A Novel Prediction Algorithm for the Cross Temperature Estimation of Blast Furnace

Abstract

In order to predict the distribution of gas flow, we need to get the temperature of each point in blast furnace throat in advance. In this paper, firstly, two intelligent modeling methods are used to establish a multiple-input multiple-output prediction model, one is extreme learning machine (ELM) algorithm and the other is online sequential extreme learning machine (OS-ELM) algorithm. And the model is a single-step prediction model of temperature in blast furnace, single-step prediction means the prediction of temperature in the next moment. We use autocorrelation analysis to determine input vector and output vector of the model. The result of autocorrelation analysis indicates that the method of temperature sequence prediction has a higher prediction accuracy and better prediction stability than the method of single point prediction. Next, based on real industrial data, we make a comparison between the multiple-input multiple-output model and least squares support vector machines (LS-SVM) model used in common. The experiment results show that OS-ELM model has a better forecast effect than the ELM model and LS-SVM model.