Influence of coagulants and flocculants dosing sequence on the dewaterability of oil sands mature fine tailings by pressure filtration
Bibliographic record
Abstract
• Optimal oil sands tailings filtration depended on flocculant and coagulant dosing sequence. • Flocculant-coagulant sequence (F-C) generated much better filtration results than C-F. • Surface charges of tailing flocs potentially impacted floc strength and filtration efficiency. Rapid and cost-effective dewatering of wet tailings is crucial for clean and sustainable production in mineral processing. In the process of dewatering oil sands mature fine tailings (MFT) through coagulation-flocculation followed by pressure filtration, we observed that the dosing sequence of coagulants and flocculants had a significant influence on dewatering efficiency. Employing two anionic commercial flocculants (SNF3338 or Kemira PAM) together with an inorganic or a natural coagulant (aluminum sulfate, or chitosan), it was found that the sequence of flocculants followed by coagulants (F-C) gave higher net water release during filtration and lower moisture content in filter cakes than those in reverse sequence (C-F). The capillary suction time measurement showed a faster water release rate from the MFT treated with the F-C sequence. Zeta potential measurements indicated that flocs formed by treatment with the F-C sequence had lower negative potential or more positive potentials, likely contributing to the stronger floc strength and improved inter-floc porosity that favored pressure filtration. The findings in this Technical Note could help optimize the chemical treatment regime and reduce chemical consumption in the filtration of fine wet tailings.
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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 | 0.000 |
| 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".