Prediction of Blast Furnace Fuel Ratio Based on Back‐Propagation Neural Network and K‐Nearest Neighbor Algorithm

Abstract

Table 2. Characteristic parameters selected under different thresholds. re Characteristic parameters 0.5 Coke M10, Theoretical alkalinity, Proportion of lump ore, Blast volume, Sinter particle size <5 mm, Sinter particle size >40 mm, SiO 2 content of sinter, S content of hot metal, Slag ratio, H 2 content, Si content of hot metal, Coke particle size <15 mm, Coke batch, Coke ratio 0.6 Blast temperature, Coke particle size 40-25 mm, Raceway flame temperature, Coke M10, Theoretical alkalinity, S content of hot metal, Slag ratio, H 2 content, Si content of hot metal, S content of hot metal, Sinter tumble strength, Coke particle size <15 mm, Coke batch, Coke ratio 0.7 Blast temperature, Coke particle size 4025 mm, Bottom center temperature, Raceway flame temperature, Coke M10, Theoretical alkalinity, Slag ratio, H 2

Intelligent Environment Design for Indoor Spaces Based on Perception and Behavior Correlation

“…Finally, a comparative experiment was conducted using BPNN and k-nearest neighbor algorithm. From the results, the BPNN could keep the error within 2% and achieve an accuracy of 93.02% [13]. Xu et al developed a compensation method based on BPNN to solve nonlinear magnetic interference.…”

“…It can distinguish individual strengths and weaknesses. The fitness function of GA is expressed as formula (13).…”

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

“…Data-driven modeling and machine learning algorithms have achieved important and new applications in the metallurgical industry. [16][17][18][19][20][21] However, little research has been conducted on the hearth activity of BFs. Deng et al [22] selected the temperature ratio as the characterization parameter of hearth activity based on data mining and established a quantitative model.…”

A Novel Prediction Method for Blast Furnace Gas Utilization Rate Based on Dynamic Weighted Stacked Output‐Relevant Autoencoder

“…[4] Therefore, it is significant to accurately obtain GUR online. However, BF is a typical black-box system that lacks direct and effective measuring means to understand the internal state of the furnace, [5,6] making accurately obtaining GUR in real-time is not easy.Over the last decades, many experts and scholars have been dedicated to the research for GUR; in general, these researches can be divided into two categories: mechanism-driven [7][8][9][10] and datadriven. [4,[11][12][13][14][15] Mechanism-driven methods have the advantage of strong interpretability, for instance, Kou et al [7] proposed a 3D mechanism model to analyze the effect of different blast parameters on the GUR.…”

“…[4] Therefore, it is significant to accurately obtain GUR online. However, BF is a typical black-box system that lacks direct and effective measuring means to understand the internal state of the furnace, [5,6] making accurately obtaining GUR in real-time is not easy.…”