Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine, Southeast Iran

This study investigated the use of brewing wastewater (BW) as the primary carbon source in the Postgate medium for the optimisation of sulphate reduction in acid mine drainage (AMD). The results showed that the sulphate-reducing bacteria (SRB) consortium was able to utilise BW for sulphate reduction. The response surface methodology (RSM)/Box–Behnken design optimum conditions found for sulphate reduction were a pH of 6.99, COD/SO42− of 2.87, and BW concentration of 200.24 mg/L with predicted sulphate reduction of 91.58%. Furthermore, by using an artificial neural network (ANN), a multilayer full feedforward (MFFF) connection with an incremental backpropagation network and hyperbolic tangent as the transfer function gave the best predictive model for sulphate reduction. The ANN optimum conditions were a pH of 6.99, COD/SO42− of 0.50, and BW concentration of 200.31 mg/L with predicted sulphate reduction of 89.56%. The coefficient of determination (R2) and absolute average deviation (AAD) were estimated as 0.97 and 0.046, respectively, for RSM and 0.99 and 0.011, respectively, for ANN. Consequently, ANN was a better predictor than RSM. This study revealed that the exclusive use of BW without supplementation with refined carbon sources in the Postgate medium is feasible and could ensure the economic sustainability of biological sulphate reduction in the South African environment, or in any semi-arid country with significant brewing activity and AMD challenges.

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Using Artificial Neural Network to Model Water Discharge and Chemistry in a River Impacted by Acid Mine Drainage

American Journal of Water Resources ◽

10.12691/ajwr-9-2-4 ◽

2021 ◽

Vol 9 (2) ◽

pp. 63-79

Author(s):

Toluwaleke Ajayi ◽

Dina L.Lopez ◽

Abiodun E.Ayo-Bali

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Acid Mine Drainage ◽

Mine Drainage ◽

Water Discharge ◽

Acid Mine ◽

Artificial Neural ◽

Model Water

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Study of acid mine drainage production potential in flotation tailings of Sarcheshmeh porphyry copper mine

Arabian Journal of Geosciences ◽

10.1007/s12517-014-1761-8 ◽

2015 ◽

Vol 8 (10) ◽

pp. 8229-8236 ◽

Cited By ~ 3

Author(s):

Solmaz Esmaeily Shoja ◽

Mohammad Mehdi Salari

Keyword(s):

Acid Mine Drainage ◽

Mine Drainage ◽

Porphyry Copper ◽

Production Potential ◽

Flotation Tailings ◽

Copper Mine ◽

Acid Mine

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

Water Science & Technology ◽

10.2166/wst.2021.494 ◽

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.

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Mechanisms and Remediation Technologies of Sulfate Removal from Acid Mine Drainage

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.610-613.3252 ◽

2012 ◽

Vol 610-613 ◽

pp. 3252-3256

Author(s):

Mei Qin Chen ◽

Feng Ji Wu

Keyword(s):

Heavy Metals ◽

Acid Mine Drainage ◽

Mine Drainage ◽

Metal Corrosion ◽

Sulfate Ion ◽

High Concentration ◽

Biological Reduction ◽

Acid Mine ◽

Sulfate Removal ◽

Co Precipitation

Acid mine drainage (AMD) has properties of extreme acidification, quantities of sulfate and elevated levels of soluble heavy metals. It was a widespread environmental problem that caused adverse effects to the qualities of ground water and surface water. In the past decades, most of investigations were focused on the heavy metals as their toxicities for human and animals. As another main constitution of AMD, sulfate ion is nontoxic, yet high concentration of sulfate ion can cause many problems such as soil acidification, metal corrosion and health problems. More attention should be paid on the sulfate ion when people focus on the AMD. In the paper, sulfate removal mechanisms include adsorption, precipitation, co-precipitation and biological reduction were analyzed and summarized. Meanwhile, the remediation technologies, especially the applications of them in China were also presented and discussed.

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