Effect of ore quality on non‐aqueous oil sand extraction performance
Bibliographic record
Abstract
Abstract Current commercial aqueous based extraction processes are energy and greenhouse gas (GHG) intensive and require large tailings ponds. Non‐aqueous extraction (NAE) of bitumen from mineable oil sands is an alternative that eliminates tailings ponds with potentially lower energy requirements and GHG emissions. The economics of the NAE process depend partly on the impact of ore quality on bitumen recovery and product quality (low water and solids content). It has been claimed that NAE performance is insensitive to the quality (bitumen content) of the oil sand ores. However, the available data are ambiguous because different extraction methods and solvents were used in different studies and, in many cases, a limited range of ore qualities was examined. In this study, bitumen was extracted from eight ores of different quality with cyclohexane using a multistage method equivalent to a countercurrent process with a solvent/ore ratio of 0.67 w/w. The bitumen recovery and the water and solids content of the product bitumen were determined for each ore. It was found that bitumen recovery correlated negatively to clay content of the ore. The loss of recovery was attributed to bitumen adsorption on clays. The product quality was insensitive to the ore quality and instead depended on the density of the fluid medium, as expected with a centrifuge‐based separation method. The recovery and product quality from the NAE method were similar to those from aqueous 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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".