关键词: BP神经网络, 遗传算法, 全尾砂, 絮凝沉降, 沉降速度

Abstract: In order to get the optimum parameters of flocculating sedimentation， back propagation neural network and genetic algorithm were applied to establish the flocculation sedimentation parameters prediction model of the crude tailings. The flocculating agent and tailings concentration consumption were used as the input data and the sedimentation speed was confirmed to be the output data. The learning and training samples were received by the orthogonal experiments to build neural network prediction model. Then， the optimum parameters of flocculating sedimentation were received after using genetic algorithm finding optimal in parameters prediction model of the crude tailings. The selected parameters prediction model was used in Hemushan iron mine. The results showed that the flocculating agent consumption is 12g/t and tailings concentration is 17%， the sedimentation speed is 1.31m/h， these parameters meet the production requirements. The flocculating agent consumption required is 20% less than the original production when using these flocculating sedimentation parameters. The application of the model indicates that it provides a new method to optimize the flocculating sedimentation parameters with a good effect.

Key words: back propagation neural network, genetic algorithm, crude tailings, flocculating sedimentation, sedimentation velocity