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Review

International Trends in Mining Tailings Research through Machine Learning Method: Retrospective or Prospective Oriented Research?

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References

  • Adams, A., A. Raman, and D. Hodgkins. 2013. How do the plants used in phytoremediation in constructed wetlands, a sustainable remediation strategy, perform in heavy-metal-contaminated mine sites? Water and Environment Journal 27 (3):373–86. doi:10.1111/j.1747-6593.2012.00357.x.
  • Adiansyah, J. S., M. Rosano, S. Vink, and G. Keir. 2015. A framework for a sustainable approach to mine tailings management: Disposal strategies. Journal of Cleaner Production 108:1050–62. doi:10.1016/j.jclepro.2015.07.139.
  • Agboola, O., D. E. Babatunde, O. S. Isaac Fayomi, E. R. Sadiku, P. Popoola, L. Moropeng, A. Yahaya, and O. A. Mamudu. 2020. A review on the impact of mining operation: Monitoring, assessment and management. Results in Engineering 8:100181. doi:10.1016/j.rineng.2020.100181.
  • Akbari, M., and T. N. A. Do. 2021. A systematic review of machine learning in logistics and supply chain management: Current trends and future directions. Benchmarking: An International Journal 28 (10):2977–3005. doi:10.1108/BIJ-10-2020-0514.
  • Álvarez-Ayuso, E., P. Abad-Valle, A. Murciego, and P. Villar-Alonso. 2016. Arsenic distribution in soils and rye plants of a cropland located in an abandoned mining area. Science of the Total Environment 542:238–46. doi:10.1016/j.scitotenv.2015.10.054.
  • Angelovičová, L., M. Lodenius, E. Tulisalo, and D. Fazekašová. 2014. Effect of heavy metals on soil enzyme activity at different field conditions in Middle Spis mining area (Slovakia). Bulletin of Environmental Contamination and Toxicology 93 (6):670–75. doi:10.1007/s00128-014-1397-0.
  • Armienta, M. A., O. Talavera, G. Villaseñor, E. Espinosa, I. Pérez-Martínez, O. Cruz, N. Ceniceros, and A. Aguayo. 2004. Environmental behaviour of metals from tailings in shallow rivers: Taxco, central Mexico. Applied Earth Science 113 (1):76–82. doi:10.1179/037174504225004510.
  • Atanassova, I., M. Bertin, and P. Mayr. 2019. Editorial: Mining scientific papers: NLP-enhanced bibliometrics. Frontiers in Research Metrics and Analytics 4:2. doi:10.3389/frma.2019.00002.
  • Aznar-Sánchez, J. A., J. J. García-Gómez, J. F. Velasco-Muñoz, and A. Carretero-Gómez. 2018. Mining waste and its sustainable management: Advances in worldwide research. Minerals 8 (7):284. doi:10.3390/min8070284.
  • Bachman, H. J., L. Elliott, S. Duong, L. Betancur, M. G. Navarro, E. Votruba-Drzal, and M. Libertus. 2020. Triangulating multi-method assessments of parental support for early math skills. Frontiers in Education. doi:10.3389/feduc.2020.589514.
  • Bao, Y., Z. Deng, Y. Wang, H. Kim, V. D. Armengol, F. Acevedo, N. Ouardaoui, C. Wang, G. Parmigiani, R. Barzilay, et al. 2019. Using machine learning and natural language processing to review and classify the medical literature on cancer susceptibility genes. JCO Clinical Cancer Informatics 3:1–9. doi:10.1200/CCI.19.00042.
  • Campos-Medina, F. 2019. Ecological modernization from the actor’s perspective: Spatio-temporality in the narratives about socio-ecological conflicts in Chile. Time & Society 28 (3):1239–71. doi:10.1177/0961463X17752284.
  • Carmo, F. F. D., L. H. Y. Kamino, R. T. Junior, I. C. D. Campos, F. F. D. Carmo, G. Silvino, K. J. D. S. X. D. Castro, M. L. Mauro, N. U. A. Rodrigues, M. P. D. S. Miranda, et al. 2017. Fundão tailings dam failures: The environment tragedy of the largest technological disaster of Brazilian mining in global context. Perspectives in Ecology and Conservation 15 (3):145–51. doi:10.1016/j.pecon.2017.06.002.
