ANN-Based Model to Predict the Viscosity of Molten Blast Furnace Slag at High Temperatures of &gt; 1600 K

Liu, Qingshan; Wu, Junjun; Shao, Yice; Wang, Hong; Zhu, Xun; Liu, Qiang

doi:10.1007/s40831-023-00706-0

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

ANN-Based Model to Predict the Viscosity of Molten Blast Furnace Slag at High Temperatures of > 1600 K

Cited by 4 publications

References 75 publications

Blast Furnace Thermal State Prediction Based on Multiobjective Evolutionary Ensemble Neural Networks

Blast Furnace Thermal State Prediction Based on Multiobjective Evolutionary Ensemble Neural Networks

Approaching the emissivity of CMAS at high temperatures by convenient thermocouple-based infrared thermography

Modelling centrifugal-granulation-assisted thermal energy recovery from molten slag at high temperatures

Contact Info

Product

Resources

About