Affiliation:
1. School of Metallurgy Northeastern University Shenyang Liaoning 110819 China
2. Institute for Frontier Technologies of Low-Carbon Steelmaking Northeastern University Shenyang 110819 China
3. Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking (Ministry of Education of China) Shenyang Liaoning 110819 China
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.
Funder
National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
Subject
Materials Chemistry,Metals and Alloys,Physical and Theoretical Chemistry,Condensed Matter Physics