Search Results (7)

Active and abandoned mining sites are significant sources of heavy metals and metalloid pollution, leading to serious environmental issues. This study assessed the environmental risks posed by potentially toxic elements (PTEs), specifically arsenic (As) and antimony (Sb), in the Technosols (mining residues) of the former Pejão coal mine complex in Northern Portugal, a site impacted by forest wildfires in October 2017 that triggered underground combustion within the waste heaps. Our methodology involved determining the “pseudo-total” concentrations of As and Sb in the collected heap samples using microwave digestion with aqua regia (ISO 12914), followed by analysis using hydride generation-atomic absorption spectroscopy (HG-AAS). The concentrations of As an Sb ranging from 31.0 to 68.6 mg kg⁻¹ and 4.8 to 8.3 mg kg⁻¹, respectively, were found to be above the European background values reported in project FOREGS (11.6 mg kg⁻¹ for As and 1.04 mg kg⁻¹ for Sb) and Portuguese Environment Agency (APA) reference values for agricultural soils (11 mg kg⁻¹ for As and 7.5 mg kg⁻¹ for Sb), indicating significant enrichment of these PTEs. Based on average I_geo values, As contamination overall was classified as “unpolluted to moderately polluted” while Sb contamination was classified as “moderately polluted” in the waste pile samples and “unpolluted to moderately polluted” in the downhill soil samples. However, total PTE content alone is insufficient for a comprehensive environmental risk assessment. Therefore, further studies on As and Sb fractionation and speciation were conducted using the Shiowatana sequential extraction procedure (SEP). The results showed that As and Sb levels in the more mobile fractions were not significant. This suggests that the enrichment in the burned (BCW) and unburned (UCW) coal waste areas of the mine is likely due to the stockpiling of lithic fragments, primarily coals hosting arsenian pyrites and stibnite which largely traps these elements within its crystalline structure. The observed enrichment in downhill soils (DS) is attributed to mechanical weathering, rock fragment erosion, and transport processes. Given the strong association of these elements with solid phases, the risk of leaching into surface waters and aquifers is considered low. This work underscores the importance of a holistic approach to environmental risk assessment at former mining sites, contributing to the development of sustainable remediation strategies for long-term environmental protection. Full article

In order to reduce odor emissions and surface water pollution while storing manure in field heaps near a barn, there is a challenge in properly designing manure-storage areas. Therefore, it is important to assess what solutions and conditions, considering environmental requirements, should be considered when storing manure in field heaps. The goal of the research is to determine the impact of various factors on the risk of nutrient leaching, soil, and gas emissions from solid manure heaps, considering climatic factors in the environment. Through various scientific studies, a manure pile model has been developed and evaluated for its impact on the risk of potential leaching and odor emissions (using hyperspectral gas emission analysis mass flow method) from manure and the dynamics of the 0–80 cm soil layer properties (nitrate (N-NO₃) and nitrite (N-NO₂), ammonia (NH₃), mineral, and total N). Based on the research results, requirements for manure management and storage during the prohibited fertilization period were established, considering the requirements for nitrates from agricultural sources in Lithuania. An optimal new manure heap model has been identified—a layer of not less than 20 cm of compacted straw (density 150–200 kg m⁻³) or a 10 cm layer of peat for absorbing manure slurries is formed on the soil surface, the manure heap is surrounded by an earth embankment not less than 30 cm high, the manure heap is covered with a layer of finely chopped straw not less than 10 cm thick, or 5 cm of sawdust, or 5 cm of peat. The manure is stored in the heap for 6–12 months. Following the research results, requirements for manure management and storage during the prohibited fertilization period were established, considering the requirements for nitrates from agricultural sources in Lithuania, applicable to the northern part of the temperate climate zone and applying similar requirements to the relevant countries. Full article

The mining sector actively seeks to improve operational processes and manage residual materials, especially in areas used for heap leaching disposal. The flowability of residues following deposition can have an impact on storage capacity, productivity, and workers’ safety. In this study, an artificial neural network (ANN) approach is applied to evaluate the accuracy of three models in predicting the flowability of spent heap leach when it is discharged into the dump, considering three or five segregation categories. The models with five categories exhibited the highest level of accuracy, with learning responses ranging from 72% to 78% and predictions from 88% to 96%. These indicate that ANN models have the potential to be a decision-making tool for the discharge strategy in the dump. Modules containing lithologies such as clays and phyllosilicates exhibited increased susceptibility to separation due to their water retention capacity, which negatively impacted their permeability and conductivity. The decomposition of iron oxide, along with clays and low hardness, led to the formation of fines, limited permeability, and inadequate solution drainage. Rock competence and low formation of fines provide good permeability, and better drainage conditions for the solution, and help maintain the stability of the spent heap leach in the dump. Full article

