PREDICTION OF SILICON CONTENT IN HOT METAL BASED ON GOLDEN SINE PARTICLE SWARM OPTIMIZATION AND RANDOM FOREST

被引：0
|
作者：
Hu, CH. [1 ]
Yang, K. [1 ]
机构：
[1] Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan 114051, Peoples R China
来源：
METALURGIJA | 2022年 / 61卷 / 02期
关键词：
blast furnace; hot metal; silicon; particle swarm optimization; golden sine algorithm; random forest;
D O I：
暂无
中图分类号：
TF [冶金工业];
学科分类号：
0806 ;
摘要：
Particle Swarm Optimization (PSO) algorithm quickly falls into local optimum, low precision. In this paper, add the golden sine operation to the particle position update. The results show that the improved PSO algorithm has better optimization ability. The main parameters affecting the silicon content in hot metal are selected. Then, calculate the correlation coefficient and significance level between parameters and silicon content in hot metal. Finally, the prediction model of silicon content in hot metal is established based on the Random Forest (RF) optimized by improved PSO. The results show that the hit rate is 87,17 %.
引用
收藏
页码：325 / 328
页数：4
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