Abstract
Understanding metallurgical processes has historically relied on measured data (which are often very limited), laboratory experiments, simplified unitary operation models, and more recently, computational models (like Computational Fluid Dynamics). Detailed measurements, especially during industrial operations, are often difficult due to the corrosive environment and high temperature of the process. In some cases there is currently no possibility for direct measurements, or if a possible sensor exists the sensor cost is not acceptable. Computational approaches have severe limitations due to the large computational overhead of running these models for large systems with long-term transient variations. In addition we have complex chemistry and multi-physics with strongly coupled phenomena. In such a context, pragmatic models are extremely useful to gain insight into the operations and optimize the process. The pragmatic models are designed to run real-time, or even faster. In this paper, we will provide an overview of applications of the pragmatic modelling techniques to three metallurgical applications: alumina transport in aluminium reduction cells, ladle erosion, and Søderberg electrodes. The pragmatic modelling approach used to develop the simulation framework for these processes and the predictive ability of the models are summarized. Finally, applicability of these pragmatic modelling as well as data-driven approaches (like machine learning) in building digital twins for metallurgical processes are discussed.