Practice of Artificial Intelligence for Iron Ore Pelletizing System

In the process of ore powder pelletizing, due to the thermal agglomeration of ore, the chemical reaction between minerals and other minerals reduces its solubility. Therefore, it is necessary to adjust and reform the structure of iron ore powder pellet system. The iron ore powder production line is also constantly innovating and improving, and has achieved remarkable results. At present, the production technology of ore powder pellets has become mature. This paper mainly uses the methods of experimental analysis and data comparison to explore the relevant factors of realizing the iron ore powder pelletizing system by using the visual graph. The experimental data shows that in order to reach the thickness of 2900N, the temperature must be raised to the required height.

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Publication Details

Published
2024-10-04
DOI
https://doi.org/10.1109/gcat62922.2024.10923946
Primary Topic
Mineral Processing and Grinding
Type
article
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article

Practice of Artificial Intelligence for Iron Ore Pelletizing System

Fan Honpeng, Gu Runlong
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Mineral Processing and Grinding
article

Practice of Artificial Intelligence for Iron Ore Pelletizing System

Fan Honpeng, Gu Runlong
article en

Abstract

In the process of ore powder pelletizing, due to the thermal agglomeration of ore, the chemical reaction between minerals and other minerals reduces its solubility. Therefore, it is necessary to adjust and reform the structure of iron ore powder pellet system. The iron ore powder production line is also constantly innovating and improving, and has achieved remarkable results. At present, the production technology of ore powder pellets has become mature. This paper mainly uses the methods of experimental analysis and data comparison to explore the relevant factors of realizing the iron ore powder pelletizing system by using the visual graph. The experimental data shows that in order to reach the thickness of 2900N, the temperature must be raised to the required height.

Openalex Percentile: Top 72%
Mineral Processing and Grinding
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Practice of Artificial Intelligence for Iron Ore Pelletizing System — Fan Honpeng, Gu Runlong · (2024) | TGRS Research Map | TGRS