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Prediction Method of Blast Furnace Fuel Ratio Based on Spectral Clustering and Extreme Learning Machine Model

The blast furnace fuel ratio, as the main index reflecting the blast furnace fuel consumption, plays an important role in guiding blast furnace operators. In order to solve the problem of insufficient stability of the existing fuel ratio prediction model under the condition of process parameters fluctuation, a prediction method of blast furnace fuel ratio based on spectral clustering and extreme learning machine model is proposed. Firstly, on the basis of analyzing the influencing factors of fuel ratio, the classification results after clustering are visualized with t -SNE dimension reduction downscaling and spectral clustering method; Secondly, for the classification results after clustering, the extreme learning machine is used to establish the sub-models, and the PSO algorithm was used to optimize the input weights of hidden layer and the bias of hidden layer neurons. Experimental results show that the proposed method has high prediction accuracy.