Post-mortem Analysis of Magnesia-Carbon Refractories from Steel Ladle Furnace Slag Lining
Bibliographic record
Abstract
Refractory corrosion by slag is among the major causes of refractory deterioration and the subsequent maintenance shutdowns, resulting in downtime and loss of product. In the context of sustainability, the relatively short service life of refractories remains one of the challenges to the steelmaking industry yet to be solved. The initial conditions and process parameters; e.g., slag composition, additives, and bath stirring affect the kinetics and thermodynamics of chemical reactions; i.e., the carbon oxidation and MgO dissolution involved in the refractory-slag system, leading to the eventual loss of refractory lining. To better understand the chemical reactions taking place at the refractory/slag interface, post-mortem samples were microstructurally characterized using scanning electron microscopy coupled with energy-dispersive spectroscopy. Thermodynamic assessments of the system were also performed using FactSage™ v8.2 software and databases to identify the extent of chemical reactions. The obtained results indicated that refractory corrosion is mainly controlled by simultaneous refractory-slag chemical reactions and the mass transport of slag in the porous body of refractory, the details of which are discussed in the present work.
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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) | 0.002 | 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".