Estimation of Diffusion Coefficients in Liquid Solvent–Bitumen Systems
Bibliographic record
Abstract
Solvent-based recovery processes have gained some advantage over thermal recovery processes under specific reservoir conditions. Solvent–bitumen mass transfer is a diffusion dominated process. The calculation of diffusion coefficients is fundamental for the design of solvent-based recovery processes as it allows us to understand the performance of the process. In a recent work, Babak et al. (1) developed a new slopes and intercept method which was demonstrated to be very robust for the calculation of diffusion coefficients of binary systems. In this paper, the diffusion coefficients of dimethyl ether (DME), pentane, and toluene into two different bitumen systems are calculated using this method as a function of concentration. X-ray computed tomography (CT) is used to obtain the concentration profiles of the bitumen–solvent systems. Furthermore, a new modification of Vignes’ equation is introduced to predict concentration-dependent diffusion coefficients. This new modification includes the acceleration and deceleration parameter associated with the solvent and bitumen mass transfer behavior. The developed equation successfully predicts the diffusion coefficients of the studied systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".