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.
The artificial neural network (ANN) and hybrid of artificial neural network and genetic algorithm (GANN) were applied to predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data. The leaching experiments were performed in three columns with the heights of 2, 4 and 6 m and in particle size of <25.4 and <50.8 mm. The effects of different operating parameters such as column height, particle size, acid flow rate and leaching time were studied to optimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale. It was found that the recovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height. The efficiency of GANN and ANN algorithms was compared with each other. The results showed that GANN is more efficient than ANN in predicting copper recovery. The proposed model can be used to predict the Cu recovery with a reasonable error.
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