  • Carmo, F. F., A. O. Lanchotti, and L. H. Y. Kamino. 2020. Mining waste challenges: Environmental risks of gigatons of mud, dust and sediment in megadiverse regions in Brazil. Sustainability 12 (20):8466. doi:10.3390/su12208466.
  • Carneiro, T., R. V. Medeiros Da Nóbrega, T. Nepomuceno, G.-B. Bian, V. H. C. De Albuquerque, and P. P. R. Filho. 2018. Performance analysis of Google colaboratory as a tool for accelerating deep learning applications. IEEE Access 6:61677–85. doi:10.1109/ACCESS.2018.2874767.
  • Casadiego, E., A. G. Gutiérrez Bayona, M. Á. Herrera Lopez, and M. L. Páez Rojas. 2017. Strategic management of the production of sterile wastes of sustainable mining, using eco-efficient mining practices in Colombia. Revista de Investigación Agraria Y Ambiental 8 (2):107–18.
  • Chen, C., P. Chen, A. N. Belkacem, L. Lu, R. Xu, W. Tan, P. Li, Q. Gao, D. Shin, C. Wang, et al. 2020. Neural activities classification of left and right finger gestures during motor execution and motor imagery. Brain-Computer Interfaces 8 (4):117–27. doi:10.1080/2326263X.2020.1782124.
  • Corcho Alvarado, J. A., B. Balsiger, S. Röllin, A. Jakob, and M. Burger. 2014. Radioactive and chemical contamination of the water resources in the former uranium mining and milling sites of Mailuu Suu (Kyrgyzstan). Journal of Environmental Radioactivity 138:1–10. doi:10.1016/j.jenvrad.2014.07.018.
  • Darlington, S., J. Glanz, M. Andreoni, M. Bloch, S. Peçanha, A. Singhvi, and T. Griggs. 2019. Brumadinho dam collapse: A tidal wave of mud. The New York Times, February 9. https://www.nytimes.com/interactive/2019/02/09/world/americas/brazil-dam-collapse.html.
  • Davies, M. P. 2001. Impounded mine tailings: What are the failures telling us? CIM Bulletin 94. https://www.osti.gov/etdeweb/biblio/20196602.
  • Demková, L., J. Árvay, L. Bobuľská, M. Hauptvogl, and M. Hrstková. 2019. Open mining pits and heaps of waste material as the source of undesirable substances: Biomonitoring of air and soil pollution in former mining area (Dubnik, Slovakia). Environmental Science and Pollution Research International 26 (34):35227–39. doi:10.1007/s11356-019-06582-0.
  • Demková, L., L. Bobul’ská, J. Árvay, T. Jezný, and L. Ducsay. 2017. Biomonitoring of heavy metals contamination by mosses and lichens around Slovinky tailing pond (Slovakia). Journal of Environmental Science and Health. Part A, Toxic/Hazardous Substances & Environmental Engineering 52 (1):30–36. doi:10.1080/10934529.2016.1221220.
  • Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. ArXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805.
  • Du, F., and J.-G. Lu. 2021. Finite-time stability of fractional-order delayed Cohen–Grossberg memristive neural networks: A novel fractional-order delayed Gronwall inequality approach. International Journal of General Systems 1–27. doi:10.1080/03081079.2021.1985487.
  • Fedoročková, A., P. Raschman, G. Sučik, D. Ivánová, and J. Kavuličová. 2016. Utilization of chrysotile-type tailings for synthesis of high-grade silica by controlled precipitation. Mineral Processing and Extractive Metallurgy Review 37 (5):287–94. doi:10.1080/08827508.2016.1195738.
  • Fernández-Caliani, J. C., C. Barba-Brioso, I. González, and E. Galán. 2009. Heavy metal pollution in soils around the abandoned mine sites of the Iberian pyrite belt (Southwest Spain). Water, Air, and Soil Pollution 200 (1):211–26. doi:10.1007/s11270-008-9905-7.
