The model of prediction of Blast Furnace Gas Output

Author

Xiaoshan Zhao ; Ketai He ; Lan Yang ; Zhimin Lv

Author_Institution

Sch. of Mech. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China

fYear

2014

fDate

9-11 June 2014

Firstpage

1489

Lastpage

1492

Abstract

Because it is unavailable to predict the amount of Blast Furnace Gas Output (ABFGO) based on the principle of complex chemical reaction in iron and steel enterprises. It is necessary to select and analyze the factors which are relative to ABFGO. Those factors are ironworks production, clinker consumption, consumption of iron ore, Metallurgical coke consumption and blast volume. The paper deals with the data of the factors by principal components analysis. Then there are two principal components calculated from the factors. Using those two principals, a multiple linear regression model of the ABFGO is established. The model is proved to have a good effect on the prediction of ABFGO. Consequently, it is useful for steel plants to rightly distribute and fully use Blast Furnace Gas.

Keywords

blast furnaces; chemical reactions; coke; industrial plants; minerals; principal component analysis; regression analysis; steel industry; ABFGO; blast furnace gas output prediction model; blast volume; clinker consumption; complex chemical reaction; iron enterprises; iron ore consumption; ironworks production; metallurgical coke consumption; multiple linear regression model; principal component analysis; steel enterprises; steel plants; Blast furnaces; Iron; Linear regression; Predictive models; Principal component analysis; Production; Steel; The amount of Blast Furnace Gas Output; multiple linear regression; principal components analysis; style;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4799-4316-6

Type

conf

DOI

10.1109/ICIEA.2014.6931404

Filename

6931404