Prediction of the Hot Metal Silicon Content in the Blast Furnace

被引：0

作者：

Beskardes, Ahmet ^{[1
]}

Turkoglu, Soner ^{[2
]}

Aci, Cigdem ^{[3
]}

机构：

[1] Iskenderun Demir Celik AS, Elekt Otomasyon Muldurlugu, Antakya, Turkey

[2] Cukurova Univ, Bilgisayar Muh Bolumu, Adana, Turkey

[3] Mersin Univ, Bilgisayar Muh Bolumu, Mersin, Turkey

来源：

2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU) | 2016年

关键词：

Blast furnace; artificial neural network; multilayer perceptron; hot metal silicon content;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Transforming of raw iron ore to liquid hot metal is operated at blast furnace which is one of the main unit of integrated iron and steel factories. Silicon content of liquid hot metal is the most important parameter concerning of product quality and blast furnace thermal condition. In this study a prediction model is established with artificial neural network's multilayer perceptron module by using 564 heat data of Iskenderun Iron & Steel Plant (ISDEMIR) Blast Furnace No 3. The silicon content of the next heat is predicted with accuracy of 83%.

引用

页码：709 / 712

页数：4

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