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 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).