  • Garmsiri, M. R., and A. Nosrati. 2019. Dewatering of copper flotation tailings: Effect of feed dilution on the thickener performance. Mineral Processing and Extractive Metallurgy Review 40 (2):141–47. doi:10.1080/08827508.2018.1497626.
  • Gavidia, P. 2019. Grandes catástrofes en las presas mineras. Tiempo.com | Meteored, February 17. https://www.tiempo.com/noticias/actualidad/las-catastrofes-de-las-presas-mineras.html.
  • Guthrie, M. D., A. J. Herrera, J. E. Downey, L. J. Brane, M. L. Boninger, and J. L. Collinger. 2021. The impact of distractions on intracortical brain–computer interface control of a robotic arm. Brain-Computer Interfaces 1–13. doi:10.1080/2326263X.2021.1980292.
  • Hatje, V., R. M. A. Pedreira, C. E. de Rezende, C. A. F. Schettini, G. C. de Souza, D. C. Marin, and P. C. Hackspacher. 2017. The environmental impacts of one of the largest tailing dam failures worldwide. Scientific Reports 7 (1):10706. doi:10.1038/s41598-017-11143-x.
  • Hernández-Plata, I., V. M. Rodríguez, E. Tovar-Sánchez, L. Carrizalez, P. Villalobos, M. S. Mendoza-Trejo, and P. Mussali-Galante. 2020. Metal brain bioaccumulation and neurobehavioral effects on the wild rodent Liomys irroratus inhabiting mine tailing areas. Environmental Science and Pollution Research International 27 (29):36330–49. doi:10.1007/s11356-020-09451-3.
  • Hoagland, P., S. Beaulieu, M. A. Tivey, R. G. Eggert, C. German, L. Glowka, and J. Lin. 2010. Deep-sea mining of seafloor massive sulfides. Marine Policy 34 (3):728–32. doi:10.1016/j.marpol.2009.12.001.
  • Huang, Z., Y. Qiu, and W. Sun. 2020. Recognition of motor imagery EEG patterns based on common feature analysis. Brain-Computer Interfaces 8 (4):128–36. doi:10.1080/2326263X.2020.1783170.
  • Hughes D J, Shimmield T M, Black K D and Howe J A. (2015). Ecological impacts of large-scale disposal of mining waste in the deep sea. Sci Rep, 5(1), 10.1038/srep09985
  • Hunter, J. D. 2007. Matplotlib: A 2D graphics environment. Computing in Science Engineering 9 (3):90–95. doi:10.1109/MCSE.2007.55.
  • Hussein, A. 2009. The use of triangulation in social sciences research: Can qualitative and quantitative methods be combined? Journal of Comparative Social Work 4 (1):106–17. doi:10.31265/JCSW.V4I1.48.
  • Ilyas, S., R. Chi, and J. Lee. 2013. Fungal bioleaching of metals from mine tailing. Mineral Processing and Extractive Metallurgy Review 34 (3):185–94. doi:10.1080/08827508.2011.623751.
  • Kan, X., Y. Dong, L. Feng, M. Zhou, and H. Hou. 2021. Contamination and health risk assessment of heavy metals in China’s lead–zinc mine tailings: A meta–analysis. Chemosphere 267:128909. doi:10.1016/j.chemosphere.2020.128909.
  • Karakaya, E., and C. Nuur. 2018. Social sciences and the mining sector: Some insights into recent research trends. Resources Policy 58:257–67. doi:10.1016/j.resourpol.2018.05.014.
  • Kochany, J., A. Lugowski, V. Menkal, and P. L. M. Erickson. 1996. Tailing pond remediation in the Canadian arctic. Environmental Technology 17 (10):1113–21. doi:10.1080/09593331708616480.
  • Kossoff, D., W. E. Dubbin, M. Alfredsson, S. J. Edwards, M. G. Macklin, and K. A. Hudson-Edwards. 2014. Mine tailings dams: Characteristics, failure, environmental impacts, and remediation. Applied Geochemistry 51:229–45. doi:10.1016/j.apgeochem.2014.09.010.
