Deposits of non-ferrous, precious, and rare metals are predominantly complex-structured. Such bench blocks consist of economic and subeconomic ore. The boundary between the two types is defined by a cut-off ore grade. The numerical value of the ore grade in the subeconomic portion of a block determines the thickness of material that can potentially be admixed with shipped economic ore. Controlled admixing enables complete recovery of minerals from stopes while maintaining concentrate quality. For this purpose, exploration borehole data from five copper and gold deposits in Kazakhstan with complex structures were analyzed. Based on borehole data, trend equations were derived to describe ore grade variation in the subeconomic part of a block. A software tool was developed to automate the calculation of trend lines and their equations. Using this program, new dependencies were obtained for determining the ore grade in the shipped ore (α′). For the first time in mining science, a method has been substantiated for the complete recovery of economic ore from complex-structured bench blocks, based on admixing a controlled portion of subeconomic ore. This approach increases total ore extraction and improves valuable component recovery to concentrate. The potential increase in recovered components from shipped ore may reach 10–15% of total production.

To address the current challenges in oil industry related to modeling a pore space structure in a 3D core model and evaluating permeability and porosity ("Digital Core"), it is necessary to obtain representative characteristics of the void space. A similar characteristic is required to solve geotechnical problems related to modeling and evaluating the strength properties of heterogeneous rocks. In addition, it is also important for research on capillary processes in porous media. The paper is devoted to the comparative analysis of the values of porosity of oil and gas reservoir rocks obtained by gas volumetry and X-ray computer tomography methods. The aim of this work is to develop statistical models for assessing the discrepancy between the porosity factor K_p determined using computer tomography (CT) data and more reliable laboratory petrophysical data for two lithological rock types: terrigenous and carbonate. The research objectives include: assessing the impact of lithology on the K_p evaluation using various methods (petrophysics and CT); examining and evaluating the impact of the reservoir rocks porosity factor range on the convergence of the results from these two methods for different lithological rock types; building statistical models to adjust the K_p values based on CT results for different lithological rock types. The solution to these problems is based on a detailed statistical analysis of the studies of terrigenous and carbonates rocks in oil fields in the Perm region. Porosity measurement was carried out on a AP-608 automated porosimeter-permeameter and a Nikon XT H 225 X-ray tomography system. The techniques for measuring the volume of pores in samples using the gas volumetry method, image binarization, and porosity calculation using the X-ray tomography method are described. The results of the analysis showed that the studied methods give different values of porosity factors depending on the lithology. For carbonate rocks, a greater correspondence of the porosity factor estimates obtained by different methods is characteristic that is due to the structural features of the pore space. Significant differences were found for terrigenous rocks, which are explained by the limited resolution of X-ray tomography. The analysis resulted in statistical models for evaluating and correcting K_p data obtained by X-ray tomography for terrigenous and carbonate rocks in various K_p value ranges. The results of the study can be used for petrophysical substantiation of the permeability and porosity of reservoir rocks in oil and gas fields.

Studying mineralogical composition of ores is a fundamental step in the exploration of new deposits, as it allows determining the forms in which useful components are found, the processes of ore formation, and the potential recoverability of valuable elements. The mineral associations, textures, and structures of ores not only provide key information about the geology of a deposit, but also determine the choice of beneficiation methods. Despite the development of modern analytical tools and existing solutions for automatic mineral diagnosis, such as those based on the SEM-EDS method, optical microscopy remains the most accessible means of quantitative mineralogical analysis. However, it remains labor-intensive and requires highly skilled specialists. In addition, its visual nature limits the accuracy and reproducibility of results, creating a need for more effective approaches. One promising area is the automation of ore mineral identification based on images of polished sections. The aim of the work was to develop and validate a universal segmentation model based on deep learning. In the course of the research, related problems were also solved, including the creation of an open LumenStone dataset, the development of color adaptation methods, joint analysis of PPL and XPL images, panorama construction, and the development of a fast annotation method. The work applied convolutional neural network architectures, color correction and joint image processing algorithms, as well as an original sampling method that compensates for class imbalance. The proposed segmentation model demonstrated high accuracy (IoU up to 0.88, PA up to 0.96) for nine minerals. The obtained results confirmed the effectiveness of integrating deep learning and modern image processing algorithms in mineralogical analysis systems and laid the foundation for further development of digital methods in automated petrography.

