Underground mining extraction causes the displacement and changes of stress fields in the surrounding rock mass. The determination of the changes is extremely important when the mining activity takes place in the proximity of post-flotation tailing ponds, which may affect the stability of the tailing dams. The deterministic modeling based on principles of continuum mechanics with the use of numerical methods, e.g. finite element method (FEM) should be used in all problems of predicting rock mass displacements and changes of stress field, particularly in cases of complex geology and complex mining methods. The accuracy of FEM solutions depends mainly on the quality of geomechanical parameters of the geological strata. The parameters, e.g. young modulus of elasticity, may require verification through a comparison with measured surface deformations using geodetic methods. This paper presents application of FEM in predicting effects of underground mining on the surface displacements in the area of the KGHM safety pillar of the tailing pond of the OUOW Żelazny Most. The area has been affected by room and pillar mining with roof bending in the years 2008-2016 and will be further exposed to room-and-pillar extraction with hydraulic filling in the years 2017–2019.

The paper presents the approach for optimization of preventive/technological measures increasing the safety of tailings pond dams. It is based on the combined use of monitoring results as well as advanced 3D finite element (FE) modeling. Under consideration was the eastern dam of Zelazny Most Tailings Storage Facility (TSF). As part of the work, four numerical models of the dam and the subsoil, differing in the spatial arrangement of the soil layers, were created. For this purpose, the kriging technique was used. The numerical models were calibrated against the measurements from the monitoring system. In particular the readings acquired from benchmarks, piezometers and inclinometers were used. The optimization of preventive measures was performed for the model that showed the best general fit to the monitoring data. The spatial distribution and installation time of relief wells were both optimized. It was shown that the optimized system of relief wells provides the required safety margin.

The fundamental problem from the point of view of pipeline exploitation in KGHM Polska Miedz S.A. is the very high overwearing of the pipes used for the transport of tailings, as well as determining the time of trouble-free operation of pipe system components. Failures involve significant financial outlays, severe restrictions on operation and in some cases even stopping operation. For this reason, it is vital to monitor the condition of the transport systems, as well as to determine the permissible service life of the pipe sections, after which segments at risk should be replaced or turned over in order to extend their further operation. This paper focuses on the application of interval numbers to assess the durability of piping systems. The calculations were made using classical interval numbers by using code written in INTLAB libraries. The correctness of the solutions obtained was verified using the Monte Carlo method, assuming a uniform distribution of random variables.

Water distribution systems at KGHM S.A. are of great importance for the efficient production of copper and environmental protection. For failures leading to perforation and leakage, the corrosion processes are responsible. This paper aims to assess corrosion on the basis of the analysis of the exposure of the Hydrotechnical Plant pipelines. To this end, the system of transfer and deposition of post-flotation waste as well as the circulation of industrial water in the process of copper ore enrichment are described. Water sources as well as inflows and outflows in the water system are indicated; corrosion hazards are determined. Water is obtained from mines; it is often contaminated during the copper ore mining process. The chemical analysis of industrial (technological) water and sludge water resulting from the sedimentation of post-flotation waste showed a high concentration of inorganic salts which are responsible for the corrosive processes. Furthermore, tests were carried out to determine the corrosion rate.Additionally, possible methods to reduce corrosion have been proposed, i.e., a corrosion monitoring system has been described as a tool for reducing production interruptions and environmental pollution.

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).