ANN-based structure-viscosity relationship model of multicomponent slags for production design in mineral wool

The production of mineral wool from waste high-temperature slags is considered eco-friendly and economical. However, this application is facing a great challenge from an unstable production process caused by the variable compositions and properties of slags. Especially, the viscosity of slags, as a significant property, governs the processes of melt homogenization, tapping, and fiber formation. Here, we designed a structure-property rela-tionship model to describe the development of slag viscosity over a broad range of composition and temperature. A feed-forward back-propagation artificial neural network was adopted as the basis to establish quantitative relationships between effective variables and viscosity. A comprehensive database containing 5474 reliable laboratory-based samples was constructed and further developed using the micro-scale structural features. The importance of structural features was evaluated using the statistical connection weight approach and 74 kinds of first-nearest-neighbor pairs and 4 medium-range characteristics (three kinds of oxygen species and the degree of depolymerization of network) were selected and integrated into the model. The results indicated the obtained model exhibited excellent performance, with a testing mean square error of 0.0592 and a testing Pearson's correlation coefficient of 0.9966. The model was compared with several other common viscosity models and also showed the optimal predictive ability. Furthermore, the model was successfully applied in the production of mineral wool from three complex slags and the high-quality mineral wool product was achieved.

Automated summary

The study established a structure-property relationship model for slag viscosity, utilizing artificial neural networks and a large database of experimental samples. The model successfully predicted slag viscosity and was able to be applied in the production of mineral wool, achieving high-quality results.