Enhanced settling of mature fine tailings (MFT) by paramagnetic nanofluids based on nanoiron
Bibliographic record
Abstract
The extraction of bitumen from sand by utilizing hot water processes results in the production of a slurry waste which is stored in so called “tailings ponds”. Within these ponds, while fast settling sand particles segregate from the slurry in relatively short time, the fines fraction accumulates in the center of the pond and then settles, becoming mature fine tailings (MFT). Most of the water content of the pond is recycled back, however around 86% of the volume of MFT consist of water and it cannot be recycled. It takes a few years after placement for MFT to settle to around 35% solids. By 2008 there were about 750 million cubic meters of MFT within the tailing ponds. Assuming the tailing management remains the same, the amount of fluid tailings is expected to reach two billion cubic meters by 2034. In 2009 it was estimated that there were around 130 square kilometers of tailings ponds in the oil sand region at Canada. Thus, one of the most important environmental challenges regarding oil sands mining is developing a process to separate water from the fine tailings within a reasonable time frame, in order to allow for the reclamation of the site. Bearing in mind these problems we initiated a preliminary applied research program in order to prove the concept of speeding up sedimentation of MFT by means of nanoparticles, and more specifically by using paramagnetic nanofluids manufactured with these nanoparticles. This preliminary series of tests proved that the concept of enhancing settling of MFT by means of paramagnetic nanofluids is feasible. Clarification of samples treated with magnetic nanoparticles achieved a 50% value as compared to only a 10% clarification in the same lapse for untreated samples, while dosage of conventional flocculants resulted in erratic behavior.
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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".