A Sliding-Window T-S Fuzzy Neural Network Model for Prediction of Silicon Content in Hot Metal

Abstract

Iron making in blast furnace is one of the most complicated industrial processes, especially in its dynamics, inertial properties and multi-scale availabilities. Over the years, researchers have been using silicon content to judge the temperature and the conditions within the blast furnace due to the complexity in measuring the actual status that results from extreme temperatures and intricate environment. Addressing these limitations, a sliding-window Takagi-Sugeno fuzzy neural network(SW-TS FNN) model is proposed to predict the silicon content in hot metal. Through the sliding of a proper width of the sliding-window, the train data for T-S fuzzy neural network(FNN) model can be updated at desired time increments, giving the latest prediction of silicon content. Compared to a simple T-S FNN model on the prediction of silicon content, this SW-TS FNN model shows great improvement at hit rate and mean-square error.