Solvent screening for non‐aqueous extraction of Alberta oil sands
Bibliographic record
Abstract
Abstract Non‐aqueous extraction of bitumen from oil sands has the potential to reduce fresh water demand of the extraction process and eliminate tailings ponds. In this study, different light hydrocarbon solvents, including aromatics, cycloalkanes, biologically derived solvents and mixtures of solvents were compared for extraction of bitumen from Alberta oil sands at room temperature and ambient pressure. The solvents are compared based on bitumen recovery, the amount of residual solvent in the extracted oil sands tailings and the content of fine solids in the extracted bitumen. The extraction experiments were carried out in a multistage process with agitation in rotary mixers and vibration sieving. The oil sands tailings were dried under ambient conditions, and their residual solvent contents were measured by a purge and trap system followed by gas chromatography. The elemental compositions of the extraction tailings were measured to calculate bitumen recovery. Supernatants from the extraction tests were centrifuged to separate and measure the contents of fine solid particles. Except for limonene and isoprene, the tested solvents showed good bitumen recoveries of around 95%. The solvent drying rates and residual solvent contents in the extracted oil sands tailings correlated to solvent vapour pressure. The contents of fine solids in the extracted bitumen (supernatant) were below 2.9% for all solvents except n‐heptane‐rich ones. Based on these findings, cyclohexane is the best candidate solvent for bitumen extraction, with 94.4% bitumen recovery, 5 mg of residual solvent per kilogram of extraction tailings and 1.4 wt% fine solids in the recovered bitumen. © 2012 Canadian Society for Chemical Engineering
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 | 0.001 | 0.001 |
| 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".