Systematic review on the integration of IOT in sustainable water management in mining
DOI:
https://doi.org/10.5281/zenodo.16741506Keywords:
Internet of Things, water management, mining 4.0Abstract
This article presents a systematic review in the context of environmental sustainability and mining 4.0 in the extractive industry, with the aim of analyzing the effectiveness of the Internet of Things (IoT) in sustainable water management. Based on a review of 63 scientific articles, 20 relevant studies were selected for a more exhaustive analysis. Key technologies such as smart sensors, microcontrollers, and cloud platforms were identified as fundamental for real-time monitoring and efficient control of water resources. The results show that the use of IoT significantly increases efficiency in water management and treatment, mainly through early detection of leaks. However, barriers to its implementation were identified, especially in rural contexts and small-scale mining operations, such as limited connectivity and a lack of technical capabilities in communities. The conclusion highlights the value of integrating emerging technologies, such as artificial intelligence and blockchain, to strengthen environmental decision-making, considering not only water but also air quality. It is recommended to promote field studies in real mines and advance the adoption of technological solutions adapted to diverse environments, especially in Latin America. This analysis provides a solid foundation for future research on the use of IoT in water management in the mining sector.
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Alshami, A., Ali, E., Elsayed, M., Eltoukhy, A. E. E., & Zayed, T. (2024). IoT innovations in sustainable water and wastewater management and water quality monitoring: A comprehensive review of advancements, implications, and future directions. IEEE Access, 12, 58427–58453. https://doi.org/10.1109/ACCESS.2024.3392573
Andrade, P., & Aguilar, J. (2021). Sistema de detección y predicción de la calidad del aire y del agua para el monitoreo y control ambiental en explotación minera usando componentes IoT [Tesis de licenciatura, Universidad Privada de Ciencias Aplicadas]. Repositorio Académico UPC. https://repositorioacademico.upc.edu.pe/handle/10757/657615
Basso Araya, C. E. (2024). Propuesta de implementación de un gemelo digital para la minería subterránea mediante la metodología BIM [Tesis de maestría, Universidad de Chile]. Repositorio UChile. https://repositorio.uchile.cl/handle/2250/203801
Bo, L., Liu, Y., Zhang, Z., Zhu, D., & Wang, Y. (2022). Research on an online monitoring system for efficient and accurate monitoring of mine water. IEEE Access, 10, 18743–18756. https://doi.org/10.1109/ACCESS.2022.3151244
Bothe-Fiekert, M., Binder, A., Nowosad, S. P., Apollo, F., & Langefeld, O. (2024). Lifecycle of mine water. En W. Frenz & A. Preuße (Eds.), Yearbook of sustainable smart mining and energy 2022. Technical, economic and legal framework (Vol. 2, pp. 67–92). Springer. https://doi.org/10.1007/978-3-031-41873-0_4
Casavilca, R., & Antonio, R. (2024). Prototipo de monitoreo remoto para el control del sistema de retorno de agua de infiltración de relave del sumidero en la Unidad Minera San Rafael [Tesis de licenciatura, Universidad Nacional de Huancavelica]. Repositorio UNH. https://repositorio.unh.edu.pe/items/0c16f234-5891-4368-a63d-f52c77d7b71c
Choubey, S., William, P., Pawar, A. B., Jawale, M. A., Gupta, K., & Parganiha, V. (2022). Intelligent water level monitoring and water pump controlling system using IoT. En Proceedings of the 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 423–427). IEEE. https://doi.org/10.1109/ICESC54411.2022.9885358
Fang, S., Cao, W., Shao, Q., Huang, W., Wang, F., Cheng, X., Cao, J., Luo, J., & Wu, Y. (2022). Reutilization of waste crawfish shell and sludge for efficient volatile fatty acids production by synchronously regulating the bioavailable substrates and microbial metabolic traits. Journal of Cleaner Production, 349, 131456. https://doi.org/10.1016/j.jclepro.2022.131456
Huang, W., Sui, L., Wang, Y., Zhang, C., Jiang, D., Cai, X., & Yang, Z. (2023). Study of the mining and aquifer interactions in complex geological conditions and its management. Scientific Reports, 13(1), 9462. https://doi.org/10.1038/s41598-023-34947-6
