Predicting the Viscosity of Hydrocarbon Mixtures and Diluted Heavy Oils Using the Expanded Fluid Model
Bibliographic record
Abstract
The expanded fluid (EF) viscosity model was recently developed for petroleum applications. While petroleum fluids can be modeled as a single-component fluid, process calculations often require that the fluid be treated as a mixture of pseudo-components. For mixtures, the EF model mixing rules require binary interaction parameters, α ij, between each component pair in the mixture, which are fitted to experimental data. In this study, a generalized correlation is developed for these interaction parameters as a function of the specific gravity and hydrogen/carbon ratio of the mixture components. The proposed correlation was developed on the basis of a data set that included viscosity and density literature data for pure hydrocarbon pairs at room temperature and atmospheric pressure and new data for pseudo-pairs of heavy oil/solvent at temperatures from 21 to 175 °C, pressures up to 10 MPa, and solvent contents up to 40 wt %. The correlation was assessed on a distinct test data set that included viscosity data for pure hydrocarbon mixtures from the literature, new data for deasphalted crude oils diluted with paraffinic and aromatic solvents, and new data for crude oils diluted with similar solvents. The viscosities of the development and test data sets were predicted with an overall average absolute relative deviation (AARD) of 13 and 10%, respectively, compared to 36 and 50%, respectively, when ideal mixing, α ij = 0, was assumed. Finally, the correlation was tested on an independent data set from the literature, including viscosity data for crude oils diluted with a variety of solvents. The viscosities of the independent data set were predicted with an overall AARD of 14%, as compared to 30% when ideal mixing, α ij = 0, was assumed. The deviations obtained with the correlated α ij are almost as low as those obtained when fitting the data by adjusting α ij .
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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".