ISSN 2594-357X
48° Seminário de Redução de Minérios e Matérias-primas — vol. 48, num.48 (2018)
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Blast furnace conditions are influenced by hundreds of raw materials and operation-related factors in a very complicated way. The traditional operation of a blast furnace relies on experience, but when there are many factors changing frequently and simultaneously, there is a coupling effect among the factors. The blast furnace conditions are likely to fluctuate or even become abnormal, and it is difficult to find the right way out relying on people experience. In this paper, Big Data technology is used to analyze the matching relationship between raw materials, operation and blast furnace conditions. Cluster analysis, principal component analysis, regression analysis and other Big Data analysis are carried out to find out the rules by which the raw materials and operation-related factors influence the blast furnace conditions, and the orientation to which the blast furnace operation optimization shall be adjusted. It has been applied to a 5000m3 class blast furnace in China. After the optimization, the blast furnace has been operating with a coefficient of utilization of the blast furnace of 2.2t/m3d increased from 1.2t/m3d, fuel ratio 525kg/t decreased from 580kg/t, out of the abnormal conditions, with good production targets.
Blast furnace conditions are influenced by hundreds of raw materials and operation-related factors in a very complicated way. The traditional operation of a blast furnace relies on experience, but when there are many factors changing frequently and simultaneously, there is a coupling effect among the factors. The blast furnace conditions are likely to fluctuate or even become abnormal, and it is difficult to find the right way out relying on people experience. In this paper, Big Data technology is used to analyze the matching relationship between raw materials, operation and blast furnace conditions. Cluster analysis, principal component analysis, regression analysis and other Big Data analysis are carried out to find out the rules by which the raw materials and operation-related factors influence the blast furnace conditions, and the orientation to which the blast furnace operation optimization shall be adjusted. It has been applied to a 5000m3 class blast furnace in China. After the optimization, the blast furnace has been operating with a coefficient of utilization of the blast furnace of 2.2t/m3d increased from 1.2t/m3d, fuel ratio 525kg/t decreased from 580kg/t, out of the abnormal conditions, with good production targets.
Palavras-chave
big data; blast furnace; operation optimization
big data; blast furnace; operation optimizationbig data; blast furnace; operation optimization
Como citar
Gang, Wang;
Jing-Song, Wang;
Hao, Xie;
Xin, Yan.
APPLICATION OF BIG DATA IN OPTIMIZATION OF BLAST FURNACE OPERATION
,
p. 209-216.
In: 48° Seminário de Redução de Minérios e Matérias-primas,
São Paulo,
2018.
ISSN: 2594-357X
, DOI 10.5151/2594-357X-31678
