Prediction of Acid Mine Drainage Generation Potential of A Copper Mine Tailings Using Gene Expression Programming-A Case Study

IF 1.1 Q3 MINING & MINERAL PROCESSING
B. J. Shokri, Hesam Dehghani, R. Shamsi, F. D. Ardejani
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引用次数: 2

Abstract

This work presents a quantitative predicting likely acid mine drainage (AMD) generation process throughout tailing particles resulting from the Sarcheshmeh copper mine in the south of Iran. Indeed, four predictive relationships for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH have been suggested by applying the gene expression programming (GEP) algorithms. For this, after gathering an appropriate database, some of the most significant parameters such as the tailing particle depths, initial remaining pyrite and chalcopyrite fractions, and concentrations of bicarbonate, nitrite, nitrate, and chloride are considered as the input data. Then 30% of the data is chosen as the training data randomly, while the validation data is included in 70% of the dataset. Subsequently, the relationships are proposed using GEP. The high values of correlation coefficients (0.92, 0.91, 0.86, and 0.89) as well as the low values of RMS errors (0.140, 0.014, 150.301, and 0.543) for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH prove that these relationships can be successfully validated. The results obtained also reveal that GEP can be applied as a new-fangled method in order to predict the AMD generation process.
基于基因表达编程的某铜矿尾矿酸性矿水生成潜力预测
本文提出了一种定量预测伊朗南部Sarcheshmeh铜矿尾矿颗粒可能产生酸性矿水(AMD)的过程。实际上,通过应用基因表达编程(GEP)算法,已经提出了剩余黄铁矿分数、剩余黄铜矿分数、硫酸盐浓度和pH的四种预测关系。为此,在收集适当的数据库后,将一些最重要的参数,如尾矿颗粒深度,初始剩余黄铁矿和黄铜矿分数,以及碳酸氢盐,亚硝酸盐,硝酸盐和氯化物的浓度作为输入数据。然后随机选择30%的数据作为训练数据,而验证数据则包含在70%的数据集中。随后,利用GEP提出了它们之间的关系。剩余黄铁矿、剩余黄铜矿、硫酸盐浓度和pH的相关系数较高(0.92、0.91、0.86、0.89),均方根误差较低(0.140、0.014、150.301、0.543),说明上述关系可以得到验证。结果还表明,GEP可以作为一种预测AMD生成过程的新方法。
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来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
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0
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