Analysis and Prediction of Corrosion of Refractory Materials by Potassium during Biomass Combustion-Thermodynamic Study
Bibliographic record
Abstract
As a kind of renewable resource, biomass has been used more and more widely, but the potassium contained in biomass can cause corrosion of the refractory. For a better understanding of corrosion thermodynamic mechanisms, the five components of common refractory materials (magnesium chrome spinel MgO·Cr2O3, magnesium aluminum spinel MgO·Al2O3, Al2O3, MgO, and Cr2O3) with potassium salts (K2CO3, K2SO4, and KCl) under high-temperature were studied by using the FactSageTM 7.0 software. Thermodynamic calculation results indicate that MgO is the best corrosion resistance of the five components of refractory materials. Based on the obtained results, the corrosion experiments in the laboratory were carried out (muffle furnace or high-temperature tube furnace) for corrosion reaction of KCl and MgO. The chemical compositions of the corroded samples were analyzed by X-ray diffraction (XRD). Under laboratory conditions (600–1200 °C), no corrosion products have been observed in the high-temperature corrosion experiments. The result indicates that to prevent the corrosion processes, refractories should contain as much MgO as possible.
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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 | 0.000 | 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) | 0.001 | 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 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".