A hybrid neural network model for predicting silicon content

Abstract

The silicon content of the hot metal in the blast furnace ironmaking process normally reflects the thermal state of the furnace and affects the fuel rate. In this paper a hybrid neural network model is proposed to predict the silicon contentn steps ahead. A time-delay neural network, which has self-loops to represent dynamics, is adopted here. The learning procedure of this network has been divided into two states. A BP algorithm with forgetting factor is first introduced to find the appropriate structure of the network. The temporal difference (TD) method with forgetting factor is then used forn-step-ahead prediction. The results show that the method can perform satisfactoryn-step-ahead prediction and is suited for implementation.