采用人工神经网络基因复合算法预测氧化铜矿柱浸工艺铜浸出率（英文）

发布时间：2018-02-26 05:35

本文关键词： 浸出氧化铜矿浸出率人工神经网络基因算法　出处：《Transactions of Nonferrous Metals Society of China》2017年03期 　论文类型：期刊论文

【摘要】：采用人工神经网络(ANN)以及人工神经网络和基因复合(GANN)算法来优化氧化铜矿柱浸工艺参数。采用三种高度的浸矿柱(2,4,6 m)和尺寸为25.4 mm和50.8 mm的两种矿物来进行浸出实验。在台架实验规模下,对浸矿柱高度、矿粒尺寸、硫酸流速、浸出时间等工艺参数对铜浸出率的影响进行研究,对浸出条件进行优化以得到最大的浸出率。研究结果表明,铜的浸出率随硫酸流速和浸出时间的增加而增加,随矿粒尺寸和浸矿柱高度的减小而增加。对人工神经网络(ANN)、人工神经网络和基因复合(GANN)算法的效率进行了比较,结果表明,人工神经网络和基因复合(GANN)算法比人工神经网络(ANN)算法更有效。采用新提出的算法模型来预测铜的浸出率误差更低。
[Abstract]:Using artificial neural network (ANN) and artificial neural network and genetic algorithm to optimize the composite (GANN) copper oxide column leaching process parameters. By using three kinds of high dip (2,4,6 m) and the pillar size of two kinds of minerals and 50.8 mm to 25.4 mm leaching experiments were conducted in bench scale experiments. Under the dip pillar height, particle size, flow rate of sulfuric acid leaching time, process parameters to study the effects of copper leaching rate, the leaching conditions were optimized to get the maximum leaching rate. The results show that the leaching rate of copper sulfate leaching time and flow velocity increased with the increase of particle size and decreases, with leaching the height of the pillar increases. The artificial neural network (ANN), artificial neural network and genetic algorithm (GANN) composite efficiency were compared. The results show that the artificial neural network and genetic algorithm is better than the composite (GANN) artificial neural network (ANN) algorithm is more effective. The new algorithm model is used to predict the lower error of copper leaching rate.

【作者单位】： Department
【分类号】：TF811

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