In the past two decades, the mining sector has increasingly embraced simulation and modelling techniques for decision-making processes. This adoption has facilitated enhanced process control and optimisation, enabling access to valuable data such as precise granulometry measurements, improved recovery rates, and the ability to forecast outcomes. Soft computing techniques, such as artificial neural networks and fuzzy algorithms, have emerged as viable alternatives to traditional statistical approaches, where the complex and non-linear nature of the mineral processing stages requires careful selection. This research examines the up-to-date use of soft computing techniques within the mining sector, with a specific emphasis on comminution, flotation, and pyrometallurgical and hydrometallurgical processes, and the selection of soft computing techniques and strategies for identifying key variables. From this, a soft computing approach is presented to enhance the monitoring and prediction accuracy for mineral waste disposal, specifically focusing on tailings and spent heap leaching spoils database treatment. However, the accessibility and quality of data are crucial for the long-term application of soft computing technology in the mining industry. Further research is needed to explore the full potential of soft computing techniques and to address specific challenges in mining and mineral processing. Full article

In order to solve the problem of vegetation restoration in the traditional heap leaching of rare earths, a novel heap leaching technology is proposed. In the novel technology, leaching liquid is laterally injected into the rare earth pile from the side end so that vegetation restoration can be carried out quickly on the top of the pile. In this study, a laboratory test was performed to investigate the permeability of the ion-adsorbed rare earth pile under lateral liquid injection conditions. Four rare earth samples with varied combinations of the mineral particles at different sizes were adopted in the test to emphatically discuss the influence of the particle composition on permeability. The experimental results show that the permeability of the rare earth pile under the lateral liquid injection conditions is governed by the migration of fine particles. As the hydraulic head of the leaching liquid increases, the fine particles undergo a motion process of stabilization, migration initiation, deposition, and remigration. Accordingly, the permeability coefficient of the rare earth pile exhibits a variation of stabilization, a gradual increase, a re-stabilization, and a re-increase. The fine particle migration and exudation causes the permeability of the rare earth pile to be non-uniform in space. The permeability coefficients near the liquid injection end and the liquid outlet end are much greater than those are at the middle positions of the pile. The particle composition of the rare earth has an important effect on the permeability of the pile. In the rare earth with a combination of coarse and fine particles, preferential flow paths are easy to develop as in this soil, the fine particles easily migrate and they cannot block the pore channels. By contrast, the fine particles find it difficult to migrate, and the permeability coefficient is relatively stable in the fine-medium particle rare earth. This particle composition is conducive to the heap leaching of the rare earth under lateral liquid injection conditions. Full article

This manuscript describes a method that is based on the implementation and setup of a mechatronic system that can recognize and detect, through thermal analysis, the zones where heap leaching piles may become locally saturated. Such a condition could trigger the potential of liquefaction, generating local or general collapse in the pile. In order to reduce this potential danger, and therefore achieve full stability in the pile, the irrigation system must be properly controlled; for instance, in potentially saturated zones, the irrigation flow can be reduced or eliminated until the saturation has disappeared. The mechatronic system consists of a hexacopter, equipped with a thermal infrared camera mounted on its structure and pointing down to the ground, which is used to obtain the temperature information of the heat transfer between the heap pile and the environment. Such information is very useful, as the level of saturated zones can then be traced. The communication between the operator of the irrigation system and the mechatronic system is based on a radio-frequency link, in which geo-referenced images are transmitted. Full article

Multivariate analytical models are quite successful in explaining one or more response variables, based on one or more independent variables. However, they do not reflect the connections of conditional dependence between the variables that explain the model. Otherwise, due to their qualitative and quantitative nature, Bayesian networks allow us to easily visualize the probabilistic relationships between variables of interest, as well as make inferences as a prediction of specific evidence (partial or impartial), diagnosis and decision-making. The current work develops stochastic modeling of the leaching phase in piles by generating a Bayesian network that describes the ore recovery with independent variables, after analyzing the uncertainty of the response to the sensitization of the input variables. These models allow us to recognize the relations of dependence and causality between the sampled variables and can estimate the output against the lack of evidence. The network setting shows that the variables that have the most significant impact on recovery are the time, the heap height and the superficial velocity of the leaching flow, while the validation is given by the low measurements of the error statistics and the normality test of residuals. Finally, probabilistic networks are unique tools to determine and internalize the risk or uncertainty present in the input variables, due to their ability to generate estimates of recovery based upon partial knowledge of the operational variables. Full article