The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most Facility
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KGHM Cuprum Research and Development Centre, gen. W. Sikorskiego 2-8, 53-659 Wrocław, Poland
GEOTEKO Serwis Ltd., ul. Wałbrzyska 14/16, 02-739 Warszawa, Poland
KGHM Polska Miedz S.A., M. Skłodowskiej-Curie 48, 59-301 Lubin, Poland
Submission date: 2021-08-23
Acceptance date: 2021-08-31
Publication date: 2022-06-30
Archives of Civil Engineering 2022;68(2):297-311
Approximately 30 million tons of tailings are being stored each year at the KGHMs Zelazny Most Tailings Storage Facility (TSF). Covering an area of almost 1.6 thousand hectares, and being surrounded by dams of a total length of 14 km and height of over 70 m in some areas, makes it the largest reservoir of post-flotation tailings in Europe and the second-largest in the world. With approximately 2900 monitoring instruments and measuring points surrounding the facility, Zelazny Most is a subject of round-the-clock monitoring, which for safety and economic reasons is crucial not only for the immediate surroundings of the facility but for the entire region. The monitoring network can be divided into four main groups: (a) geotechnical, consisting mostly of inclinometers and VW pore pressure transducers, (b) hydrological with piezometers and water level gauges, (c) geodetic survey with laser and GPS measurements, as well as surface and in-depth benchmarks, (d) seismic network, consisting primarily of accelerometer stations. Separately a variety of different chemical analyses are conducted, in parallel with spigotting processes and relief wells monitorin. This leads to a large amount of data that is difficult to analyze with conventional methods. In this article, we discuss a machine learning-driven approach which should improve the quality of the monitoring and maintenance of such facilities. Overview of the main algorithms developed to determine the stability parameters or classification of tailings are presented. The concepts described in this article will be further developed in the IlluMINEation project (H2020).
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