The formation of terrain reflects a combination of geological processes, including tectonics, magmatism, and erosion. A digital terrain model (DTM) is an important tool for solving geological problems, including the prediction of ore deposits, especially in areas of low exploration maturity. The paper discusses the surface imprinting of geological processes that form a terrain, and also considers a method of digital terrain model analysis. The presented method allows identifying specific landforms similar to those formed above known deposits that makes it possible to add one more prospecting criterion when prospecting for porphyry deposits and deposits formed under similar conditions. The Aktogai ore field was selected as the object for studying the terrain above the known deposits. The Aktogai ore field is located in the northeastern part of Near-Balkhash territory. It includes two major porphyry copper deposits, Aktogai and Aidarly, a small copper deposit Kyzylkia, as well as a number of copper and polymetallic occurrences of porphyry and vein types associated with the Koldar granite massif of the "variegated batholith" formation. All objects identified within the Aktogai ore field were formed under specific structural conditions of ore formation and were affected by erosion processes to varying degrees. This, in turn, is expressed by the presence of local landforms of these objects on the day surface, which are characterized by different "terrain energy" factors. Analysis of the DTM surface allows not only to identify local landforms above an ore body and their morphological features, but also to indirectly assess the erosional truncation of the ore body. Based on morphological characteristics, it is also possible to classify host rocks lithological units. An assessment of the applicability of the method depending on the characteristics of an ore body and an indirect assessment of the tectonic conditions of deposit formation are provided. It has been established that large ore deposits (Aktogai, Aidarly, Zapadny Stockwork) manifest themselves in the terrain as local cauldron subsidences. These zones are characterized by a high degree of textural heterogeneity and coincide with areas of intense metasomatism. Areas morphologically and spectrally similar to the areas of known ore bodies have been identified, indicating their potential for further exploration. DTM and textural analysis methods allow identifying geological and structural features associated with porphyry systems and serve as an additional tool for predicting new ore bodies/deposits. The integration of morphometric (physiographic) and spectral analysis increases the reliability of the interpretation of geological processes.

The Ta Phoi copper deposit, located in the northeastern Phan Si Pan zone, Northwestern Vietnam, is a significant site of Neoproterozoic Cu mineralization. Its distinct geological characteristics justify its investigations, especially in comparison to nearby IOCG Sin Quyen deposit. This study is aimed at clarifying the genesis, ore-forming conditions, and fluid evolution of the Ta Phoi deposit through an integrated approach combining geological, petrographic, geochemical, and isotopic data analysis. The research specifically employs U-Pb dating of sphene, sulfur isotope analysis, and fluid inclusion microthermometry to identify the age, origin, and physicochemical environment of the mineralization. Sphene U-Pb dating yielded concordant ages of 810.7 ± 4.6 Ma and 819.5 ± 2.0 Ma, indicating a Neoproterozoic mineralization event temporally linked to regional granodiorite and diorite intrusions. Sulfur isotope values (δ³⁴S = +2.2 to +3.1‰) suggest a magmatic origin for ore-forming fluids. Fluid inclusion data detected fluid temperatures ranging from 163.1°C to 410°C, fluid salinities of 2.1–16.25 wt% NaCl equiv., and formation pressures of 44–100 MPa at depths of 3.4–6.5 km. These results confirmed that the Ta Phoi deposit formed from medium- to high-temperature, magmatogene hydrothermal fluids in a subduction-related continental arc setting; it may represent a porphyry-related skarn or endoskarn system that developed in response to magmatic fluid migration along lithological contacts and faults. These findings provide new insights into the metallogenic framework of the Ta Phoi deposit and highlight its potential for further Cu exploration in Northwestern Vietnam.

It is very urgent to increase the efficiency of depressing magnesium-containing silicates (MS) in the course of the flotation of copper-nickel ores to reduce the content of magnesium in the concentrate, which causes a significant increase in energy consumption in pyrometallurgical processing of the concentrate. The use of polymer reagents containing sulfo groups seems to be a promising area of research. However, only lignosulfonates have been studied so far in this field. The question of the effectiveness of the depressing effect of other polymer sulfonates including polystyrene sulfonates (PSS), and their comparison with polysaccharides used in industrial conditions remains unclear. The purpose of this work is to study the depressing effect of PSS on the performance of bulk flotation of copper-nickel ores. Research objectives: to experimentally compare the effectiveness of the depressing effect of PSS and a reagent from the polysaccharide class on MS; to determine the modes of PSS use to reduce the magnesium content in the concentrate without significantly reducing the recovery of copper and nickel into the bulk concentrate; to establish the effect of molecular weight and the method of obtaining PSS samples on the effectiveness of their depressing effect. Laboratory experimental studies were carried out on the bulk flotation of copper-nickel ores from the Kola Peninsula, containing 15.7% of magnesium, 0.44% of nickel and 0.25% of copper. The effect of the following polymer anionic reagents on the flotation was studied: PSSs with molecular weight ranging from 89,000 to 208,000 g/mol; polyanionic cellulose (PAC-N) was used for comparison. To increase the effectiveness of these reagents, magnesium chloride was previously added. It was found that the lowest magnesium content in the concentrate of 14.7% was achieved using a composition of magnesium chloride and PSS against 16.7% without the depressants. It was shown that PSS provides a higher recovery of copper (by 7%) and nickel (by 8%) into the concentrate than when using PAC-N, since PSS, unlike polysaccharides, does not form chelate complexes with these metals. It was also shown that for PSS samples, the molecular weight within these limits has virtually no effect on the studied ore flotation performance. New scientific knowledge has been obtained about the effect of the consumption and properties of PSS on the flotation performance. It has been shown that the practical use of this class of reagents is advisable for the flotation of copper-nickel ores with a high magnesium content in cases where it is necessary to achieve the maximum possible decreasing the content of this element in the concentrate without significantly reducing the recovery of copper and nickel.

