Tailings Dam Mining, Theoretical Considerations, and Circular Economy: A Review

Mining in tailings dams has emerged as a strategic alternative for mining companies for both economic and environmental reasons. Owing to technological limitations in recent decades, many of these dams have high metal contents, emphasizing the need to evaluate the quality of these residues, especially considering the technological advancements in current concentration plants. An economic viability analysis associated with reusing these materials is crucial. From an environmental point of view, improving mining techniques for dams by considering both safety and feasibility is an advantageous option in decommissioning processes and alignment in the circular economy. In this context, representing these tailings in terms of grade quality and granulometry, as well as the associated contaminants, is essential. Geostatistical estimation and simulation methods are valuable tools for modeling tailings bodies, but they require a reliable sampling campaign to ensure acceptably low errors. From an operational perspective, tailings recovery can be conducted via dry methods, such as mechanical excavation, or hydraulic methods, such as dredging or hydraulic blasting. Dredging is a commonly used method, and cutter suction dredgers, which require pumping to transport fragmented material, are the most commonly used tools. In this paper, some practical applications of geostatistical methods for resource quantification in tailings dams will be discussed. Additionally, the main mining methods for tailings recovery in dams will be presented. Emphasis will be given to the dredging method, along with the key analysis parameters for sizing dredgers, pumps, and pipelines.

Mapping of the Water Table Levels of Unconfined Aquifers Using Two Interpolation Methods

The spatial prediction of the water table can be used for many applications related to civil works (foundations, excavations) and other urban and environmental management activities. Deterministic and geostatistical interpolation methods were used to predict the spatial distribution of water table levels (unconfined aquifers) of important geological formations of the Joao Pessoa City (capital of Paraiba State, Brazil) with dense urban occupation and high demand for new civil works. The deterministic (topo to raster) and geostatistical (ordinary kriging) interpolation methods were evaluated using a Geographic Information System (GIS)-based investigation. The water table levels were obtained from 276 boring logs of Standard Penetration Test (SPT) in situ investigation distributed over the geological formations studied (an area of 59.8 km2, covering 40 districts of the Joao Pessoa City). The Nspt values and textural characterization data are stored for levels of 1 m depth. Some boreholes located in the area investigated were not included in the interpolation processes in order to be compared with estimated values (validation of the results). Maps of the water table depths were also produced to further analyze the quality of the water table surfaces interpolated by both methods. The phreatic surface interpolations provided satisfactory results for both methods (RMSE = 1.8 m). The topo to raster method showed a slight general tendency to be less affected by local values in relation to the kriging method and also has the advantage of integrating the drainage flow system, which is a relevant aspect for spatial models of the water table levels of unconfined aquifers. The ordinary kriging (geostatistical method) provides a prediction surface and some measure of the certainty or accuracy of the predictions.

The application of geostatistics in grain size trend analysis： A case study of eastern Beibu Gulf

There are 71 surface sediment samples collected from the eastern Beibu Gulf. The moment parameters （i.e. mean size, sorting and skewness） were obtained after applying grain size analysis. The geostatistical analysis was then applied to study the spatial autocorrelation for these parameters; while range, a parameter in the semivariogram that meters the scale of spatial autocorrelation, was estimated. The results indicated that the range for sorting coefficient was physically meaningful. The trend vectors calculated from grain size trend analysis model were consistent with the annual ocean circulation patterns and sediment transport rates according to previous studies. Therefore the range derived from the semivariogram of mean size can be used as the characteristic distance in the grain size trend analysis, which may remove the bias caused by the traditional way of basing on experiences or testing methods to get the characteristic distance. Hence the results from geostatistical analysis can also offer useful information for the determination of sediment sampling density in the future field work.