Manipulating chemicals dosing sequence for improved aggregation and filtration of oil sands tailings
Bibliographic record
Abstract
Dry stackable tailings is highly desirable as it allows for fast water recycle and facilitates land reclamation. To generate stackable tailings, it is critical to develop rapid and cost-effective dewatering processes. In this study, we investigated the aggregation and filtration of oil sands fluid fine tailings (FFT), a complex waste slurry of extreme colloidal stability, following individual and combined chemical treatment with coagulants and flocculants. We observed that the filtration efficiency depended on the sequence of coagulant and flocculant dosing. When an anionic flocculant (Kemira polyacrylamide) was added before a coagulant (alum, ferric chloride, or polyDADMAC) (F-C sequence), much faster filtration rates and higher solids content in filter cakes were observed than that resulting from the reverse coagulant-flocculant sequence (C-F). Adsorption analysis using QCM-D revealed that the coagulant adsorbed at higher densities and with faster kinetics following the prior adsorption of the anionic flocculant in the F-C sequence. The coagulant neutralizes the negative charges of the flocculant and enhances the rigidity of the integrated adsorption layer. The closer packing and cross-linking of the flocculant chains induced by the coagulant generates a robust floc matrix with higher shear resistance, and maintains the porous structures needed for fast filtration. In contrast, the C-F dosing sequence produces a softer, more flexible adsorption layer where the anionic flocculant readily undergoes conformational rearrangement, leading to a weaker and more deformable floc matrix that is incompatible with fast and efficient filtration. These findings elucidate the relationship between microscopic adsorption layer configuration, floc structure, and macroscopic filtration performance in individual or sequential dual chemical treatment of oil sands FFT at different dosing sequences. The understanding can help optimize chemical conditioning schemes for fast tailings filtration and water recycle, ultimately leading to timely reclamation and sustainable mining practice.
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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".