Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Data‐Driven Analysis and Process Metallurgy

Abstract

For complex, difficult‐to‐control, and hour‐delay blast furnace (BF) systems, the quantitative characterization, prediction, and adjustment of the BF hearth activity are significant in improving the furnace status. In this study, data including raw fuel, process operation, smelting state, and slag and iron discharge during the entire BF process are analyzed, with a total of 171 variables and 5033 groups of data. Based on the knowledge of BF technology, a comprehensive index of hearth activity is then proposed to quantitatively characterize and grade the activity level of the BF hearth; the rationality of this method is verified from the two aspects of furnace heat level and furnace status coincidence. Compared with the traditional single‐machine learning algorithm, the performance of the proposed method that combines genetic algorithm and stacking exhibits significant improvement. The hit rates for 10% and 5% errors in the prediction and estimation of hearth activity are 94.64% and 80.36%, respectively. To enhance the BF hearth activity, quantized and dynamic actions and suggestions are also simultaneously pushed. The model of BF hearth activity is successfully applied in practical online production. During the application period, the average furnace hearth activity increases by 10% compared to the historical value.