more affordable as compared to diesel.
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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".