Impact on refractory lining due to the transition from carbon-based fuels and reductants to hydrogen: separating myths from facts
Bibliographic record
Abstract
The global drive to significantly reduce greenhouse gas emissions is pushing industries to adopt hydrogen as both a reducing agent and fuel for high-temperature processes. While some knowledge exists on the impact of hydrogen-rich reducing atmospheres on refractory linings from established industrial processes like glass manufacturing, ammonia or syngas synthesis, and natural gas-based direct reduced iron (DRI), the use of hydrogen as a reductant for iron production is gaining traction as a key solution for the steel industry's transition to net-zero emissions. This involves using hydrogen in small percentages to replace coal in blast furnaces and, more significantly, substituting natural gas with hydrogen in the DRI process, potentially up to 100 %. Moreover, the growing demand for fossil-free fuels has spurred the development of innovative and optimized hydrogen and syngas generation technologies.While new and established processes differ in their specific conditions, there is a limited amount of information available in the literature about the long-term effects of hydrogen-rich atmospheres on refractories. Recent studies have yielded contradictory results compared to earlier research, making it challenging to discern myths from facts and accurately assess the performance and lifespan of refractory linings in these environments. This presentation aims to share fact-based findings from ongoing research and our experience in various industries, providing insights into the impact of hydrogen on refractory linings as a function of process conditions and testing parameters.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".