  • Kotsiantis, S. B., I. D. Zaharakis, and P. E. Pintelas. 2006. Machine learning: A review of classification and combining techniques. Artificial Intelligence Review 26 (3):159–90. doi:10.1007/s10462-007-9052-3.
  • Lamberti, J. 2015. De Canadá a Brasil, un año de grandes accidentes mineros en América, December 10. PODER. https://poderlatam.org/2015/12/de-canada-a-brasil-un-ano-de-grandes-accidentes-mineros-en-america/.
  • Liu, Y., W. Zhou, B. Gao, Z. Zheng, G. Chen, Q. Wei, and Y. He. 2021. Determination of radionuclide concentration and radiological hazard in soil and water near the uranium tailings reservoir in China. Environmental Pollutants and Bioavailability 33 (1):174–83. doi:10.1080/26395940.2021.1951123.
  • Mahmood, A. A., and M. Elektorowicz. 2015. A review of sustainable management of mine tailings. Applied Mechanics and Materials 773–774:1256–60. h ttp://1 0.4028/w ww.scientific.net/A MM.7 7 3-7 74.1 256.
  • Marín, O., J. O. Valderrama, A. Kraslawski, and L. A. Cisternas. 2021. Potential of tailing deposits in chile for the sequestration of carbon dioxide produced by power plants using ex-situ mineral carbonation. Minerals 11 (3):320. doi:10.3390/min11030320.
  • Marshall, I. J., and B. C. Wallace. 2019. Toward systematic review automation: A practical guide to using machine learning tools in research synthesis. Systematic Reviews 8 (1):163. doi:10.1186/s13643-019-1074-9.
  • Martín-Martín, A., E. Orduna-Malea, M. Thelwall, and E. D. López-Cózar. 2018. Google scholar, web of science, and scopus: A systematic comparison of citations in 252 subject categories. Journal of Informetrics 12 (4):1160–77. doi:10.1016/j.joi.2018.09.002.
  • Martin, R., K. Dowling, D. Pearce, J. Sillitoe, and S. Florentine. 2014. Health effects associated with inhalation of airborne arsenic arising from mining operations. Geosciences 4 (3):128–75. doi:10.3390/geosciences4030128.
  • Medvinsky, G., V. Caroca, and J. Vallejo. 2015. Informe sobre la situación de los Relaves Mineros en Chile para ser presentado en el cuarto informe periódico de Chile para el Comité de Derechos Económicos, Sociales y Culturales, perteneciente al consejo Económico Social de la Naciones Unidas. Fundación Relaves Chile y Fundación Terram. https://tbinternet.ohchr.org/Treaties/CESCR/Shared%20Documents/CHL/INT_CESCR_CSS_CHL_20605_S.pdf.
  • Memmott, T., A. Koçanaoğulları, M. Lawhead, D. Klee, S. Dudy, M. Fried-Oken, and B. Oken. 2020. BciPy: Brain-computer interface software in python. ArXiv:2002.06642 [Cs]. http://arxiv.org/abs/2002.06642.
  • Menezes, E. 2015. Lama contaminada tem concentração de metais até 1.300.000% acima do normal. R7.com, November 11. https://noticias.r7.com/minas-gerais/lama-contaminada-tem-concentracao-de-metais-ate-1300000-acima-do-normal-12112015.
  • National Service of Geology and Mining of Chile. 2021. Que es un depósito de relave. SERNAGEOMIN CHILE. https://www.sernageomin.cl/preguntas-frecuentes-sobre-relaves/.
  • Ngole-Jeme, V. M., and P. Fantke. 2017. Ecological and human health risks associated with abandoned gold mine tailings contaminated soil. PLoS ONE 12 (2):e0172517. doi:10.1371/journal.pone.0172517.
  • Nuijten, E. 2011. Combining research styles of the natural and social sciences in agricultural research. NJAS - Wageningen Journal of Life Sciences 57 (3):197–205. doi:10.1016/j.njas.2010.10.003.
