Evaluating Consolidation Behaviors in High Water Content Oil Sands Tailings Using a Centrifuge
Bibliographic record
Abstract
The composition of oil sands tailings is a complex mixture of water, fine clay, sand, silt, and residual bitumen that remains after the extraction of bitumen. Effective tailings disposal management requires an understanding of the mechanisms controlling water movement, surface settlement rates and extents (hydraulic conductivity and compressibility), and strength variation with depth. This investigation examines the self-weight consolidation behavior of oil sands tailings, typically assessed by utilizing large strain consolidation (LSC) methods such as the multi-step large strain consolidation (MLSC) test and seepage-induced consolidation test (SICT). These methods, however, are time consuming and often take weeks or years to complete. As an alternative, centrifuge testing, including both geotechnical beam type and benchtop devices, was utilized to evaluate the consolidation behaviors of three untreated high water content oil sands tailing slurries: two high-plasticity fluid fine tailing (FFT) samples and one low plasticity FFT. The centrifuge-derived compressibility data closely matched the LSC testing compressibility data within the centrifuge stress range. However, the hydraulic conductivity obtained from centrifuge testing was up to an order of magnitude higher than the LSC test results. Comparing centrifuge and large strain modeling results indicates that centrifuge test data demonstrate average void ratios 10–33% lower than those predicted by simulations using LSC parameters, highlighting a notable deviation. To examine the scale effect on result accuracy, validation tests indicated that the benchtop centrifuge (BTC) yielded comparable results to the geotechnical beam centrifuge (GBC) for the same prototype, saving time, resources, and sample volumes in the assessment of tailings consolidation behavior. These tests concluded that the small radius of the benchtop centrifuge had a minimal impact on the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".