Prediction of Silicon Content of Molten Iron in Blast Furnace Based on Particle Swarm - Random Forest

被引：3

作者：

Zhou, Kecheng ^{[1
]}

Hu, Fei ^{[1
]}

Gong, Jun ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Peoples R China

来源：

PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年

关键词：

Molten iron silicon content; Random forest; Particle swarm optimization;

D O I：

10.1109/CCDC52312.2021.9602093

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The silicon content of blast furnace hot metal is not only the key index in the steel preparation process, but also is often used to characterize the furnace temperature and the running state of blast furnace. Therefore, the prediction method of molten iron silicon content is an important topic in the research of blast furnace smelting. In this paper, the classical stochastic forest model theory is improved and an inter-tree weighted stochastic forest model is proposed. At the same time, particle swarm optimization (PSO) algorithm is used to optimize the model parameters. Finally, through experimental verification, PSOTWB-RF model proposed in this paper has high prediction accuracy and hit ratio.

引用

页码：2814 / 2819

页数：6