Dynamic accelerated solvent extraction for faster oil sands oil, water, and solids determination and solids cleaning
Bibliographic record
Abstract
Abstract Determining the oil, water, and solids (OWS) contents of oil sand is essential to many aspects of the oil sands industry including mine planning, optimization of extraction processes, and tailings management. Beyond OWS, the characterization of the mineral solids associated with oil sands can provide critical supplementary information for oil sands operations. The industry standard, Soxhlet–Dean and Stark extraction method, has been commonly used for oil sands OWS determination and solids cleaning. This method is reliable and robust, but it requires long extraction times, high solvent consumption, and extensive glassware handling and ergonomic issues. Similarly, other traditional solids cleaning methods like cold solvent washing are labour, time, and solvent intensive. In this study, dynamic accelerated solvent extraction (dASE) is evaluated as a potential faster alternative technique for OWS characterization and solids cleaning. The dynamic extraction method enhances bitumen extraction efficiency by the constant refreshing of solvent under elevated pressure and temperature during the extraction process. The fully automated nature of the dASE system significantly reduces sample extraction time as well as technologist contact time and solvent exposure. The dASE method parameters were systematically optimized for both fast solvent cleaning of solids and full OWS determination using oil sand samples from various Syncrude mine locations with a wide range of OWS contents. Extraction times were found to range from 5 to 60 min depending on the composition of the oil sand. Paired sample testing showed that the optimized dASE method provided equivalent bitumen and solid contents to Dean and Stark extractions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".