The reliability of mine ventilation fans plays a crucial role in aerological systems and is determined by a combination of various geological, mining-technological, and structural factors, most of which are stochastic in nature. The problem of quantifying the reliability indicators of mine fan installations is addressed using various mathematical methods for modeling random processes. This study considers the possibility of applying Markov process theory to develop a methodology for calculating the operational reliability of centrifugal main mine ventilation fans, using the VShTs-16 as an example. The limitations of applying Markov processes to reliability analysis are demonstrated, particularly due to the condition of stochastic independence of failures. The use of homogeneous Markov processes has its constraints, since the transition intensities between individual system states are not always constant. In such cases, it is impossible to construct a system of differential equations with time-dependent coefficients. When stochastic dependence is present in failure flows of mechanical systems, the application of Markov process theory remains possible, but the most effective tool for reliability analysis in such cases is the Monte Carlo numerical simulation method.

Across all sectors of the Russian economy, the adoption of digital technologies (DT) is accelerating, with high-tech industries leading the way. The coal industry, like other extractive sectors, has been slower to embrace these solutions, yet digitalization is advancing both at the industry level and within individual companies. One of the most dynamic areas of DT development is the adoption of digital twins (DTw), which form a core element of integrated digital management systems—acting as an integrator for cross-cutting technologies and sub-technologies. This article examines current approaches to studying and implementing digital twins in the coal sector. The objective is to highlight the specific features of digitalization processes, identify barriers, and outline promising directions for the adoption of DTw in the coal industry. To this end, the article systematizes conceptual and applied approaches to DTw, proposes an original framework for defining, structuring, and classifying digital twins based on maturity levels, and identifies both general and industry-specific trends in the development of DT and DTw. The analysis demonstrates that digital twins are a critical tool for managing value chains, and their effectiveness depends on the maturity of production and digital technologies and on the degree of their interoperability. The study compares and evaluates international and domestic experiences of DT and DTw adoption in mining and coal companies, as well as national-level models. It identifies barriers to adoption in the coal sector and offers recommendations for overcoming them. The research applies systems and comparative analysis, bibliographic review, generalization, and expert surveys. Data sources included media reports, websites of leading coal and mining companies, expert assessments, digital project case studies, consulting reports, and primary and secondary expert surveys. The findings show that digital transformation in the coal industry, including the adoption of DTw, lags behind other sectors. This gap is driven by both general and sector-specific factors: high costs and limited resources, scale effects, the absence of a clear development model and digitalization strategy, low levels of automation in production and management, insufficient digital infrastructure, and an acute shortage of personnel with digital competencies, particularly among executives.

It is not unusual for the valuations of mining companies and projects to be debated by mining investors, analysts and regulators. Difficulties understanding geological information, volatility of metals prices, high investment risks and poor historical returns on capital in the mining industry are among the reasons. An additional and important factor is the varying risk profile at different stages of a mining project. A non-zero probability of not advancing to production for a project with a positive feasibility study (FS) requires a careful analysis of its valuation methods and supportive data. Because each mining project is different, and public mining companies are a small part of the market, it's hard to compare them accurately. Once a company prepares a mineral resources report, the exploration costs cease to be a relevant value metric. We offer a practical valuation method for non-producing mining companies, accounting for development stage risks to determine market value. Recognizing the specific attributes of the mining industry, we show that the NPVs calculated using the expected cash flows and discount rates developed using the traditional CAPM framework provide realistic estimates of the project’s value, that compare well to the market indications for the peer groups. We are also investigating the large gap between the NPV values in technical reports and the actual market values of mining companies.