期刊
NEURAL COMPUTING & APPLICATIONS
卷 21, 期 6, 页码 1391-1402出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-011-0593-7
关键词
Multiple metal grades; Cut-off grade; Hybrid intelligent; Artificial neural networks; Genetic algorithms; Optimization
资金
- National Natural Science Foundation of the People's Republic of China [70573101]
- Wuhan iron and steel (group) corp.
- Research Foundation Outstanding Young Teachers, China University of Geosciences(Wuhan) [CUGQNW0901]
- China University of Geosciences(Wuhan)
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据