Bioprocessing-Based Approach for Bitumen/Water/Fines Separation and Hydrocarbon Recovery from Oil Sands Tailings
Bibliographic record
Abstract
Oil sands are a major source of oil, but their industrial processing generates tailings ponds that are an environmental hazard. The main concerns are mature fine tailings (MFT) composed of residual hydrocarbons, water, and fine clay. Tailings ponds include toxic contaminants such as heavy metals, and toxic organics including naphthenics. Naphthenic acids and polyaromatic hydrocarbons (PAHs) degrade very slowly and pose a long-term threat to surface and groundwater, as they can be transported in the MFT. Research into improved technologies that would enable densification and settling of the suspended particles is ongoing. In batch tests, BioTiger™, a microbial consortium that can metabolize PAHs, demonstrated improved oil sands tailings settling from a Canadian tailings pond. Results also showed, depending on the timing of the measurements, lower suspended solids and turbidity. Elevated total organic carbon was observed in the first 48 hours in the BioTiger™-treated columns and then decreased in overlying water. Oil sands tailings mixed with BioTiger™ showed a two-fold reduction in suspended solids within 24 hours as compared to abiotic controls. The tailings treated with BioTiger™ increased in microbial densities three orders of magnitude from 8.5 × 105 CFU/mL to 1.2 × 108 CFU/mL without any other carbon or energy source added, indicating metabolism of hydrocarbons and other available nutrients. Results demonstrated that bioaugmentation of BioTiger™ increased separation of organic carbon from particles in oil sands and enhanced settling with tailings with improved water quality. Journal style is for Abstract to be less than 200 words, and contain no citations to other sources; please edit as needed
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".