Avaliação das características de volatilidade de blendas diesel/biodiesel usando técnicas de análise térmica
Bibliographic record
Abstract
Since 2008, diesel sold in Brazil receives the mandatory addition of biodiesel (B100).Currently, the blending percentage to B100 in diesel is 10% (B10), but from June / 2019 this value will increase to 11% (B11) and from this date will increase by 1% each year until reaching B15 in 2023.The quality of the diesel sold in Brazil, because the increasing compulsory added B100 content, it has been a cause for concern.Because of its composition and chemical structure B100 is more prone to oxidation than the latter.The purpose of this work was to study the thermal behavior of diesel / biodiesel blends caused by the effect of increasing the B100 content to the mixture using thermal analysis.The blends prepared using soybean biodiesel and S10 diesel, by using the volumetric ratios of 10%, 15%, 20%, 25%, 30%, 35%, 40% and 50%, were analyzed in a simultaneous DSC-TGA mod.SDT-Q600 equipment, from TA Instruments.The experimental conditions used were: dynamic analysis from 25ºC to 600ºC, at a heating rate of 10 o C.min -1 , in N 2 .The results indicate that diesel / biodiesel blends volatilization characteristics depends on the content of biodiesel present in the mixture, which reflects in changes in the respective TG/DTG curves profile.It is also observed that the increase of B100 delays the stages mass losses of the blends to higher temperatures.Consequently, there is a displacement of the volatilization onset temperatures (T onset ) to a higher value. .The onset temperature value of diesel is 134 °C, while for the blend B25 and B50 is 140 °C and 164 °C, respectively.The mass loss initial and decomposition endset temperatures (T endset ) are not significantly altered by the presence of biodiesel.The values temperatures are, for the B50, 35 °C and 253 °C, that is, 2 °C and 4 °C higher of those of the S10 diesel.The interaction between biodiesel and diesel is not complete, because are observed two peaks in the DTG analysis.This behavior is the result of the difference between their physicochemical properties.The results show that biodiesel modifies the volatilization process of diesel / biodiesel blends, especially for volumetric contents above 20%, which can affect fuel performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".