Document Type : Original Research Paper


1 Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran

2 School of Mining, College of Engineering, University of Tehran, Tehran, Iran


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.


[1]. Blowes, DW., Ptacek, CJ., Jambor, JL., Weisener, CG., Paktunc, D., Gould, WD. and Johnson, DB. (2003). The geochemistry of acid mine drainage. 10.1016/B978-0-08-095975-7.00905-0
[2]. Buckley, AN. and Woods, RW. (1987). The surface oxidation of pyrite. Applied Surface Science, 27: 437–452.
[3]. Brown, AD. and Jurinak, JJ. (1989). Pyrite oxidation in aqueous mixtures. Journal of Environmental Quality 18: 545–550.
[4]. Wiersma, CL. and Rimstidt, JD. (1984). Rates of reaction of pyrite and marcasite with ferric iron at pH 2. Geochimica et Cosmochimica Acta 48: 85–92.
[5]. Sasaki, K., Tsunekawa, M., Ohtsuka, T. and Konno, H. (1995). Confirmation of a sulfur-rich layer on pyrite after oxidative dissolution by Fe(III) ions around pH 2. Geochimica et Cosmochimica Acta 59: 3155–3158.
[6]. Rimstidt, JD., Chermak, JA. and Gagen, PM., (1994). Rates of reaction of galena, sphalerite, chalcopyrite and arsenopyrite. In: Alpers CN and Blowes DW (eds.) Environmental Geochemistry of Sulfide Oxidation, vol. 550, pp. 2–13. Washington, DC: American Chemical Society.
[7]. Ferguson K.D. and Erickson P.M., (1988). Pre-Mine Prediction of Acid Mine Drainage. In: Salomons W., Förstner U. (eds) Environmental Management of Solid Waste. Springer, Berlin, Heidelberg.
[8]. Rooki, R., Doulati Ardejani, F., Aryafar, A. and Asadi, AB. (2011). Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine, Southeast Iran. Environmental Earth Sciences. 64 (5): 1303-1316.
[9] Aryafar, A., Gholami, R., Rooki, R. and Doulati Ardejani, F. (2012). Heavy metal pollution assessment using support vector machine in the Shur River, Sarcheshmeh copper mine, Iran. Environmental Earth sciences, 67(4): 1191-1199.
[10]. Doulati Ardejani, F., Rooki, R., Jodieri Shokri, B., Eslam Kish, T., Aryafar, A. and Tourani, P. (2012). Prediction of rare earth elements in neutral alkaline mine drainage from Razi coal mine, Golestan Province, northeast Iran, using general regression neural network. Journal of Environmental Engineering. 139 (6): 896-907.
[11]. Sadeghiamirshahidi, M. and Doulati Ardejani, F., (2013). Application of artificial neural networks to predict pyrite oxidation in a coal washing refuse pile. Fuel, 104: 163-169.
[12] Bouzahzah, H., Benzaazoua, M. and Bussiere, B. (2014). Prediction of Acid Mine Drainage: Importance of Mineralogy and the Test Protocols for Static and Kinetic Tests. Mine Water and the Environment 33: 54–65.
[13]. Jodeiri Shokri, B., Ramazi, H., Doulati Ardejani, F. and Sadeghiamirshahidi, M. (2014). Prediction of pyrite oxidation in a coal washing waste pile applying artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS). Mine Water and the Environment. 33 (2): 146-156.
[14]. Jodeiri Shokri, B., Ramazi, H., Doulati Ardejani, F. and Moradzadeh, A. (2014). A statistical model to relate pyrite oxidation and oxygen transport within a coal waste pile: case study, Alborz Sharghi, northeast of Iran. Environmental Earth sciences. 71 (11): 4693-4702.
[15]. Bahrami, S. and Doulati Ardejani, F. (2016). Prediction of pyrite oxidation in a coal washing waste pile using a hybrid method, coupling artificial neural networks and simulated annealing (ANN/SA). Journal of Cleaner Production, 137: 1129-1137.
[16]. Dold, Bernhard. (2017). Acid rock drainage prediction: A critical review. Journal of Geochemical Exploration 172: 120-132.
[17]. Balci, N. and Demirel, C. (2018). Prediction of acid mine drainage (AMD) and metal release sources at the Küre Copper Mine Site, Kastamonu, NW Turkey. Mine Water and the Environment. 37 (1): 56-74.
[18]. Hadadi, F., Jodeiri Shokri, B, Zare Naghadehi, M., Doulati Ardejani, F., (2020). Probabilistic prediction of acid mine drainage generation risk based on pyrite oxidation process in coal washery rejects-A Case Study. Journal of Mining and Environment.  10.22044/jme.2020.9609.1873
[19]. Sebogodi, KR., Johakimu, JK., B. and Sithole, B. (2020). Beneficiation of pulp mill waste green liquor dregs: Applications in treatment of acid mine drainage as new disposal solution in South Africa. Journal of Cleaner Production 246: 118979.
[20]. Luis, P, Beltran, L., Scharwz, A., Cristina Ruiz, M. and Borquez, R. (2020). Optimization of nanofiltration for treatment of acid mine drainage and copper recovery by solvent extraction. Hydrometallurgy, 195: 105361
[21]. Zhou, Y., Zhou, W., Lu, X., Jiskani, IM., Cai, Q., Liu, P. and Li, L. (2020). Evaluation Index System of Green Surface Mining in China. Mining, Metallurgy & Exploration 37: 1093–1103.
[22]. Chen, J., Jiskani IM., Jinliang, C. and Yan H. (2020). Evaluation and future framework of green mine construction in China based on the DPSIR model. Sustainable Environment Research. 30 (1): 1-10.
[23]. Behnia, D. and Shahriar, K. (2015). Prediction of Tunnelling-induced Settlement Using Gene Expression Programming. American Rock Mechanics Association. 49th U.S. Rock Mechanics/Geomechanics Symposium, 28 June-1 July, San Francisco, California.
[24]. Johari, A., Hooshm and Nejad, A. (2015). Prediction of soil-water characteristic curve using gene expression programming. IJST, Transactions of Civil Engineering, 39, 143-165.
[25]. Shirani Faradonbeh, R., Hasanipanah, M., Amnieh, H.B., Armaghani, D.J. and Monjezi, M. (2018). Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environmental Monitoring and Assessments, 190,351.
[26]. Thneibat, M. and Tarawneh, B. (2019). Genetic expression programming model for selecting the appropriate ground improvement technique. Journal of Engineering and Applied Sciences. 14 (20): 3469-3480.
[27]. Hajihassani, M., Abdullah, S.S., Asteris, P.G. and Armaghani, D.J. (2019) A Gene Expression Programming Model for Predicting Tunnel Convergence. Appl. Sci, 9, 4650.
[28]. Jannesar Malakooti, S., Shafaei Tonkaboni., SZ. Noaparast, M., Doulati Ardejani, F. and Naseh, R. (2014) Characterization of the Sarcheshmeh copper mine tailings, Kerman province, southeast of Iran. Environmental Earth Sciences., 71: 2267-2291.
[29]. Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027.‏
[30]. Ferreira, C. (2006). Gene expression programming: mathematical modelling by an artificial intelligence (Vol. 21). pp. 55-56. Springer.‏