Examining the effect of conditioning sequence of polymer flocculants and coagulants on sedimentation and filtration of oil sands tailings
Bibliographic record
Abstract
• Adding cationic coagulants prior to nonionic flocculants improved slurry filtration. • Adding anionic flocculants prior to cationic coagulants improved slurry filtration. • Reversing the sequence of addition in the above two cases made filtration ineffective. This study investigated the impact of conditioning sequence of polymer flocculants and coagulants on the sedimentation and filtration of oil sands mature fine tailings (MFT). Nonionic poly(ethylene oxide) (PEO) or anionic polyacrylamide (A3370) was used as the flocculant, and cationic polydiallyldimethylammonium chloride (polyDADMAC) was used as the coagulant. Irrespective of addition sequence, the combination of flocculant and coagulant yielded higher initial settling rate and clearer supernatant than when either was used alone. However, the filtration behavior was found to depend significantly on dosing sequence of the flocculant and coagulant. A much faster filtration rate was observed when the MFT was first treated by the cationic coagulant polyDADMAC followed by the nonionic polymer PEO. In contrast, adding the anionic flocculant A3370 prior to the cationic coagulant polyDADMAC resulted in a much faster filtration rate. Floc size distribution and floc surface charge were measured to understand the effects of conditioning sequence on the sedimentation and filtration performance of MFT. It was found that the two optimal addition sequences led to larger and more porous flocs whose surface charges were closer to zero than the flocs obtained when the addition sequence was reversed. These findings underscore the importance of optimizing the sequence of flocculant and coagulant addition to enhance solid–liquid separation, with implications extending beyond oil sands tailings treatment.
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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 | 0.001 |
| 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) | 0.001 | 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".