Ensemble Prediction of Tundish Open Eyes Using Artificial Neural Networks

Abstract

As global steelmakers are feeling the economical pinch, the need for improving quality and quantity using what is already readily available, increases. This gap in achievement can be bridged by innovation and perforation of already existing techniques and methodologies from other fields. Steel quality, an important issue, is often not associated with a phenomenon known as tundish open eyes. However, recently researchers have shown the detrimental effects of reoxidation and the deterioration of the final product (slabs/billets). Understanding the formation of this event, and mitigating the formation will be an important issue to solve. Current models investigating the former have existed largely in the computational fluid dynamics modelling domain. However, the solution for the former, can only provide static recommendations thus are less useful in a dynamic environment. Hence, development of a reliable model which has the ability to “learn on the fly” is very much needed. In the current study, artificial neural network models have been used to predict non-dimensional open eye sizes in the tundish. The dataset has been compiled from previous regression formulations. The performance of the models is determined based on the following metrics 1) coefficient of multiple determination (R²), 2) and root mean square error (RMSE). The ANN based models, show significant promise, in particular the ensemble variants, which have shown increased accuracy and stability across all domain and range.