Prediction of Gas Utilization Ratio Under Different Working Conditions of Blast Furnace Based on Extreme Learning Machine

被引：1

作者：

Ma, Tianen ^{[1
]}

An, Jianqi ^{[1
]}

机构：

[1] China Univ Geosci, Sch Automat, Minist Educ,Engn Res Ctr Earth Explorat Intellige, Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan, Peoples R China

来源：

2020 3RD INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTS (ICCR 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

blast furnace; fuzzy clustering; working condition classification; extreme learning machine; SYSTEM;

D O I：

10.1109/ICCR51572.2020.9344139

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

Since the working condition affects the chemical reactions in a blast furnace (BF), the relationship between the blast-supply parameters and the gas utilization ratio (GUR) varies with different working conditions. This paper presents a GUR prediction model based on working condition classification. First, this paper analyzes the influence of working conditions on the state parameters of a BF. Then, a fuzzy clustering method is used to identify the working condition by using some state parameters. Next, the GUR prediction model was designed based on an extreme learning machine under different working conditions. Finally, the simulation results verified the effectiveness of this method.

引用

页码：176 / 180

页数：5

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