Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C<sub>1</sub>–C<sub>5</sub>)/Bitumen Systems Using Genetic Programming
Bibliographic record
Abstract
Accurate modeling of thermophysical properties of solvent/bitumen mixtures is critical for proper design and implementation of thermal- and solvent-based bitumen recovery processes. In this study, three generalized correlations were developed for prediction of solubility, density, and viscosity of light hydrocarbon/bitumen mixtures. The generalized correlations were developed using symbolic regression based on genetic programming and employing a 10-year set of comprehensive phase behavior experimental studies conducted under the SHARP research program on solvent-aided thermal recovery of bitumen. The data set comprised Surmont, JACOS, Mackay River, and Cold Lake bitumen samples and five light hydrocarbon solvents including methane, ethane, propane, n-butane, and n-pentane. The developed correlations are valid for gaseous solvents. Finally, the developed correlations for solubility, density, and viscosity were validated against a large data set of experimental measurements collected from the literature. The validation demonstrates that the developed correlations are able to accurately predict the available experimental data of solubility, density, and viscosity reported in the literature.
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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".