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Heap leaching of copper is faced with a complex set of challenges, including mineral heterogeneity, the formation of passivating species, and the need to regulate critical variables such as pH, redox potential (Eh), oxidant concentration, and irrigation rate. If these factors are not properly managed, copper recovery is reduced, and significant environmental impacts may be generated, highlighting the urgency for systematic and sustainable approaches. To address this challenge, a systematic literature review (SLR) was conducted, screening 2344 documents and selecting 106 primary sources to analyze operational drivers and environmental considerations. Statistical methodologies (factorial designs, response surface methodology), multiscale modeling, and laboratory column tests were used to validate key variables, including pH (1.5–2.0), Eh (600–750 mV), temperature (25–55 °C), irrigation rate (5–15 L/(h·m2)), acid concentration (0.5–2.0 M), and emerging “green” reagents (e.g., glycine, organic surfactants). Precise control of these factors was found to reduce passivation, minimize fine-particle migration, and improve copper extraction up to 90%. The incorporation of oxidizing agents (e.g., Fe3+, H2O2) further accelerated mineral dissolution while preventing unwanted precipitates. In parallel, bioleaching strategies maintained high recoveries with lower chemical demand. Reviews of pilot studies confirmed the scalability of these optimized conditions, emphasizing both sustainability and cost-effectiveness.

The microbial communities are important for minerals decomposition in biological heap leaching system. However, the differentiation and relationship of composition and function of microbial communities between leaching heap (LH) and leaching solution (LS) are still unclear. In this study, 16S rRNA gene sequencing was used to assess the microbial communities from the two subsystems in ZiJinShan copper mine (Fujian province, China). Results of PCoA and dissimilarity test showed that microbial communities in LH samples were significantly different from those in LS samples. The dominant genera of LH was Acidithiobacillus (57.2 ∼ 87.9 %), while Leptospirillum (48.6 ∼ 73.7 %) was predominant in LS. Environmental parameters (especially pH) were the major factors to influence the composition and structure of microbial community by analysis of Mantel tests. Results of functional test showed that microbial communities in LH utilized sodium thiosulfate more quickly and utilized ferrous sulfate more slowly than those in LS, which further indicated that the most sulfur-oxidizing processes of bioleaching took place in LH and the most iron-oxidizing processes were in LS. Further study found that microbial communities in LH had stronger pyrite leaching ability, and iron extraction efficiency was significantly positively correlated with Acidithiobacillus (dominated in LH), which suggested that higher abundance ratio of sulfur-oxidizing microbes might in favor of minerals decomposition. Finally, a conceptual model was designed through the above results to better exhibit the sulfur and iron metabolism in bioleaching systems.

The leaching process is essential in the mining industry, because it efficiently extracts valuable minerals, such as copper. However, monitoring and controlling the leaching process presents significant challenges due to material variability, uneven distribution of the leaching solution, and the effects of environmental factors like temperature and moisture content. One of the main technological challenges is measuring variables within the leaching heap. Implementing state observers or estimators (i.e., virtual sensors) offers a promising solution, allowing for a cost-effective estimation of non-measurable process variables. To validate this approach, this paper proposes and analyzes the use of two estimation methods, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), to estimate the moisture content, copper in the ore, and acid consumption based on measurements of acid and copper concentrations in the heap leaching process. The results obtained from simulations demonstrate accurate estimations from both state observers. The variable best estimated with EKF was the moisture content, achieving a 0.041% Integral Absolute Error (IAE) and a 0.069% Integral Square Error (ISE) in one of the analyzed scenarios. Utilizing these state estimators improves the understanding of the internal dynamics of heap leaching, often limited by the lack of field-level instrumentation, such as sensors and transmitters. This approach can enhance the operational efficiency of heap leaching plants by enabling the real-time estimation of unmeasurable variables, ultimately improving metal recovery and reducing acid consumption.

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.

Water level and wind speed have important influences on radon release in particle-packing emanation media. Based on radon migration theory in porous media under three water level conditions, an experimental setup was designed to measure the surface radon exhalation rate of uranium tailings from heap leaching uranium mine at different water levels and wind speeds. When the water level was at 0.3 m (overlying depth 0.05 m), radon transfer velocities at the gas–liquid interface were also measured at different wind speeds. Results show that when the water level was equal to or lower than the surface of the sample, the radon exhalation rate increased with increasing wind speed and decreased with increasing water level. When the water level was higher than the surface of the sample, radon exhalation rate of the water surface increased with increasing surface wind speed. The wind speed, however, was less influential on the radon exhalation rate as the depth of the overlying water increased, with a dramatic decrease in radon release. That said, at different wind speeds, radon transfer velocities at the gas–liquid interface were consistent with literature. On the other hand, changes in wind speed had significant influences on the radon transfer velocity at the gas–liquid interface, with the effect less pronounced at higher wind speeds.

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.

