Digital twins for optimization of ironmaking operations

Abstract

Manufacturing of steel involves conversion of raw iron ores into different steel products through a complex network of unit operations. Optimizing manufacturing operations and ensuring high availability of associated equipment are the key challenges faced by plant engineers. Artificial intelligence and machine learning technologies can play an important role in this. Development and deployment of digital twins for some of the unit operations in the ironmaking process are described in this article. The generic architecture of a digital twin system is presented and its adaptation for sintering, pelletization, cokemaking and blast furnace ironmaking is explained with relevant details of their industrial scale implementation and realization of tangible business benefits. The importance of developing hybrid digital twins combining physics-based models, machine learning algorithms and domain knowledge is emphasized. Potential future directions for applying physics-informed neural networks and large language models in the development and deployment of digital twins are indicated.