Characterization and Dynamics of Residual Organics in Oil Sands Fluid Fine Tailings
Bibliographic record
Abstract
Fluid fine tailings (FFTs) generated by the surface mining of oil sand ore bodies are high-water-content, clay-rich materials that contain unrecovered bitumen and additional residual organics, including solvents used during bitumen extraction. Despite a large accumulated inventory of these tailings and despite the environmental implications and challenges that the presence of organics poses, little is known about the differences in the residual organics generated by different bitumen extraction processes and about the effects of polymeric flocculation on mineral–organic interactions. This study investigated residual organics in two compositionally different FFTs both before and after flocculation with a commercially available polyacrylamide polymer. Thermogravimetric (TG) analysis, Fourier transform infrared (FTIR) spectroscopy, and evolved gas analysis (EGA-TG-FTIR) were used to characterize the makeup of total organics, free bitumen, and mineral-associated organics. These observations highlighted distinct differences between the FFTs. Relative to the sample receiving waste only from the bitumen extraction process (FFT-1), the FFT sample that received waste from both the extraction and the froth treatment processes (FFT-2) exhibited a higher residual organic content and a larger fraction of lower-molecular-mass organic substances and showed a proportionally smaller release of free bitumen as a result of the washing process, evidence of a higher fraction of mineral-associated organics. Moreover, TG and FTIR analyses showed that the free bitumen released from both FFTs contained a higher fraction of lighter organics and was characterized by a more ordered molecular structure compared to the remaining organics. Finally, the study also found that the bitumen release characteristics of FFT-1 were fundamentally different from those of FFT-2. Bitumen release continued to be observed from both FFTs after polymeric flocculation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".