A FactsSage Simulation Study on the Interaction of Synthetic Petcoke Slags with Alumina Crucibles
Bibliographic record
Abstract
In entrained flow gasifiers, inorganic species in solid fuels are converted to slag, which flows continuously along the gasifier’s refractory lining. Slag viscosity is critical for its continuous flow and, consequently, reliable operation of the gasifier. Viscosity of synthetic petcoke ash was measured in a high temperature viscometer (up to 1500 °C) using high alumina crucibles. Crucible material was found to dissolve in slag, causing thinning and leading to formation of holes on the walls. To explain this dissolution, thermodynamic equilibrium calculations were performed in FactSage™ (Thermfact/CRCT, Montreal, QC, Canada and GTT-Technologies, Aachen, Germany) using different synthetic petcoke ash compositions in 100% H2, 5% H2/ 95% N2, 69.5% CO/30.5% CO2, and 100% O2 atmospheres. An inverse correlation was found between crucible dissolution and alumina content in the slag. Rates of dissolution of alumina from crucible into slag varied significantly in the different atmospheres. The correlation was validated experimentally by heating six synthetic slags with varying compositions to 1500 °C in 5% H2/N2 (to simulate viscometer’s atmosphere) gas. SEM-EDS analysis of the samples confirmed that the sample with lower initial content of alumina in the slag showed higher amounts of aluminum at the slag–crucible interface. Additions of alumina in the synthetic petcoke ash (containing up to 49.74% V2O5) mitigated crucible dissolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".