Assessing MMCs for Corrosion and Erosion-Corrosion Applications in the Oil Sands Industry
Bibliographic record
Abstract
Abstract Erosion-corrosion arising from aqueous slurry environments can be a significant problem in the oil sands industry. Interactions between erosion and corrosion are complex and as such it is difficult to determine the rate of material loss with sufficient accuracy for reliable prediction of equipment lifetime. One material which has been successfully used on production critical equipment is tungsten carbide (WC) metal matrix composite (MMC) weld overlays. Four WC-based hardfacings with different particle size distributions were investigated. These overlays were comprised of 65 wt % WC hard phase with a metal matrix binder consisting of mainly Ni, Cr, Si, B and Fe. The Metal Matrix Composites (MMCs) overlays were applied using the plasma transferred arc (PTA) welding process Electrochemical corrosion tests in a simulated recycle cooling water environment were conducted to investigate the corrosion behaviour of the MMCs. In static corrosion tests, little change in the corrosion rate with different WC grain sizes was observed. The smallest WC grain size distribution did show a slight decrease in corrosion resistance. Similarly, little difference in erosion-corrosion was recorded for the different WC grain size fractions tested with larger grain sizes showing a slight reduction in erosion-corrosion resistance. The interactions between erosion and corrosion can be identified and are important in the MMC degradation. The corrosion mechanisms in static condition and the erosion-corrosion mechanisms can be directly linked to the complex microstructure of the MMCs.
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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 | 0.000 | 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".