Experimentally Validated Model for the Determination of Concentration-Dependent Diffusion of a Light Hydrocarbon in Bitumen
Bibliographic record
Abstract
A simple experiment of transient, one-dimensional diffusion of pure butane vapor into bitumen was designed that permitted direct measurement of bitumen swelling and butane uptake with time. The experimental results showed significant swelling as a result of butane uptake by the viscous liquid. A mathematical model that rigorously accounts for the dependence of both density and mutual diffusion coefficient on liquid composition was developed and used to interpret the experimental data. Given the dependence of mixture density on composition, the concentration dependence of the diffusion coefficient of butane in bitumen was determined using least-squares nonlinear regression. It was found that the dependence of diffusion coefficient on concentration cannot be ascertained from butane uptake or bitumen swelling data alone. In isolation, either set of experimental data (bitumen swelling or butane uptake) can be fitted equally well with rather different functions describing the dependence of diffusion coefficient on composition. Validation against experimental data not used to fit model parameters, however, suffices for discriminating between candidate concentration-dependent diffusivities. These findings improve our ability to accurately measure the diffusivity of light n -alkanes in bitumen, information that is relevant to the understanding of in situ heavy oil recovery using hydrocarbon solvents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".