Upgrading Bio-oil through Emulsification with Biodiesel: Mixture Production
Bibliographic record
Abstract
There has been increasing interest in alternative fuels made from biomass, which is abundant and renewable. Bio-oil produced by fast pyrolysis of biomass is highly viscous and acidic and has a high water content. To overcome these problems as a fuel, a method of emulsifying bio-oil with biodiesel has been investigated. In the present study, various effects on the mixture stability have been examined. The optimal conditions for obtaining a stable mixture between bio-oil and biodiesel are with an octanol surfactant dosage of 4% by volume, initial bio-oil/biodiesel ratio of 4:6 by volume, stirring intensity of 1200 rpm, mixing time of 15 min, and emulsifying temperature at 30 °C. Furthermore, selected fuel properties, such as viscosity, density, water content, acid number, and average molecular weight, are measured for characterizing the bio-oil/biodiesel mixture. Thermogravimetric analysis (TGA) has been used to further evaluate the thermal properties. Data from the TGA and Fourier transform infrared (FTIR) analyses confirm the presence or absence of a certain group of chemical compounds in the mixture. The kinetic parameters for the thermal decomposition of the bio-oil, bio-oil/biodiesel-rich phase, and pyrolytic lignin-rich phase were obtained from the TGA experiments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".