Viscosity Mixing Rules for Bitumen at 1–10 wt % Solvent Dilution When Only Viscosity and Density Are Known
Bibliographic record
Abstract
Viscosity is an important parameter to assess heavy oil and bitumen upgrading operations, such as bitumen dilution to meet pipeline viscosity specification limits. In conceptual design studies that involve blending of high- and low-viscosity materials, the experimental measurement of viscosity is impractical; therefore, such studies employ mixing rules to estimate mixture viscosity. Mixing rules for conceptual design evaluations where limited or no information apart from the viscosity and density of the bitumen and solvents is available were of interest. This study determined which viscosity mixing rules could be used with such limited input, what the predictive errors were, whether the performance of those mixing rules were measurably affected by changes in chemical composition, and if mixing rules could be applied at the <10 wt % solvent concentration in bitumen. Binary mixtures of 1–10 wt % of six different solvents (1-methylnaphthalene, decahydronaphthalene, 1,2,3,4-tetrahydronaphthalene, butylcyclohexane, butylbenzene, and n- decane) and Athabasca bitumen were prepared, and their viscosity, density, and refractive index were measured at 303, 313, and 333 K (30, 40, and 60 °C) and atmospheric pressure. The performance of mixing rules in predicting these properties for the binary mixtures at low solvent concentration was evaluated. It was found that the simple mixing rule, log [log ( ν m + 0.7)] = Σ w i log [log ( ν i + 0.7)], and that by Miadonye et al. ( Petrol. Sci. Technol. 2000, 18, 1–14) consistently gave the better viscosity estimation with an absolute average relative deviation (AARD) of around 30%. Within this uncertainty, there was no evidence indicating that the mixing rules could not be used for viscosity prediction of bitumen–solvent mixtures at low dilution levels. The mixing rules appeared not to be affected by the chemical nature of the solvent, and if it had an effect, it was of the same order or less than the uncertainty of viscosity prediction.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".