  • Owen, J. R., D. Kemp, É. Lèbre, K. Svobodova, and G. Pérez Murillo. 2020. Catastrophic tailings dam failures and disaster risk disclosure. International Journal of Disaster Risk Reduction 42:101361. doi:10.1016/j.ijdrr.2019.101361.
  • Paraschiv, I. C., M. Dascalu, S. Trausan-Matu, and P. Dessus. 2015. Analyzing the semantic relatedness of paper abstracts: An application to the educational research field. 2015 20th International Conference on Control Systems and Computer Science 759–64. doi:10.1109/CSCS.2015.146.
  • Pashias, N., D. V. Boguer, K. J. Summers, and D. J. Glenister. 2000. A fifty cent rheometer for waste management of environmentally sensitive ore tailings. Mineral Processing and Extractive Metallurgy Review 20 (1):115–22. doi:10.1080/08827509908962466.
  • Ramos, W., C. Galarza, G. Ronceros, F. de Amat, M. Teran, L. Pichardo, D. Juarez, R. Anaya, A. Mayhua, J. Hurtado, et al. 2008. Noninfectious dermatological diseases associated with chronic exposure to mine tailings in a Peruvian district. The British Journal of Dermatology 159 (1):169–74. doi:10.1111/j.1365-2133.2008.08630.x.
  • Rao, F., and Q. Liu. 2015. Geopolymerization and its potential application in mine tailings consolidation: A review. Mineral Processing and Extractive Metallurgy Review 36 (6):399–409. doi:10.1080/08827508.2015.1055625.
  • Reback, J. J., W. McKinney, J. V. Den Bossche, T. Augspurger, P. Cloud, S. Hawkins, S. Gfyoung, M. Roeschke, A. Klein, T. Petersen, et al. 2021. Pandas-dev/pandas: Pandas 1.3.1. Zenodo. doi:10.5281/zenodo.5136416.
  • Schotten, M., M. El Aisati, W. J. N. Meester, S. Steiginga, and C. A. Ross. 2017. A brief history of scopus: The world’s largest abstract and citation database of scientific literature. In Research analytics. Florida: Auerbach Publications.
  • Shenoy, U. G., and K. Kutty. 2020. Pulmonary functions and respiratory symptoms of the women exposed to mine tailings. Indian Journal of Community Medicine 45 (3):311. doi:10.4103/ijcm.IJCM_313_19.
  • Sirkeci, A. A., A. Gül, G. Bulut, F. Arslan, G. Onal, and A. E. Yuce. 2006. Recovery of Co, Ni, and Cu from the tailings of divrigi iron ore concentrator. Mineral Processing and Extractive Metallurgy Review 27 (2):131–41. doi:10.1080/08827500600563343.
  • Sitharam, T. G., and A. Hegde. 2017. Stability analysis of rock-fill tailing dam: An Indian case study. International Journal of Geotechnical Engineering 11 (4):332–42. doi:10.1080/19386362.2016.1221574.
  • Song, M., L. Jiaping, J. Qian, L. Jianzhong, and S. Liang. 2016. Experimental study on utilization of quartz mill tailings as a filler to prepare geopolymer. Mineral Processing and Extractive Metallurgy Review 37 (5):311–22. doi:10.1080/08827508.2016.1218867.
  • Sousa, M. J., G. O. de Barros, and N. Tavares. 2022. Artificial intelligence trends: Insights for digital economy policymakers. In Information and knowledge in internet of things, ed. T. Guarda, S. Anwar, M. Leon, and F. J. M. Pinto, 163–86. New York: Springer International Publishing. doi:10.1007/978-3-030-75123-4_8.
  • Stovern, M., H. Guzmán, K. P. Rine, O. Felix, M. King, W. P. Ela, E. A. Betterton, and A. E. Sáez. 2016. Windblown dust deposition forecasting and spread of contamination around mine tailings. Atmosphere 7 (2):16. doi:10.3390/atmos7020016.
  • Su, M., H. Peng, and S. Li. 2021. A visualized bibliometric analysis of mapping research trends of machine learning in engineering (MLE). Expert Systems with Applications 186:115728. doi:10.1016/j.eswa.2021.115728.
