The successful design and implementation of hydroengineering projects crucially rests upon three collaborative pillars of research: field observations, physical models, and mathematical models.

Prof. Tomasz Okruszko explains what role wetlands play in the environment and how they are affected by human activity.

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.

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.

Multi-purpose reservoirs play an important role in satisfying demands for water supply, irrigation, hydropower, drinking water, flood protection, recreation, navigation, and other purposes. At the same time, they can often have considerable negative impacts on the environment and local biodiversity that remain largely unseen. These “dirty secrets” include sediment deposition, cyanobacteria blooms, and greenhouse gas emissions.

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).

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult-to-interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the authors of this paper, this method can be applied to monitor both concrete and earth objects, with examples of analyses for different object types presented in the article.