Lubricity of renewable diesel fuel blends
Bibliographic record
Abstract
Renewable diesel is a hydrocarbon blending component option available to fuel suppliers to meet the current renewable fuel regulations in North America, while offering a potential pathway to lower carbon diesel fuels in the future. In this study, the lubricity of renewable diesel fuel blends additized with ester- and monoacid-type lubricity improver additives (LIAs) was investigated following the ASTM D6079-11 test method. Appropriate LIA treat rates were established for the base fuel, an ultra-low sulphur diesel fuel derived from oil sands sources, using the high-frequency reciprocating rig (HFRR). Then, 10, 20, 30 and 50% renewable diesel and winter-grade diesel/jet components were blended with the base fuel using the same lubricity additive treat rates required for the base fuel. The experimental results show that the test fuels with higher renewable content produced larger HFRR wear scar diameters (WSDs) for both lubricity additive types. The WSD increase was more significant for the renewable diesel fuel blends with the ester-based LIA. This may be related to the observation that the ester-based lubricity additive was not completely miscible with the base fuel or the renewable fuel blends. Significant fractions of the test fuels with higher levels of volatile, winter-grade diesel/jet components were found to evaporate during lubricity determinations, which effectively increased the LIA treat rates. The HFRR WSDs were larger when a covered 15 ml sample was used to measure the lubricity of the more volatile diesel fuels in place of the 2 ml sample specified in the ASTM test method. This suggests that a larger factor of safety should be used when employing ASTM D6079-11 test method to measure the lubricity of more volatile diesel fuels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".