Optimizing Extreme Learning Machine Algorithm using Particle Swarm Optimization to Estimate Iron Ore Grade

Fathi, M.; Alimoradi, A.; Hemati Ahooi, H.R.

doi:10.22044/jme.2021.10368.1984

Document Type : Original Research Paper

Authors

Department of Mining Engineering, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

https://doi.org/10.22044/jme.2021.10368.1984

Abstract

Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. This paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine and the particle swarm optimization approaches, and is designed based on the location of the boreholes, depth of the boreholes, and drill hole information from an orebody, and applied for the ore grade estimation on the basis of a block model. In this work, the two algorithms of optimization clustering and neural networks are used for the iron grade estimation in the Choghart iron ore north anomaly in the central Iran. The results of the training and testing the algorithms indicate a significant ability of the optimized neural network system in the ore grade estimation.

Keywords

20.1001.1.22518592.2021.12.2.8.5

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Optimizing Extreme Learning Machine Algorithm using Particle Swarm Optimization to Estimate Iron Ore Grade

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References

Volume 12, Issue 2April 2021Pages 397-411

Volume 12, Issue 2
April 2021
Pages 397-411