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A hybrid intelligent optimization method for multiple metal grades optimization

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Abstract

One of the most important aspects of metal mine design is to determine the optimum cut-off grades and milling grades which relate to the economic efficiency of enterprises and the service life of mines. This paper proposes a hybrid intelligent framework which is based on stochastic simulations and regression, artificial neural network, and genetic algorithms is employed for grade optimization. Firstly, stochastic simulation and regression are used to simulate the uncertainty relations between cut-off grade and the loss rate. Secondly, BP and RBF network are applied to establish two complex relationships from the four variables of cut-off grade, milling grade, geological grade, and recoverable reserves to lost rate and total cost, respectively, in which, BP is used for the one of lost rate, and RBF is for the other. Meanwhile, the real-coding genetic algorithm is performed to search the optimal grades (cut-off grade and milling grade) and the weights of neural networks globally. Finally, the model has been applied to optimize grades of Daye Iron Mine. The results show there are 6. 6978 milling Yuan added compare to unoptimized grades.

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Acknowledgments

This research was fully supported by National Natural Science Foundation Grant No. 70573101 of the People’s Republic of China and Wuhan iron and steel (group) corp. the Research Foundation Outstanding Young Teachers, China University of Geosciences(Wuhan)CUGQNW0901, and the Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences(Wuhan).

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Authors and Affiliations

  1. School of Economics and Management, China University of Geosciences, 430074, Wuhan, China

    Shiwei Yu & Kejun Zhu

  2. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, 100181, Beijing, China

    Shiwei Yu

  3. School of Management, Guangdong University of Technology, 510520, Guangzhou, China

    Yong He

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  1. Shiwei Yu
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  2. Kejun Zhu
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  3. Yong He
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Correspondence to Shiwei Yu.

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Yu, S., Zhu, K. & He, Y. A hybrid intelligent optimization method for multiple metal grades optimization. Neural Comput & Applic 21, 1391–1402 (2012). https://doi.org/10.1007/s00521-011-0593-7

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  • DOI: https://doi.org/10.1007/s00521-011-0593-7

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