Fouling propensity, compatibility and stability of diesel/biofuel blends
Bibliographic record
Abstract
Varying levels of carburization (fouling) were observed when vaporizing ultra-low sulphur diesel fuels and biofuel blends for use in a Homogeneous Charge Compression Ignition (HCCI) engine to study fuel performance and characteristics in a well controlled research test cell. The fouling propensity of fuels and fuel blends, which is directly related to compatibility and thermal stability characteristics, was investigated to understand the chemistry involved in foulant precursor formation The base fuel was a commercial ULSD diesel fuel. The two blending stocks were a fatty acid methyl ester (FAME) biodiesel derived from canola oil and a renewable diesel blending component (biodiesel-B) obtained by hydrotreating vegetable oil. Compatibility tests indicated that petroleum ULSD and specific biofuels are compatible with each other at any blending ratio. Fouling tests suggested that, for all the diesel blends, the fouling propensity was very low level. Thermal stability tests-fuel thermal oxidation test (JFTOT), breakpoint temperature, oxidation stability, induction time, and peroxide number-indicated that the renewable diesel and biodiesel blends with ULSD have good thermal stability. However, stability consequences of different fuel samples can be described as: ULSD > Biodiesel-B B5 > Biodiesel-B B20 > FAME B5 > FAME B20. The fouling observed in HCCI engine operation could be caused by the heating configuration used in engine design combined with temperature and oxygen levels. The hydrodynamic conditions, mass and heat transfer could lead to fouling and need further research.
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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 | 0.001 | 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".