Viscosities and densities of systems containing fatty compounds and alcoholic solvents
Bibliographic record
Abstract
Abstract This work presents viscosities and densities of systems composed of vegetable oils (or animal fat), commercial fatty acids, ethanol, and water at several temperatures. These systems are of great importance for oil/fat deacidification by liquid‐liquid extraction (LLE) using aqueous ethanol as solvent, as well as for ethylic biodiesel production. Experimental data and accurate predictive models of physical properties, such as viscosity and density, are essential for process designers mainly because they affect fluid dynamics, coefficients of heat and mass transfer, and consequently column efficiency. Experimental data for viscosities were also estimated using well‐known methods, such as Kay's mixing rules (simple and modified form) and GC‐UNIMOD. Despite the non‐ideality inherent to the mixtures studied in this work, approximately 71 % of the systems showed an acceptable estimation of their viscosities using at least one of these three models, with average relative deviations (ARD) up to 10 %. A simple mixing rule, based on densities of pure compounds and their mass fractions in the mixture, was used for estimating densities with ARD values not higher than 0.54 %.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".