  • Suthers, S. P., P. Pinto, V. Nunna, and A. V. Nguyen. 2019. Experimental study of dry desliming iron ore tailings by air classification. Mineral Processing and Extractive Metallurgy Review 40 (5):344–55. doi:10.1080/08827508.2019.1635470.
  • Tranfield, D., D. Denyer, and P. Smart. 2003. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management 14 (3):207–22. doi:10.1111/1467-8551.00375.
  • Tripathy, S. K., Y. R. Murthy, S. Farrokhpay, and L. O. Filippov. 2021. Design and analysis of dewatering circuits for a chromite processing plant tailing slurry. Mineral Processing and Extractive Metallurgy Review 42 (2):102–14. doi:10.1080/08827508.2019.1700983.
  • Tshitoyan, V., J. Dagdelen, L. Weston, A. Dunn, Z. Rong, O. Kononova, K. A. Persson, G. Ceder, and A. Jain. 2019. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571 (7763):95–98. doi:10.1038/s41586-019-1335-8.
  • Upmanyu, S., A. Upmanyu, A. Jamwal, and R. Agrawal. 2022. Machine learning in CAD/CAM: What we think we know so far and what we don’t. In Recent advances in industrial production, ed. R. Agrawal, J. K. Jain, V. S. Yadav, V. K. Manupati, and L. Varela, 495–507 978-981-16-5281-3 . Singapore: Springer. doi:10.1007/978-981-16-5281-3_48.
  • Van de Schoot, R., J. De Bruin, R. Schram, P. Zahedi, J. de Boer, F. Weijdema, B. Kramer, M. Huijts, M. Hoogerwerf, G. Ferdinands, et al. 2021. An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence 3 (2):125–33. doi:10.1038/s42256-020-00287-7.
  • Waskom, M. L. 2021. Seaborn: Statistical data visualization. Journal of Open Source Software 6 (60):1–4. doi:10.21105/joss.03021.
  • Witten, M. L., B. Chau, E. Sáez, S. Boitano, and R. C. Lantz. 2019. Early life inhalation exposure to mine tailings dust affects lung development. Toxicology and Applied Pharmacology 365:124–32. doi:10.1016/j.taap.2019.01.009.
  • Wolff, A. P., G. M. Da Costa, and F. De Castro Dutra. 2010. A comparative study of ultra-fine iron ore tailings from Brazil. Mineral Processing and Extractive Metallurgy Review 32 (1):47–59. doi:10.1080/08827508.2010.530718.
  • Yang, Z., Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q. V. Le. 2020. XLNet: Generalized autoregressive pretraining for language understanding. ArXiv:1906.08237 [Cs]. http://arxiv.org/abs/1906.08237.
  • Yohannessen , K., S. Alvarado, S. Mesias, J. Klarian, C. Silva, D. Vidal, and Da Cáceres. 2015. Exposure to fine particles by mine tailing and lung function effects in a panel of schoolchildren, Chañaral, Chile. Journal of Environmental Protection 6 (2):118–28. doi:10.4236/jep.2015.62014.
  • Yoo, K., K. Lee, H. Park, and J.-P. Wang. 2011. The extraction of arsenic from tailing using NaOH and NaHS. Geosystem Engineering 14 (4):165–68. doi:10.1080/12269328.2011.10541346.
  • Yu, Y. 2019 fAST: Flattening Abstract Syntax Trees for Efficiency 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Montreal, Canadá 25-31 May 2019 doi:10.1109/ICSE-Companion.2019.00113
  • Zhang H, Zeng J, Xie H, Guan C and Chen L. (2020). Enhanced separation for ilmenite tailings with a novel HGMS-flotation process. Separation Science and Technology, 55(4), 752–760. 10.1080/01496395.2019.1567546
  • Zhu, J., and Liu, W. 2020 A tale of two databases: the use of Web of Science and Scopus in academic papers Scientometrics 123 321–335 doi:10.1007/s11192-020-03387-8

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