High Temperature Density, Viscosity, and Interfacial Tension Measurements of Bitumen–Pentane–Biodiesel and Process Water Mixtures
Bibliographic record
Abstract
As an alternative to solvent addition to the steam-assisted gravity drainage process for bitumen recovery, coinjection of biodiesel with steam as a surfactant additive to reduce bitumen–water interfacial tension was considered. The density and viscosity of bitumen and bitumen–pentane mixtures up to 15 % pentane concentrations by mass were measured at a 1 MPa pressure and up to 448 K temperature. The interfacial tension between bitumen, bitumen–pentane mixtures up to 15 % pentane concentrations, bitumen–biodiesel mixtures up to 0.3 % and water, and process water was also measured at a 1 MPa pressure and up to 423 K temperature. Laboratory tests showed that the density of bitumen–pentane mixtures decreased linearly with an increase in pentane content, and their viscosity decreased exponentially with the increase in temperature. A decrease in bitumen viscosity with an increase in pentane content was dramatic at low temperatures and became less sensitive at temperatures above 373 K. Interfacial tension measurements suggest that asphaltic acids naturally occurring in bitumen act as surfactants. The decay in interfacial tension with time is attributed to the diffusion of surfactant species in a bitumen droplet. The increase in interfacial tension of bitumen–pentane mixtures and water with an increase in pentane content and temperature needs further attention because of its commercial application.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".