Neural network model of burden layer formation dynamics in the blast furnace

Abstract

A model of the thickness of burden layers in the ironmaking blast furnace is presented. Local layer thickness estimates are calculated on the basis of signals from stockrods that measure the burden (stock) level in the furnace. These estimates are used in developing a model for the relation between the layer thickness and variables such as stock level and movable armor settings. Because of the nonlinear dependence of the variables, the models are based on feedforward or recurrent neural networks. The network size is carefully selected based on a cross-validation procedure. The resulting neural model is first studied by analyzing its predictions for different inputs. By further introducing a simplified scheme for considering the practical constraints of the charging process, an autonomous model, where the neural network plays an important role, is formed. This hybrid model is applied to yield insight into the dynamics of the layer formation process; the effect of movable armor settings, stock level and burden descent rate are analyzed and compared with practical experience.