Prediction of the Distillate and SARA Content of Visbroken Bitumen, Vacuum Bottoms, and Deasphalted Oil
Bibliographic record
Abstract
Methods to predict the final properties of visbroken bitumen and bitumen fractions are useful for the design of upgrading processes. Previously, correlations were proposed for the density, viscosity, and stability of visbroken products as a function of the measured feed properties, conversion, and product composition. The composition was defined in terms of the distillate content ( D ) and the saturate, aromatic, resin, and pentane-insoluble asphaltene contents of the distillation residue (SARA). However, for design purposes, it is necessary to predict rather than measure the product composition. The objective of this study was to develop correlations for the product distillates, saturates, aromatics, resins, and asphaltenes (DSARA) composition. To do so, seven different feeds including three Western Canadian bitumens, two partially deasphalted bitumens, a fully deasphalted bitumen, and a vacuum residue fraction were thermally cracked in two different continuous visbreakers at constant pressure over a range of conversions. The simulated distillation assay, gas yield, pentane-insoluble asphaltene content, toluene-insoluble (TI) content, distillate content, and residue SARA contents of each feed and product were measured. Conversions were determined from the SimDist assays and ranged from 5 to 46%. First, correlations were developed for the product DSARA contents as functions of the measured conversion and feed composition. The average absolute deviation (AAD) for the correlated composition was within the experimental error (±1.0 to ±3.0 wt % depending on the component). Next, the correlations were applied with a conversion determined from the reactor temperature profile and space time by using a previously developed method. Finally, the correlations were applied with feed DSARA content determined from readily available feed oil measurements (SimDist assay, oil density, and asphaltene content) using a previously developed method. In all cases, the AAD remained within experimental error.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".