An optimized extraction and gas chromatography analysis method for the quantification of diluent hydrocarbons in froth treatment tailings
Bibliographic record
Abstract
Froth treatment tailings are one type of waste stream generated during the extraction of surface-mined oil sands bitumen. To remove water and solids from bitumen froth recovered during the water-based extraction process, hydrocarbon diluent is added, and settling and/or centrifugation are applied to the diluted bitumen froth, producing diluted bitumen and froth treatment tailings. While recovery processes are in place to remove and recycle the diluent from froth treatment tailings, some residual diluent can remain. Since tailings are stored in outdoor ponds, the residual diluent can have implications for methanogenic microbial processes and resulting greenhouse gas emissions. This work presents a methodology to accurately extract and quantify diluent hydrocarbons from froth treatment tailings using gas chromatography. A cold-start temperature program is used to separate diluent hydrocarbons from any residual bitumen in the sample, and diluent is quantified using commercial standards as well as unprocessed diluent. A series of extraction parameters were tested and results from multiple conditions are shown with a rationale for the selected optimized parameters. Quantification of diluent in tailings samples is demonstrated from 60 to 5329 μg/g, and results from quality control standards show an average diluent recovery of 100 ± 10%.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".