An ecological risk assessment for a South African acid mine drainage

1999 ◽  
Vol 39 (10-11) ◽  
Keyword(s):  
Risk Assessment ◽  
Acid Mine Drainage ◽  
South African ◽  
Ecological Risk ◽  
Ecological Risk Assessment ◽  
Mine Drainage ◽  
Acid Mine

Toxicity and potential risk assessment of a river polluted by acid mine drainage in the Iberian Pyrite Belt (SW Spain)

2011 ◽  
Vol 409 (22) ◽  
pp. 4763-4771 ◽  
Author(s):  
Aguasanta M. Sarmiento ◽  
Angel DelValls ◽  
José Miguel Nieto ◽  
María José Salamanca ◽  
Manuel A. Caraballo
Keyword(s):  
Risk Assessment ◽  
Acid Mine Drainage ◽  
Potential Risk ◽  
Mine Drainage ◽  
Iberian Pyrite Belt ◽  
Sw Spain ◽  
Acid Mine

scholarly journals Application of neural network techniques to predict the heavy metals in acid mine drainage from South African mines

2021 ◽  
Author(s):  
John Kabuba ◽  
Andani Valentia Maliehe
Keyword(s):  
Neural Network ◽  
Heavy Metals ◽  
Transfer Function ◽  
Acid Mine Drainage ◽  
South African ◽  
Mine Drainage ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Output Layer ◽  
Acid Mine

Abstract Acid Mine Drainage (AMD) is the formation and movement of highly acid water rich in heavy metals. Prediction of heavy metals in the AMD is important in developing any appropriate remediation strategy. This paper attempts to predict heavy metals in the AMD (Zn, Fe, Mn, Si and Ni) from South African mines using Neural Network (NN) techniques. The Backpropagation (BP) neural network model has three layers with the input layer (pH, SO42− and TDS) and output layer (Cu, Fe, Mn and Zn). After BP training, the NN techniques were able to predict heavy metals in AMD with a tangent sigmoid transfer function (tansig) at hidden layer with 5 neurons and linear transfer function (purelin) at output layer. The Levenberg-Marquardt back-propagation (trainlm) algorithm was found as the best of 10 BP algorithms with mean-squared error (MSE) value of 0.00041 and coefficient of determination (R) for all (training, validation and test) value of 0.99984. The results indicate that NN can be considered as an easy and cost-effective technique to predict heavy metals in the AMD.

Efficiency of ball milled South African bentonite clay for remediation of acid mine drainage

2015 ◽  
Vol 8 ◽  
pp. 227-240 ◽  
Author(s):  
Vhahangwele Masindi ◽  
Mugera W. Gitari ◽  
Hlanganani Tutu ◽  
Marinda DeBeer
Keyword(s):  
Acid Mine Drainage ◽  
South African ◽  
Mine Drainage ◽  
Bentonite Clay ◽  
Acid Mine
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