Review of machine learning based mineral ore grade estimations
DOI:
https://doi.org/10.17159/Abstract
Mineral ore grades estimation plays a crucial role on the profitability of the future mining operations. The conventional statistical methods used for ore grade estimation require expertise and clean data to build accurate models. However, the statistical models are extremely sensitive to change in data and would have to be rebuilt on newly acquired data with different characteristics which has proved to be a time-consuming process. Machine learning methods have recently been proposed as an alternative to the statistical methods to alleviate the problems they suffer from in ore grade estimation. In this paper, a systematic literature review of machine learning methods used in ore grade estimation has been conducted on the studies published from 1990 to 2019. The types, performances, and capabilities of several machine learning methods have been evaluated and compared against each other as well as against the conventional statistical methods. The results based on 31 research studies show that the machine learning based methods have actually outperformed the conventional statistical methods in ore grade estimation. The review also shows that there is an active research on the application of machine learning to ore grade estimation and further improvements can be expected if advanced machine learning techniques are to be used.Downloads
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Copyright (c) 2024 Muhammad Ahsan Mahboob, Turgay Celik, Bekir Genc
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