Settling of Asphaltene Aggregates in <i>n</i>‑Alkane Diluted Bitumen
Bibliographic record
Abstract
The settling rate of asphaltene aggregates\nis a key design parameter\nfor partial deasphalting processes, and yet few data and models are\navailable for these systems. The settling rates of asphaltene aggregates\nwere measured in two Western Canadian bitumens diluted with <i>n</i>-heptane or <i>n</i>-pentane at 21 °C and\natmospheric pressure. The density and viscosity of the mixtures, the\nsize distributions of the aggregates, and the fractal dimensions of\nthe aggregates were also measured. The asphaltene aggregates settled\nas a zone, that is, all aggregates settled at the same rate. The settling\nrates increased with increasing <i>n</i>-alkane content,\nreached a maximum at approximately 75 wt % <i>n</i>-alkane\nand then decreased at higher dilutions. The maximum settling rate\ncorresponded to the maximum aggregate diameter and fractal dimension.\nThe maximum settling rate in <i>n</i>-pentane diluted bitumen\nwas 2 orders of magnitude greater than in the same bitumen diluted\nwith <i>n</i>-heptane. The difference was attributed to\nthe lower density and viscosity of the medium, larger aggregates,\nand higher fractal dimensions in <i>n</i>-pentane versus <i>n</i>-heptane. The settling rates were modeled with Stokes’\nlaw modified to include the fractal dimension of the aggregates. Since\nzone settling was observed, the settling rate was determined from\na single average diameter applied to all of the aggregates. The Sauter\nmean diameter was found to provide the most consistent results for\nthe diluted bitumen systems in this study. No other form of hindering\nwas required to match the data. The model matched the measured settling\nrates to within 20% of the maximum settling rate. The model is sensitive\nto the fractal dimension of the aggregates, and therefore precise\ndetermination of the fractal dimension is critical or it must be tuned\nto match the settling data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.053 | 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; both teacher heads agree on what is shown here.
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".