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Дата издания
16.05.2025На каком языке издана
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Аннотация (краткое описание)
The purpose of this study is to extend the service life of liquid-layer furnaces through experimental evaluation of their performance. Instead of using conventional large and small aggregates, this research investigates the use of secondary refractory materials to reduce the cost of the concrete mixture. To address this problem, it was proposed to replace the traditional method of lining the furnace floor with SHB-brand fireclay bricks by a monolithic concrete structure. Considering the operational characteristics of the furnace - high temperatures up to 1000 °C and an aggressive environment producing SO3 gaseous sulfur -sodium-infused liquid glass concrete was selected as the base. Instead of cement, liquid glass with low thermal conductivity, including amorphous-phase materials and glass-layered composite viscous substances, is recommended. A novel aspect of this research is the integration of machine learning techniques with experimental data to predict the thermal performance of concrete under high-temperature conditions. Furthermore, the study introduces the use of secondary refractory materials as a cost-effective aggregate alternative, offering a unique combination of sustainability and performance in industrial furnace applications.DOI
https://doi.org/10.21595/vp.2025.25013Тип публикаций
Практическое значение
The experimental study confirmed the feasibility of using secondary refractory materials as aggregates in the production of heat-resistant concrete. This approach offers a cost-effective alternative by reducing the consumption of primary raw materials. By optimizing the concrete composition, including the replacement of cement with sodium-based liquid glass and the use of crushed SHCU fireclay bricks, improvements in both thermal resistance and compressive strength were achieved. Test results demonstrated that certain compositions could endure up to 14 heat cycles at 900 °C, meeting industrial furnace requirements. Furthermore, machine learning analysis was employed to model the relationship between physical parameters (such as weight, density, and curing age) and thermal performance. The regression model showed that data-driven approaches can provide useful predictions for material behavior, especially in variable high-temperature environments. These findings contribute to the development of standards for heat-resistant concrete and offer practical guidance for engineers and designers working on furnace lining systems. The proposed monolithic lining method reduces the number of joints, enhances durability in aggressive environments, and shortens repair times, thereby improving operational efficiency in industrial applications. The combination of experimental validation and machine learning analysis represents a novel methodological contribution, enabling more accurate design of durable, high-performance concrete for industrial furnace applications.Квартиль
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SCImago (Scopus)Impact Factor
36Количество цитирований
2Данный документ располагается в коллекциях
Международные публикации ученых SU
Scopus
Файлы документа
vp-58-25013.pdf