Selective Rejection of Inorganic Fine Solids, Heavy Metals, and Sulfur from Heavy Oils/Bitumen Using Alkane Solvents
Bibliographic record
Abstract
Bitumen and heavy oil + alkane solvents exhibit complex phase behaviors. For example, organic components of these materials can be solubilized and inorganic solids dispersed into a high-pressure gas phase. Conversely, they can also be partitioned into as many as three bulk phases: a gas phase, a liquid phase that is largely free of inorganic solids, and a phase comprising essentially all of the inorganic solids and a small fraction of the organic material. The outcome depends on the temperature, pressure, and solvent-to-feed ratio. Mass balance results for a screening survey for Athabasca vacuum bottoms (ABVB) + alkane solvents (pentane, heptane, decane, and dodecane) are reported along with a limited number of phase composition data for ABVB + pentane and dodecane mixtures. Reversible phase behavior and irreversible thermolysis conditions were considered. The key findings are that inorganic solids are readily partitioned from ABVB irrespective of the solvent and operating conditions employed, while key heavy metals, such as vanadium, require a combination of phase behavior and mild thermolysis. Sulfur- and nitrogen-containing species possess low rejection selectivities in this solvent series.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".