Development of a Novel Testing Method for Characterizing Wet Sliding Abrasion in Slurry Transport Systems
Bibliographic record
Abstract
Abstract Slurry pipelines are critical systems used in oil sands mining operations to efficiently transport ore from the mine site to centralized extraction facilities. One of the main challenges in designing these systems is the screening and selection of optimal pipeline materials. Slurry pipelines in oil sands mining (hydro-transport and tailing lines) are subjected to aggressive abrasion and erosion-corrosion conditions resulting in relatively rapid material wear rates. For some operations, the degradation of these pipelines can result in significant maintenance and replacement costs. To address this issue, operators are constantly looking for new material systems which can be used to increase wear resistance and run-life of pipeline systems. Numerous lab-scale test methods exist to assess and rank the ability of materials to resist abrasive conditions, but no definitive method is recognized as a standard by industry (other than costly field testing). In the first part of this paper, the relative merits of a number of currently available lab-scale testing methods used to characterize material wear are critically assessed. To highlight these merits, comparative tests were performed on a variety of materials including a number of polymers and carbon steel. Results show that the ranking of material performance varies with test method used, and highlights the importance (and difficultly) in selecting an appropriate test method that represents actual service conditions. For the second part of this paper, the development and preliminary assessment of a novel wet wheel abrasion test apparatus is showcased. The intent of this new method is to better simulate the wear mechanisms found in multiphase pipelines with dense-bed slurry flows. Preliminary tests were performed on a number of novel titanium-carbide (Ti-C) reinforced polyurethane materials with two distinct particle size ranges. Performance was evaluated by comparing results to a conventional steel alloy commonly used by the industry, and an unreinforced polyurethane system. Wear mechanisms were assessed through microscopy and wear scar profile analysis. A discussion is also provided of the key benefits of this test method (including the potential for assessing the effects of dissolved oxygen and/or fluid chemistry effects), and future work required to validate this novel test system.
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 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 | 0.002 | 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".