| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Alumina & Bauxite
|
| Presentation Title |
Fast Solution of Shrinking Core Model for Calcination Applications |
| Author(s) |
Vladimir Golubev, Dmitriy Chistyakov, Dmitriy Mayorov, Evgeniy Fomichev, Iliya Blednykh |
| On-Site Speaker (Planned) |
Vladimir Golubev |
| Abstract Scope |
The shrinking core model is used in many applications, including simulations on alumina and limestone calcination. It helps to predict product properties, i.e. phase composition, reactivity and strength. Its application is limited by computational costs and the complexity of known numerical solutions of transfer equations for mass, heat, concentration and moment in the core and shell of a particle with moving reaction front, where the material properties are broken. The paper proposes to integrate the shrinking core model equations using a deep learning neural network which allows obtaining a high-quality solution much faster as compared with finite difference methods. The paper discusses the examples demonstrating the use of a new fast solver in the model of alumina flash calciner to calculate LOI for each particle size grade. The paper demonstrates the similar way to solve the problem of burning limestone in a shaft kiln. |
| Proceedings Inclusion? |
Planned: Light Metals |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Other |