Sorption equilibrium and kinetics for cyclohexane, toluene, and water on Athabasca oil sands solids
Bibliographic record
Abstract
ABSTRACT The sorption of solvents from the vapour phase to solid substrates is important in producing heavy oil and bitumen, particularly from the oil sands. We investigated the sorption and desorption equilibrium and kinetics of cyclohexane, toluene, and water on kaolinite, fine solids, and organic‐rich fine solids isolated from Athabasca oil sands over a wide range of solvent vapour concentrations using a gravimetric method. The isotherms for both adsorption and desorption of solvent vapour were determined, and the Brunauer‐Emmett‐Teller equation was used to fit the experimental data. The initial kinetic rate constants for the adsorption and desorption of solvent vapour were determined with the linear driving force model. The results were compared and discussed in terms of the type of solvent, the content of organic materials, and the surface area of the solid substrates. The results suggest that, in addition to the adsorption of solvent on mineral surfaces, the contribution from solvent partitioning in the organic materials and the porosity resulting from high amounts of organic materials are significant factors for solvent uptake and kinetic rate constants. In mixed solvent vapours, the competitive sorption of water and cyclohexane was observed in the solid substrates. However, the degree of competitiveness decreased with an increase in the content of organic materials in the substrates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".