Density and Viscosity of Multicomponent Solvent (<i>n</i>-Pentane + <i>n</i>-Hexane + <i>n</i>-Heptane + <i>cyclo</i>-Hexane + Toluene) and Bitumen Mixtures─Implications for In Situ Bitumen Recovery and Transportation of Diluted Bitumen
Bibliographic record
Abstract
Conventional techniques for extracting and transporting bitumen, such as steam-assisted gravity drainage (SAGD), are associated with significant environmental drawbacks and contribute to greenhouse gas emissions. Embracing alternative processes could significantly mitigate these environmental impacts significantly. Furthermore, developing more energy-efficient extraction and transportation methods holds the potential to reduce energy consumption and associated carbon emissions, contributing to a more sustainable energy sector. This study introduces novel measurements pertaining to the thermophysical properties of a multicomponent synthetic solvent ( n -pentane + n -hexane + n -heptane + cyclo -hexane + toluene) and bitumen mixtures within a pressure range extending up to 8.769 MPa and temperatures reaching up to 389 K. This information is of utmost significance in the design, optimization, and simulation of in situ recovery methods. Moreover, the measured properties are crucial in the design of surface processing and transport of diluted bitumen. Empirical relationships are established to determine the thermophysical properties of the studied multicomponent systems. These correlations, yielding average absolute relative deviations (AARD) of 0.36% for density and 8.81% for viscosity, offer simple tools for estimation of these properties in multicomponent systems composed of bitumen and synthetic diluent. Our results illustrate that the utilization of a synthetic diluent for viscosity reduction leads to a corresponding reduction in greenhouse gas emissions and results in significant energy savings. The results hold relevance across a spectrum of applications, spanning solvent-based bitumen recovery processes, surface treatments, and the efficient transportation of bitumen via pipelines.
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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 | 0.000 |
| 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) | 0.002 | 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".