Humnabadkar, A., Karve, A., Shivbhakta, B., & Kokate, A. A. (2024). Advanced technologies for sustainable water management: A comprehensive review. International Journal for Future Multidisciplinary Research, 6(5), 382–388. https://doi.org/10.36948/ijfmr.2024.v06i05.27573
Kagiso, M., Wolkersdorfer, C., Kang, N., & Elmaghraby, A. S. (2020). Automated measurement systems in mine water management and mine workings: A review of potential methods. Water Resources and Industry, 24, 100136. https://doi.org/10.1016/j.wri.2020.100136
Leuenberger, A., Dietler, D., Lyatuu, I., Farnham, A., Kihwele, F., Brugger, F., & Winkler, M. S. (2021). Water and health in mining settings in sub-Saharan Africa: A mixed methods geospatial visualization. Geospatial Health, 16(1), 965. https://doi.org/10.4081/gh.2021.965
Maseeh, H. M., Zeebaree, S. R. M., Sadeeq, M. A. M., Ameen, S. Y., Ibrahim, I. M., Zebari, R. R., Ibrahim, R. K., & Sallow, A. B. (2021). IoT and ICT based smart water management, monitoring and controlling system: A review. Asian Journal of Research in Computer Science, 8(2), 42–56. https://doi.org/10.9734/ajrcos/2021/v8i230198
Miller, T., Durlik, I., Kostecka, E., Kozlovska, P., Łobodzińska, A., Sokołowska, S., & Nowy, A. (2025). Integrating artificial intelligence agents with the Internet of Things for enhanced environmental monitoring: Applications in water quality and climate data. Electronics, 14(4), 696. https://doi.org/10.3390/electronics14040696
Molaei, F., Rahimi, E., Siavoshi, H., Afrouz, S. G., & Tenorio, V. (2020). A comprehensive review on Internet of Things (IoT) and its implications in the mining industry. American Journal of Engineering and Applied Sciences, 13(3), 499–515. https://doi.org/10.3844/ajeassp.2020.499.515
Monteiro, C., Van, J., & Schutte, C. (2022). A simulation-based water management strategy for life-of-mine water planning. South African Journal of Industrial Engineering, 33(3), 151–164. https://doi.org/10.7166/33-3-2787
Nguyen, D., Khong, C., & Nguyen, V. (2022). IoT-based automatic dust monitoring and suppression system for coal warehouses and processing areas with a reduction in water consumption. International Journal of Next-Generation Computing, 13(3), 487–497. https://doi.org/10.47164/ijngc.v13i3.658
Puneeth, G. J., Shashank, T., Vinit, P., Muzammil, S. D., & Karthikeya, N. R. (2024). Smart water pollution management: IoT for automatic detection and prevention. International Journal of Advanced Research in Science, Communication and Technology, 4(5), 532–541. https://doi.org/10.48175/IJARSCT-18484
Quispe, W. (2024). Diseño y elaboración de un sistema de envío de datos inalámbrico de sensores para barcazas de relaves mineros utilizando tecnología IoT [Tesis de licenciatura, Universidad Nacional de San Agustín de Arequipa]. Repositorio UNSA. https://repositorio.unsa.edu.pe/server/api/core/bitstreams/c4d11c83-5310-429e-b1fa-04729ae7511f/content
Shafaei, F., Doulati Ardejani, F., Bahroudi, A., Hoseini, M., & Khakpour, M. (2022). Mechanical-electrical dewatering (EDW) of mine tailings: Influence of voltage level on water recovery and moisture reduction. Minerals Engineering, 175, 107303. https://doi.org/10.1016/j.mineng.2021.107303
Shaikh, M., Ali, A., Ahmed, R., & Shaikh, B. A. (2023). A review on Internet of Things (IoT) based water monitoring system. Jurnal Kejuruteraan, 35(6), 1273–1278. https://doi.org/10.17576/jkukm-2023-35(6)-01
Vallejo-Sanchez, D., Muñoz-García, A., Chaverra-Zuleta, E., Correa-Casas, J., Londoño, L. F., & Bustamante-Rúa, O. (2024). Desarrollo de una arquitectura IoT para monitoreo ambiental: Integración de tecnologías de código abierto con proyección de aplicación en el sector minero. DYNA, 91(231), 163–168. https://doi.org/10.15446/dyna.v91n231.112093
Yong García, G. E. (2021). Desarrollo de un sistema de monitoreo y control automático en apoyo a las operaciones de la empresa Sitech Perú S.A., distrito de Santiago de Surco, provincia y departamento de Lima-Perú [Tesis de licenciatura, Universidad Tecnológica del Perú]. https://hdl.handle.net/20.500.12867/5031
Zhenguo, Y., Xintan, C., & Yanping, W. (2020). The prediction of water conducted zone in coal mining by Internet of Things perception. Arabian Journal of Geosciences, 13(21), 1–18. https://doi.org/10.1007/s12517-020-05833-6
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