Short‐chain esters enriched biofuel obtained from vegetable oil using molecular distillation
Bibliographic record
Abstract
Abstract Aviation fuels used in gas‐turbine engine powered aircraft are mainly obtained from the distillation of mineral oil. These jet fuel molecules present carbon chain length of C8 to C16 in the same range of fossil kerosene and have high calorific values and a great cold behaviour. With the increase in consumption of jet fuels, it has become extremely important to develop alternative fuels with adequate properties that could be capable of fulfilling the aviation industry requirements. In this context, aviation alternative fuel originated from sustainable raw materials must meet a set of safety requirements and should exhibit similar physicochemical properties to mineral kerosene. In this study the production of a short‐chain esters enriched biofuel using molecular distillation of FAME obtained from babassu oil was evaluated. Operational conditions were assessed to obtain high mass yields and high ester content in the carbon chain length range of kerosene. A fuel with properties close to those of aviation biofuels was obtained at 140 °C. At this temperature, more than 80 % of the esters in the product composition were within the desired range and there was a mass recovery higher than 88 %. In addition, the short‐chain esters enriched biofuel was blended with fossil kerosene at different concentrations and its properties were analyzed in order to study the effects of the gradual addition of this biofuel stream to commercial aviation kerosene. Density, heating value, freezing temperature, and pour point were evaluated. A mixture up to 6.0 % g/g accomplished the specification limits established by ASTM D1655.
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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".