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| Auteurs principaux: | , , , |
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| Format: | Recurso digital |
| Langue: | |
| Publié: |
Zenodo
2025
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| Accès en ligne: | https://doi.org/10.5281/zenodo.18318806 |
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Table des matières:
- <p>The drying of monolithic refractories remains a significant entry barrier for many potential markets and equipment that could benefit from their unique features such as automated application, higher installation rates and lack of expansion joints. Although the promise of using numerical models to predict drying behavior and to optimize this lengthy process - while avoiding explosive spalling - is not new and has been explored since the early 1990s, most industrial drying processes still rely on semi-empirical approaches. These methods result in extended maintenance shutdowns, profit losses, and unnecessary CO<span>2</span> emissions. Bridging the gap between reliable and practical numerical tools and real industry applications requires an in-depth understanding of the mechanisms during water removal and an appropriate selection of assumptions that can accurately capture the observed behavior. This is a long-term endeavor, and immediate economic and environmental improvements cannot wait, especially in the current challenging context. Thus, the present work addresses the major milestones that could drive the decision-making on simulating drying and illustrates several insights and guidelines obtained from the currently existing, albeit imperfect, models. Key findings include the confirmation of the moisture-clogging phenomenon, the role of thermomechanical stresses in explosive spalling, and the impact of weepholes on drying efficiency. Thermomechanical simulation results are also presented to investigate potential hypotheses for the mechanisms driving the permeability increase in fiber-containing castables. These insights provide practical guidance for optimizing drying processes today, with the potential to reduce downtime, minimize emissions, and improve overall efficiency in refractory applications, while also laying the ground for improved models that will eventually lead to optimized and safe heat-up curves.</p>