Thermodynamic Study of the Corrosion of Refractories by Sodium Carbonate
Bibliographic record
Abstract
The corrosion of refractories by sodium salts in waste liquid at high temperature has become a serious problem. This paper focuses on the thermodynamic characterization of sodium carbonate (Na₂CO₃) corrosion of six refractories by FactSage modelling in combination with X-ray diffraction (XRD). Three of the refractories are oxides (Fe₂O₃, Al₂O₃, and Cr₂O₃), and the other three are synthetics spinels (magnesium chromium, MgO·Cr₂O₃; magnesioferrite, MgO·Fe₂O₃; and, magnesium aluminium, MgO·Al₂O₃). First, thermodynamic simulations were carried out with the FactSage thermodynamics model using the reaction package to predict the direction of the Na₂CO₃ corrosion reaction in terms of the Gibbs free energy. Then, the reactions between the six refractories and Na₂CO₃ were conducted through a series of refractories/Na₂CO₃ reaction tests. The XRD analytical method was used to describe and understand the chemistry and interpret mineral matter transformation. The products of the tests were also determined by X-ray diffraction and the experimental observations were compared with the results of the thermodynamic simulations. Furthermore, the strength of sodium corrosion of the refractory materials was comprehensively discussed. The results show that MgO·Al₂O₃ has the best thermal stability and it is hard to corrode by Na₂CO₃, while the chrome-containing refractory reacts easily with Na₂CO₃ with a considerably high amount of corrosion product at a temperature of 600 °C. These experimental results are in agreement with the thermodynamic calculations.
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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 | 0.001 |
| 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) | 0.001 | 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".