Improved Density Prediction for Mixtures of Native and Refined Heavy Oil with Solvents
Bibliographic record
Abstract
A correlation was developed to predict the density of mixtures of heavy oil (and other petroleum liquids) and hydrocarbon solvents when the densities of each fluid in the mixture are available. Densities at atmospheric pressure and 293 K were measured for saturates and aromatics (SA) fractions from 10 native, thermo-cracked, and hydrocracked heavy oils all mixed with toluene and n -heptane; distillation cuts from 6 heavy oils mixed with toluene; and mixtures of deasphalted heavy oils with naphtha, diesel, and condensate. Density of mixtures of hydrocarbons and solvents at higher pressures (0.1–10 MPa) and temperatures (298–353 K) were also measured or obtained from the literature. Symmetry versus mass fraction was observed for all of the mixtures, and their densities were fitted with a mixing rule in which excess volumes are quantified with a binary interaction parameter and the density of each mixture component. The excess volume mixing rule fit the data for each mixture with average absolute deviations (AAD) less than 1.1 kg/m 3, and the overall average AAD was 0.39 kg/m 3 . The binary interaction parameter was correlated to the density of the components in the mixture and to temperature. Pressure was found to have no consistent effect in the interaction parameter and was neglected. The overall AAD for the density determined with the correlated β 12 for binary mixtures was 1.1 kg/m 3 compared with 3.0 kg/m 3 if regular solution behavior was assumed and 3.6 kg/m 3 when the standard American Petroleum Institute (API) correlation was used to predict the density of the mixtures. The API correlation and correlated excess volume mixing rule performed similarly for hydrocarbons with carbon numbers above five. The proposed correlation was also tested on ternary data from the literature with comparable results.
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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 | 0.000 |
| 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) | 0.000 | 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".