Abstract
Cut-off grade and grade of crude ore is crucial to the economic benefit of enterprise and sustainable utilization of resource in mining system. Generally, they are determined by experiment data or worker’s experience, and they can’t be widely used. In this work, we use genetic algorithm and neural networks nesting method to simulate the highly complexity and non-linear relationship of mine system, to optimize the cut-off grade and grade of crude ore. The inner layer of nesting is neural networks, which is used to compute loss rate, amount of tailing ore and total cost; the outer layer is genetic algorithm, with cut-off grade and grade of crude ore as chromosome, which is used to get the revenue. These two layers carry out the optimization of cut-off grade and grade of crude ore jointly. Take Daye Iron Mine as a case, the result shows that: During the period of August to November in the year 2007, the optimal cut-off grade is 15.8%, and optimal grade of crude ore is 43.7762-44.1387%. Comparing with the present scheme (cut-off grade is 18%, grade of crude ore is 41-43%), the optimized scheme can improve the present value by 9.01-9.44 million Yuan.
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References
Xie, Y.L.: Optimization of Cut-off Grade in Open-pit Based on Control Theory. Tran. Nonferrous Metals Society of China 8, 353–356 (1998)
Dong, C.H.: Application of Ore Grade Optimization Method on Erfengshan Iron Mine. Metal Mine 4, 14–17 (2002)
Osanloo, M., Ataei, M.: Using Equivalent Grade Factors to Find the Optimum Cut-off Grades of Multiple Metal Deposits. Minerals Engineering 16, 771–776 (2003)
Mishra, B.: Development of a Computer Model for Determination of Cut off Grade for Metalliferous Deposits. J. Mines, Metals and Fuels 54, 147–152 (2006)
Bascetin., A.: Determination of Optimal Cut-off Grade Policy to Optimize NPV Using a New Approach with Optimization Factor. J. the South African Institute of Mining and Metallurgy. 107, 87–94 (2007)
Asad, M.W.A.: Optimum Cut-off Grade Policy for Open Pit Mining Operations through Net Present Value Algorithm Considering Metal Price and Cost Escalation. Engineering Computations 24, 723–736 (2007)
Holland, J.H.: Adaptation in Nature and Artificial Systems. The University of Michigan Press/MIT Press, Ann Arbor/Cambridge (1975)
McCulloch, W.S., Pitts, W.: A Logical Calculus of Ideas Immanent in Nervous Activity. Bull. Math. Biophys. 5, 15–33 (1943)
Cybenko, G.: Approximation by Superposition of Sigmoidal Functions. Math. Control. Signal Syst. 2, 303–314 (1989)
Rumelhart, D.E., Hinton, G., Williams, R.: Learning Internal Representations by Error Propagation. PDP Research Group. MIT Press, Cambridge (1986)
Martin, T.H., Howard, B.D., Mark, B.: Neural Network Design. PWS Publishing Company, New York (1996)
Xu., D.: System Analysis and Design Based on MATLAB——Neural Networks. Xidian Press, Xi’an (2002)
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He, Y., Xu, S., Zhu, K., Liu, T., Li, Y. (2008). A Genetic-Neural Method of Optimizing Cut-Off Grade and Grade of Crude Ore. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_67
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DOI: https://doi.org/10.1007/978-3-540-87734-9_67
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