Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine,Southeast Iran |
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| Authors: | R Rooki F Doulati Ardejani A Aryafar A Bani Asadi |
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| Institution: | (1) Mining Engineering, Shahrood University of Technology, Shahrood, Iran;(2) Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran;(3) Department of Mining Engineering, Faculty of Engineering, Birjand University, Birjand, Iran;(4) Environmental Geology, Faculty of Geosciences, Shahrood University of Technology, Shahrood, Iran; |
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| Abstract: | Water is one of the basic and fundamental requirements for the survival of human beings. Mining of the sulphide mines usually
produce a significant amount of acid mine drainage (AMD) contributing to huge amounts of chemical components and heavy metals
in the receiving waters. Prediction of the heavy metals in the AMD is important in developing any appropriate remediation
strategy. This paper attempts to predict heavy metals (Cu, Fe, Mn, Zn) from the AMD using backpropagation neural network (BPNN),
general regression neural network (GRNN) and multiple linear regression (MLR), by taking pH, sulphate (SO4) and magnesium (Mg) concentrations in the AMD into account in Shur River, Sarcheshmeh porphyry copper deposit, southeast
Iran. The comparison between the predicted concentrations and the measured data resulted in the correlation coefficients,
R, 0.92, 0.22, 0.92 and 0.92 for Cu, Fe, Mn and Zn ions using BPNN method. Moreover, the R values were 0.89, 0.37, 0.9 and 0.91 for Cu, Fe, Mn, and Zn taking the GRNN method into consideration. However, the correlation
coefficients were low for the results predicted by MLR method (0.83, 0.14, 0.9 and 0.85 for Cu, Fe, Mn and Zn ions, respectively).
The results further indicate that the ANN can be used as a viable method to rapidly and cost-effectively predict heavy metals
in the AMD. The results obtained from this paper can be considered as an easy and cost-effective method to monitor groundwater
and surface water affected by AMD. |
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