Dewatering Oil Sands Mature Fine Tailings (MFTs) with Poly(acrylamide-<i>co</i>-diallyldimethylammonium chloride): Effect of Average Molecular Weight and Copolymer Composition
Bibliographic record
Abstract
Copolymers of acrylamide and cationic comonomers such as diallyldimethylammonium chloride can effectively dewater solid suspensions containing negatively charged fine particles. A good example of such suspensions is oil sands mature fine tailings (MFTs). However, little is known about the impact of the microstructure of copolymers in dewatering performance. In this study, we used a surface response methodology to systematically assess the impact of chemical composition and average molecular weight of poly(acrylamide- co -diallyldimethylammonium chloride) in dewatering MFTs. We controlled the chemical composition and average molecular weight of the copolymers by manipulating feed monomer/comonomer ratios and initiator concentration. We found both the copolymer composition and the molecular weight averages were statistically significant variables for initial settling rates and supernatant turbidities of flocculated MFTs. However, capillary suction time and resistance to filtration depended only on copolymer composition and polymer dosage, not on average molecular weight. Depending on the polymer dosage, we found the optimum chemical composition to vary between 0 and 60 mol % acrylamide. The results of this study are important for the oil sands and polymer flocculation industry trying to use/produce flocculants at minimum cost and maximum performance.
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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 | 0.001 |
| 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".