Co-processing of fresh oil sand tailings and fluid fine tailings
Bibliographic record
Abstract
This paper describes co-processing of fresh tailings (i.e. whole tailings), such as coarse tailings and flotation tailings from the oil sand extraction plant, and legacy fluid fine tailings (FFT) in line with a polymeric flocculant to produce paste tailings without the use of thickeners and cyclones. The objective of the project is to develop an efficient and low-cost tailings management technology to accelerate the creation of trafficable landforms that are ready for terrestrial reclamation. The idea for co-processing is that increasing the sand-to-fines ratio (SFR) will increase the hydraulic conductivity of the co-processed deposit, thus accelerating its consolidation rate. Compared to composite tailings (CT) with SFR of 3–5 and FFT centrifuge cake or flocculated FFT (fFFT) with SFR of 0–0.1, the target SFR of co-processing is 1–3 with an optimal SFR of 2. In a broader definition, the co-processing could become the treatment of whole tailings when the FFT supply is shut down or switch to the flocculated FFT process if the fresh tailings are diverted for conventional beaching operation. This paper highlights the advancements in co-processing technology development from laboratory-scale to small pilotscale. It discusses learnings from large strain consolidation (LSC) and beam centrifuge testing of the co-processed deposits to assess its long-term consolidation within the context of final reclamation and closure. Results to date show that co-processing of fresh tailings and FFT is a promising technology for achieving terrestrial closure of oil sand tailings.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".