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

Abstract

Uniform blast furnace (BF) hearth activity is crucial for BF smooth running. For complex, difficult‐to‐control, and hour‐delay 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 were 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 was then proposed to quantitatively characterize and grade the activity level of the BF hearth; the rationality of this method was 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 (GA) and stacking exhibited significant improvement. The hit rates for 10% and 5% errors in the prediction and estimation of hearth activity were 94.64% and 80.36%, respectively. To enhance the BF hearth activity, quantized and dynamic actions and suggestions were also simultaneously pushed, which were highly recognized by BF operators. The model of BF hearth activity was successfully applied in practical online production. During the application period, the average furnace hearth activity increased by 10% compared to the historical value, and the average daily output level of hot metal increased by 1.3%.This article is protected by copyright. All rights reserved.