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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM14486

Iron ore Quality prediction Using Machine Learning

Stream/Area:

-

Subject:

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Paper ID:

10.55041/IJSREM14486

Volume:

6 |

6 |

Jun 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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10.55041/IJSREM14486

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

CKGS

Chetan Kumar G S

KKM

Kavana K M

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Abstract

The main goal of this project is to predict how much impurity is in the ore concentrate. The% of Silica is measured in a lab experiment it takes at least one hour for the process engineers to have this value. As this impurity is measured every hour, if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers, giving them early information to take actions (empowering!). Hence, they will be able to take corrective actions in advance (reduce impurity, if it is the case) and also help the environment (reducing the amount of ore that goes to tailings as you reduce silica in the ore concentrate).

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Cite This Article

Chetan Kumar G S, Kavana K M, "Iron ore Quality prediction Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 6, Jun 2022. https://doi.org/10.55041/IJSREM14486

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11/5/2022

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The authors declare no conflict of interest related to this research work.

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