Compatibility of Non-Metallic Liners for Dense Slurry (Oil Sands) Applications
Bibliographic record
Abstract
Abstract Dense slurry pipelines in the Canadian oil sands industry are exposed to significant wear from low-stress sliding abrasion and corrosion. Polyurethane and neoprene lined piping is an attractive alternative to extend dense slurry piping service life over the current non-metallic dense piping materials. The advantage of polyurethane and neoprenes is that they provide corrosion-protection from the aqueous slurry, good heat insulation properties and excellent low-stress sliding abrasion resistance. The mechanical properties of polyurethanes and neoprenes can be optimized for specific applications given the desired mechanical properties are known. However, there is limited information available in the industry on how the mechanical properties of polyurethanes and neoprenes affect its wear performance. There is also very limited information with the wear performance effects due to absorption of water and hydrocarbon (bitumen) contained in the oil sands slurry. The physical properties and wear performance of four polyurethanes and five neoprene liner materials in the virgin (un-immersed) state were determined using industry testing procedures. These materials were then immersed in a typical bitumen-water slurry solution at 70°C and atmospheric and 24 barg pressures for one, four and six month durations. Physical properties and wear performance was measured for all nine samples after each immersion duration. This paper summarizes the experimental findings, discusses the effects of a typical bitumen-water slurry solution on the wear performance of polyurethanes and neoprenes and proposes a mathematical relationship between Coriolis (low stress, low angle abrasion & scouring) wear to the relevant physical properties in the virgin state of polyurethanes and neoprenes.
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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".