Application of Microbial Culture and Rhamnolipid for Improving the Sedimentation of Oil Sand Tailings
Bibliographic record
Abstract
Densification of oil sand tailings deposited in the tailing ponds and recovering water from them are two major challenges in the oil sands surface mining industry. A small increase in the tailings settlement rate (which normally is very slow) can improve the densification of tailings and significantly reduce water consumption and the volume of the tailing ponds. In this work, the objective was to evaluate the role of a mixed culture of two microbial strains isolated from weathered oil and rhamnolipid (JBR 425) together with these strains in the sedimentation of fine tailing particles. It has been found that a mixed culture of two microbial strains isolated from weathered oil increased the sedimentation. Rhamnolipid (0.5%) together with these two microbial strains at 15°C ± 2°C showed significant increases in sedimentation (by a factor of 5.1), the concentration of larger particles (by a factor of 2.63), the particle mean diameter (by a factor of 2.70) and flocculation in the tailings samples compared to the control while the zeta potential is still negative. This means that the mechanism of flocculation is probably due to increasing the hydrophobicity of the particles, interaction of biosurfactant and high molecular weight microbial organic compounds through a bridging mechanism with clay particles. This work shows the potential of using rhamnolipid and microbial culture in order to increase the oil sand sedimentation through flocculation and microbial activity in a more environmentally friendly densification process.
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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 | 0.001 | 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 | 0.000 |
| 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".