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Machine-Learning-Aided Determination of Post-blast Ore Boundary for Controlling Ore Loss and Dilution

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Abstract

In the process of open-pit bench blasting for many mines, rock fragments move in the direction of loose space after fragmentation under explosive energy, leading to ore distribution differences before and after blasting. Considering that there is no simple and inexpensive method for post-blast ore boundary determination, a machine-learning-aided determination method and a corresponding evaluation system are proposed. In this regard, 95 datasets with nine variables were investigated using different kinds of predictive models: support vector regression, the Gaussian process (GP), extreme learning machine, and two metaheuristic algorithms. By evaluating the predictive performance using three performance metrics, absolute error comparison, and Taylor diagram, a hybrid model composed of whale optimization algorithm (WOA) and GP, namely WOA–GP, was found to be the best precision model. Then, a blast block with 55 blast holes was assumed and was analyzed using the WOA–GP model and the established evaluation system. It was found that using the post-blast ore boundary to guide the shovel can decrease the ore loss rate by 33.1% and the ore dilution rate by 57.9%, meaning that the methodology has great significance for improving resource recovery and increasing economic benefits. In addition, the mixing of high-grade and low-grade rock fragments during the geological modeling process may also cause ore loss and dilution. This article provides a new methodology for post-blast ore boundary determination, which can inspire development of other ore loss and dilution management techniques.

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Notes

  1. 1 mile = 1.60934 km.

  2. 1 lb = 0.453592 kg.

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Acknowledgments

This study is supported by the National Natural Science Foundation Project of China (Grant Nos. 51874350 and 41807259), the National Key R&D Program of China (2017YFC0602902), the Fundamental Research Funds for the Central Universities of Central South University (2018zzts217), and the Innovation-Driven Project of Central South University (2020CX040).

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  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, Hunan, China

    Zhi Yu, Xiuzhi Shi, Jian Zhou, Yonggang Gou, Dijun Rao & Xiaofeng Huo

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  1. Zhi Yu
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  2. Xiuzhi Shi
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  3. Jian Zhou
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Correspondence to Yonggang Gou.

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Yu, Z., Shi, X., Zhou, J. et al. Machine-Learning-Aided Determination of Post-blast Ore Boundary for Controlling Ore Loss and Dilution. Nat Resour Res 30, 4063–4078 (2021). https://doi.org/10.1007/s11053-021-09914-5

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