The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most Facility
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 reserv...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2022-06-01
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| Series: | Archives of Civil Engineering |
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| Online Access: | https://journals.pan.pl/Content/123608/PDF/art17.pdf |
| _version_ | 1811314633768697856 |
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| author | Wioletta Koperska Maria Stachowiak Natalia Duda-Mróz Paweł Stefaniak Bartosz Jachnik Bartłomiej Bursa Paweł Stefanek |
| author_facet | Wioletta Koperska Maria Stachowiak Natalia Duda-Mróz Paweł Stefaniak Bartosz Jachnik Bartłomiej Bursa Paweł Stefanek |
| author_sort | Wioletta Koperska |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-13T11:15:56Z |
| format | Article |
| id | doaj.art-c353e33430314fc0b563f60aa1585230 |
| institution | Directory Open Access Journal |
| issn | 2300-3103 |
| language | English |
| last_indexed | 2024-04-13T11:15:56Z |
| publishDate | 2022-06-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Civil Engineering |
| spelling | doaj.art-c353e33430314fc0b563f60aa15852302022-12-22T02:48:59ZengPolish Academy of SciencesArchives of Civil Engineering2300-31032022-06-01vol. 68No 2297311https://doi.org/10.24425/ace.2022.140643The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most FacilityWioletta Koperska0https://orcid.org/0000-0002-5882-362XMaria Stachowiak1https://orcid.org/0000-0001-7501-3437Natalia Duda-Mróz2https://orcid.org/0000-0002-5320-5822Paweł Stefaniak3https://orcid.org/0000-0002-1772-5740Bartosz Jachnik4https://orcid.org/0000-0002-7050-4373Bartłomiej Bursa5https://orcid.org/0000-0001-8076-7006Paweł Stefanek6https://orcid.org/0000-0003-3357-0053KGHM Cuprum Research and Development Centre, gen. W. Sikorskiego 2-8, 53-659 Wrocław, PolandKGHM Cuprum Research and Development Centre, gen. W. Sikorskiego 2-8, 53-659 Wrocław, PolandKGHM Cuprum Research and Development Centre, gen. W. Sikorskiego 2-8, 53-659 Wrocław, PolandKGHM Cuprum Research and Development Centre, gen. W. Sikorskiego 2-8, 53-659 Wrocław, PolandKGHM Cuprum Research and Development Centre, gen. W. Sikorskiego 2-8, 53-659 Wrocław, PolandGEOTEKO Serwis Ltd., ul. Wałbrzyska 14/16, 02-739 Warszawa, PolandKGHM Polska Miedz S.A., M. Skłodowskiej-Curie 48, 59-301 Lubin, PolandApproximately 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).https://journals.pan.pl/Content/123608/PDF/art17.pdfhydrotechnicstailing damdata miningrisk analysisstrength parameters |
| spellingShingle | Wioletta Koperska Maria Stachowiak Natalia Duda-Mróz Paweł Stefaniak Bartosz Jachnik Bartłomiej Bursa Paweł Stefanek The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most Facility Archives of Civil Engineering hydrotechnics tailing dam data mining risk analysis strength parameters |
| title | The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most Facility |
| title_full | The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most Facility |
| title_fullStr | The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most Facility |
| title_full_unstemmed | The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most Facility |
| title_short | The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Zelazny Most Facility |
| title_sort | tailings storage facility tsf stability monitoring system using advanced big data analytics on the example of the zelazny most facility |
| topic | hydrotechnics tailing dam data mining risk analysis strength parameters |
| url | https://journals.pan.pl/Content/123608/PDF/art17.pdf |
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