Erosion-Corrosion Performance Evaluation of Different Materials for Oil Sand Application
Bibliographic record
Abstract
Abstract Material loss due to erosion-corrosion decreases the throughput and useful life of the equipment. Simultaneous action of erosion and corrosion is responsible for the high degradation of the hydro-transport equipment. To minimize the operational cost, based on the degradation severity in a specific operation, different classes of materials are being used for different applications. Pipeline steels (i.e. plain carbon, API steels), for example, are relatively cheap and do not provide good wear resistance. These types of steels are typically used for less severe applications such as tailings. Chrome white irons (CWIs) and WC-based overlays, on the other hand, are usually used for more severe wear conditions (i.e. hydro-transport pipeline). In this study, we have evaluated the erosion-corrosion performance of 4 homogeneous materials (pipeline and abrasion-resistant steels) and 5 materials containing different types and amounts of carbide (chrome white iron and WC-based overlay). Erosion-corrosion test was performed inside a slurry pot at 45°C, in an aqueous slurry containing 35wt% natural silica sand and 3.5wt% NaCl. The wear performance of these materials was evaluated based on the total erosion-corrosion (E-C) rate as well as the separate components of synergistic effect. In the current test condition, WC-based overlays demonstrate the best erosion-corrosion resistance. For all carbide-containing materials, it was found that matrix wear influences the extent of carbide degradation. For the carbides to provide good erosion-corrosion resistance, the surrounding matrix that supports the carbide should have sufficient wear and corrosion resistance. Dominant wear mechanisms for homogeneous and carbide-containing materials have also been identified.
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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 | 0.001 | 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) | 0.008 | 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".