关键词: 等效线性超挖经验图表, 矿石非计划贫化, BP神经网络, 预测模型, 相对误差

Abstract: In order to eliminate the limitation of equivalent linear overbreak slough （ELOS） empirical graph method in estimate unplanned ore dilution，BP neural network algorithm was applied to establish the three-layers BP neural network prediction model with 6 hidden layer neurons by considering the modified stable number of stope， hydraulic radius， average deviation of borehole and powder factor as input variables and ELOS of quantified unplanned ore dilution as output variables. The training and testing of model was performed by 120 group samples， the fitting degree of the prediction model was 0.987 42， and the mean square error was 9×10-5， the relative error of the forecast was about 6%.The prediction method of unplanned ore dilution was proposed.The calculation of unplanned ore dilution was applied in the test stope of Sandaoqiao Pb-Zn Mine， the calculation results showed that the calculated value of the unplanned ore dilution based on BP neural network was 0.717 m， and the relative error between the calculated results and the measured results 0.7 m was 2.4%， which was better than the results of empirical graph method and numerical simulation analysis （0.80 m and 0.55 m）， BP neural network model could be applied to predict the unplanned ore dilution in actual mines.

Key words: ELOS empirical graph, nnplanned ore dilution, BP neural network, prediction model, relative error