Although the taxonomical/phylogenetic diversity of microbial communities in biological heap leaching systems has been investigated, the diversity of functional genes was still unclear, and, especially, the differentiation and the relationships of diversity and functions of microbial communities in leaching heap (LH) and leaching solution (LS) were also still unclear. In our study, a functional gene array (GeoChip 5.0) was employed to investigate the functional gene diversity, and 16S rRNA gene sequencing was used to explore the taxonomical/phylogenetic diversity of microbial communities in LH and LS subsystems of Dexing copper mine (Jiangxi, China). Detrended correspondence analysis (DCA) showed that both functional gene structure and taxonomical/phylogenetic structure of microbial communities were significantly different between LH and LS. Signal intensities of genes, including genes for sulfur oxidation (e.g., soxB ), metal homeostasis (e.g., arsm ), carbon fixation (e.g., rubisco ), polyphosphate degradation (e.g., ppk ), and organic remediation (e.g., hydrocarbons) were significantly higher in LH, while signal intensities of genes for carbon degradation (e.g., amyA ), polyphosphate synthesis (e.g., ppx ), and sulfur reduction (e.g., dsrA ) were significantly higher in LS. Further inspection revealed that microbial communities in LS and LH were dominated by Acidithiobacillus and Leptospirillum . However, rare species were relatively higher abundant in LH. Additionally, diversity index of functional genes was significantly different in LS (9.915 ± 0.074) and LH (9.781 ± 0.165), and the taxonomical/phylogenetic diversity index was also significantly different in LH (4.398 ± 0.508) and LS (3.014 ± 0.707). Functional tests, including sulfur-oxidizing ability, iron-oxidizing ability, and pyrite bioleaching ability, showed that all abilities of microbial communities were significantly stronger in LH than those in LS. Further studies found that most key genes (e.g., soxC and dsrA ), rather than functional gene diversity index, were significantly correlated with abilities of microbial communities by linear regression analysis and Pearson correlation tests. In addition, the abilities were significantly correlated with taxonomical/phylogenetic diversity index and some rare species (e.g., Ferrithrix ).

The article presents data from theoretical and experimental studies related to the search for a solution to an urgent geotechnological problem - selective mining of standard quality ore, substandard and diluted ores, taking into account the peculiarities of their processing by heap leaching. For complex-structural large-scale ore deposits of the stockwork morphological-structural type, multilevel heterogeneity is typical. The morphology and structure of the objects of direct extraction - ore bodies within the production blocks - vary significantly. The expediency of selective mining and separate heap leaching (or heap oxidation and leaching) of gold and copper from conditioned ores of various geological and technological types and grades is justified. In addition, it is advisable to selectively extract and separately process by heap leaching or flux in a controlled manner before it off-balance and diluted ores, as well as mineralized overburden and tailings of high-grade ores.

High-throughput sequencing technology also known as \"next generation\" sequencing technology, compared with the traditional sequencing method has the characteristics of fast speed, high flux, low cost. In recent years the technology in the detection of microbial diversity has been fully applied. In this study, the microbial community of ore heap in different area and different depth was studied by using this method. The results showed the bio - heap leaching of low - grade secondary copper sulfide ore in Zijinshan from China could effectively recover the copper in the ore. The number of microorganisms in the center was significantly larger than that on the edge of ore heap, and as the depth increases in the heap, the number of microorganisms decreases. The dominant bacteria in the ore heap center was Acidithiobacillus ferrooxidans, in addition there were also some Sulfobacillus thermosulfidooxidans, Ferroplasma cupricumulans, Acidithiobacillus caldus and a small amount of Leptospirillum ferriphilum in the center of the ore heap. Unlike the ore heap center, Sulfobacillus thermosulfidooxidans was the main species on the edge of the ore heap, moreover Acidithiobacillus ferrooxidans, Ferroplasma cupricumulans, Acidithiobacillus caldus and a small amount of Leptospirillum ferriphilum were found on the edge of the ore heap. In addition, some heterotrophic bacteria such as Pseudomonas, Serratia, Sediminibacterium, Stenotrophomonas, Brevundimonas and Variovorax were found both in the center and the edge of the sample, these heterotrophic bacteria may be beneficial for the leaching of valuable metals.

In this research, first, a brief review of heap leaching structures is presented. Then, some engineering properties of natural and geo-synthetic materials of a leaching heap bed are introduced. Next, physical models of these structures are tested using a tilting table, and the apparatus’s “scale factor” for the evaluation of the probable slides in these structures is determined. Analyses have shown that the “scale factor” of the “factor of safety” for the above physical models against slide approximately equals 1; therefore, the slide mechanism in such structures can be studied using tilting table experimental models. On this basis, 12 physical models of leaching heaps were made and their stability checked with the apparatus. Next, using Morgenstern and Price method, the stability of the above models was evaluated and the analytical and physical results were compared for the verification of the limit equilibrium approaches. Comparisons have shown that the error of such methods in the stability analyses of leaching heaps is <8 %. As a real case study, the Shahr-e-Babak heap leaching structure failure and its stabilization methods have been presented. In this project, tensile cracks occurred in the upper section of the heap after loading the second ore lift, and a part of it slid. The heap was stabilized using a double textured geo-membrane liner and safety berms.