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Predicting the silicon content of the hot metal based on dynamic neural network

This paper introduces a real time method of predicting the silicon content of the hot metal based on improved Elman net. With preserving most character of original data, this method can eliminate the relativity of sample data, simplify net structure, and then realize the predicting of the silicon content online. The hit rate reached 89.57% in the predicting experiments of a blast furnace on Bao Steel with error ranging from -0.05 to 0.05.