Effects of size and composition of bitumen drops on intra-oil diffusion and dissolution of hydrocarbon solvents in froth treatment tailings ponds
Bibliographic record
Abstract
The rate of mass transfer of lower molecular weight hydrocarbons (naphtha) from bitumen drops in mature fine tailings of oil sand tailings ponds (OSTPs) may control their bioavailability and the associated rate of GHG production. Experiments were conducted using bitumen drops spiked with o-xylene and 1-methylnaphthalene to determine the mass transfer rate of these naphtha components from bitumen drops. The results were compared to simulations using a multi-component numerical model that accounted for transport in the drop and across the oil-water interface. The results demonstrate rate-limited mass transfer, with aqueous concentrations after 60 days of dissolution that were different than those in equilibrium with the initial drop composition (less for o-xylene and greater for 1-methylnaphthalene). The simulations suggest that mole fractions were unchanged at the center of the drop, resulting in concentration gradients out to the oil-water interface. Numerical simulations conducted using different drop sizes and bitumen viscosities also suggest the potential for persistent naphtha dissolution, where the time required to deplete 80% of the o-xylene and 1-methylnaphthalene mass from an oil drop was estimated to be on the order of months to years for mm-sized drops, and years to decades for cm-sized drops assuming instantaneous biodegradation in the aqueous phase surrounding the bitumen.
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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) | 0.000 | 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) | 0.001 | 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".