Ann based prediction of blast furnace parameters

Abstract

The paper presents a method to predict blast furnace parameters based on artificial neural network (ANN). The prediction is important as the parameters cause the degradation of the production process. The productivity as well as quality can be improved by knowing these parameters in advance. In this context, the iron making process in the modern blast furnace is briefly illustrated. Characterisation of the input and the output parameters as well as the design of a feed forward neural network (FFNN) is outlined. The implementation issues are discussed to predict the parameters like hot metal temperature (HMT) and percentage of impurity of silicon content in molten iron. The simulation and plant trial results are compared to show the effectiveness of the approach.