Ensemble Prediction of Tundish Open Eyes Using Artificial Neural Networks

Often considered a trivial event, recent research on the tundish open eye phenomena has directly associated the existence of the event with final steel quality. To prevent reoxidation during the liquid metal transfer from ladle to tundish, an inert gas such as argon is typically used around the refractory ladle shroud to protect the melt stream from re-oxidizing. However, aspiration of argon is inevitable due to the presence of a negative static pressure. Once entrained, the argon bubbles travel downwards into the tundish, until the buoyancy force starts to act and pushes them upwards. At which point, a rising bubble plume is formed beneath the ladle shroud, and the slag is pushed radially outwards forming an exposed eye (Fig. 1). A similar affair also occurs in the ladle whereby, porous plug purging results in slag being pushed radially outwards due to the rising plume. Correspondingly, with the current pinch in prices of steel, any iterative improvement on understanding and predicting steel quality using already available data is very much welcomed. Because the occurrence of open eyes is inherently physical, the modelling of these events has understandably been, for the most part limited to physical simulations. Work in this domain has been lead by researchers such as Chattopadhyay Ensemble Prediction of Tundish Open Eyes Using Artificial Neural Networks