Sooting Tendency of a Series of Kerosene Surrogates and Blends Containing Various Additives Considered as Sustainable Alternative Fuels
Bibliographic record
Abstract
The present work aims at analyzing the sooting tendency of 32 additives considered as potential alternative transportation fuels including 4 C2–C5 alcohols, 4 C7–C10 esters, 16 C4–C7 furans, 6 C10 terpenes, and 2 pyrolysis oils (POs), some of which have seldom (if ever) been studied in the literature. The sooting propensity of each additive, following its mixture with a kerosene surrogate, was characterized using the oxygen extended sooting index (OESI), which is based on the measurement of the smoke point (SP), before being converted into unified index (UI) values for modeling purposes. To that end, a benchmarking analysis of the SP measurement approaches commonly used in the literature was first conducted. The SP of a series of reference fuels from the ASTM D1322 standard were measured using different methods to compare their precision, repeatability, and ease of use. This led to identifying the so-called vision-based algorithm-aided procedure as being the best suited. This method was then selected to test the ability of 8 model fuels to emulate the sooting propensity of a commercial Jet-A. Based on the results obtained, an n-dodecane/isocetane/mesitylene/n-propylbenzene blend was chosen to be mixed with up to 40 vol % of additives. The measured UI showed that each tested fuel, except for one terpene (myrcene), soots less than the kerosene surrogate. Their soot-suppressing effect was found to decrease in the following order: alcohols > esters > furans > terpenes. Measured data, moreover, allowed extending the predictive capability of a group contribution model (GCM) recently developed through the proposal of dedicated submodels integrating 15 sooting propensity factors suitable for predicting the tendency to soot of furans and terpenes. While satisfactorily simulating measured data, the GCM proved to be valuable for identifying the chemical structures influencing soot production. Finally, although POs exhibit low UI, their high water content, among other things, removes them from consideration as attractive additives.
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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 | 0.001 |
| 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".