Impact of Liquid–Vapor to Liquid–Liquid–Vapor Phase Transitions on Asphaltene-Rich Nanoaggregate Behavior in Athabasca Vacuum Residue + Pentane Mixtures
Bibliographic record
Abstract
The bulk phase behavior of heavy oil + alkane mixtures and the behavior of the asphaltenes that they contain are topics of importance for the design and optimization of processes for petroleum production, transport, and refining and for performing routine saturates, aromatics, resins, and asphaltenes (SARA) analyses. In prior studies, partial phase diagrams and phase behavior models for Athabasca vacuum residue (AVR) comprising 32 wt % pentane asphaltenes + n -alkanes were reported. For mixtures with pentane, observed phase behaviors included single-phase liquid as well as liquid–liquid, liquid–liquid–vapor, and liquid–liquid–liquid–vapor regions. Dispersed solids were detected under some conditions as well but not quantified. In this work, small-angle X-ray scattering (SAXS) is used to study nanostructured materials in liquid phases present in AVR + n -pentane mixtures from 50 to 170 °C at mixture bubble pressure. The investigation focuses on the impact of the transition from a single AVR-rich liquid to co-existing pentane-rich and AVR-rich liquids on the nanostructure and the nanostructures most resistant to aggregation as the pentane composition axis is approached. Background scattering subtraction was performed using global mixture composition. The robustness of this assumption with respect to values obtained for coefficients appearing in a two level Beaucage unified equation fit is demonstrated. The nanostructured material is shown to arise at two length scales from 1 to 100 wt % AVR. Smaller nanostructures possess mean radii less than 50 Å, while the larger nanostructures possess mean radii greater than 250 Å. The addition of pentane to the AVR causes an increasingly large fraction of the large and small nanostructures to grow in size. Only nanostructures resistant to aggregation remain in the pentane-rich phase as the 0 wt % AVR axis is approached. Step changes in aggregation identified from changes in average radius of gyration, scattering coefficients, and surface/volume ratios arise at the liquid–vapor to liquid–liquid–vapor transition (∼40 wt % pentane) and within the liquid–liquid–vapor region (∼90 wt % pentane). The temperature is shown to have a limited effect on the mean size and structure of the nanostructured material present, irrespective of global composition.
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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 | 0.000 | 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".