Evaluation of the flocculation and dewatering performance of dual polymers for treating oil sands tailings
Bibliographic record
Abstract
Abstract Mature fine tailings produced in the processing of oil sands create serious water‐recovery problems; thus, it is important to characterize them and to improve the applicable dewatering technologies. Existing methods to dewater the tailings are found to be insufficient. Previous research has demonstrated that the addition of dual polymers is more efficient than using a single polymer to flocculate the fine tailings particles. Different from studies that took sophisticated procedures to assess the flocculation and dewatering efficiency of dual polymers, readily available and simple but effective experiments were conducted in this work. Evaluations were conducted with dual polymers comprised of low molecular weight poly(diallydimethylammonium chloride) (polyDADMAC) and high molecular weight cationic or anionic polyacrylamide (PAM). Turbidity and residual solids measurements for dewatered tailings treated with these polymers confirmed that the dual‐polymer approach yield superior performance compared to either polymer functioning alone. Zeta potential and microscopic observations indicated that the dosages of polyDADMAC and PAM that lead to near charge neutralization of the system result in maximal performance. We hypothesize that when dual polymers are added to the tailings, polyDADMAC acts to neutralize the charge and destabilize the fine particles, while high molecular weight PAM, either cationic or anionic, acts to aggregate the destabilized fines by the bridging mechanism. PolyDADMAC forms weak and tiny flocs when mixed with tailings particles. Cationic PAM transforms these small flocs into much larger ones, while anionic PAM connects them into aggregates. Therefore, synergism exists between polyDADMAC and PAM but this synergism is different, based on the charge of the PAM.
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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) | 0.001 | 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 | 0.001 |
| 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".