Safety control of the underground coal gasification (UCG) process involves ensuring that the process runs smoothly, monitoring changes occurring on the surface and in the rock mass, and counteracting the negative environmental impact of the process. The article presents the application of a monitoring system implemented in Poland, both in pilot tests conducted in shallow coal seams at the Barbara Experimental Mine and in deep coal seams at the Wieczorek mine, where a full-scale gasification process was tested. The analysis of the monitoring results carried out during the gasification experiment in an operating mine allowed for the identification of key conditions determining the efficiency and safety of the process, pointing to both its strengths and potential limitations in the context of complex mining, environmental, geological and hydrogeological conditions specific to the studied area. Based on the analysis of the data, no significant impact of the studied process on the rock mass or mine water was observed during the experiment. Studies conducted 5 and 8 years after the end of the process did not confirm any long-term impact of the process on water quality in the area of the georeactor's operation.
Two heat treatment processes for a nickeliferous laterite sample are presented, one by the conventional muffle route and the other by microwave. The heating was carried out in order to improve the dissolution of nickel in an acid medium. The study describes the changes observed in the mineral for the increase in temperature as a consequence of radiation in the microwave and in the muffle. The changes in the mineral crystalline phases were analyzed by X-ray diffraction. The leaching media consisted of a 1 M sulfuric acid solution at ambient conditions for 7 h. The interaction between the reagent and the nickel ions is due to the reaction zones generated after heating. Only one of the heat treatments studied increased the dissolution of the metal by 11%. The leaching efficiency of the lateritic mineral showed that the mineral structure and matrix transformation during heating were important in the extraction of nickel. Optimizing the conditions of both thermal treatment and leaching could enhance nickel extraction by increasing its availability in the process.
The topic of the article is the carbon footprint of coal as a primary source of energy. In light of climate change and the energy transition in the European Union, it is increasingly important to analyse the carbon footprint of individual economic sectors. The aim of this review article is to present an analysis of the carbon footprint of the hard coal sector in Poland against the background of the EU’s climate neutrality policy. The paper reviews EU strategic documents, such as the European Green Deal, the ”Fit for 55” package, and the REPowerEU and Clean Industrial Deal initiatives, in terms of their impact on the functioning of the coal sector. It also presents a methodology for calculating the carbon footprint in terms of life cycle assessment (LCA), taking into account different scopes of greenhouse gas emissions according to the GHG Protocol. The article uses databases from the Instrat Foundation, Eurelectric – the Association of the Power Industry, the Polish Geological Institute, the Energy Market Agency, Ember – Electricity Data Explorer and the National Centre for Emissions Management. Using the latest available data from 2018 to 2024, the paper analyses changes in Poland’s and the EU’s energy mix and highlights the importance of the coal sector’s transition in the context of decarbonisation. The challenges associated with mine closures, the social transformation of mining regions and the future of energy security were also discussed. The article provides a starting point for further detailed comparative analyses of the carbon footprint of various energy sources in Poland and the European Union.
Low-permeability coal seams in China are often characterized by mineral-clogged pore-fracture systems, severely limiting coalbed methane (CBM) extraction efficiency. Acidification technology, which involves injecting acidic solutions into coal seams to dissolve minerals and enhance permeability, has emerged as a promising approach. This paper systematically reviews the mechanisms, laboratory findings, and field applications of acidification technology for permeability enhancement in low-permeability coal seams. Key findings include significant improvements in pore connectivity, reductions in gas adsorption capacity, and enhanced gas extraction rates post-acidification. Field tests demonstrate that acid fracturing can increase gas extraction volume by 4-7 times. The study also identifies current challenges – such as acid-rock reaction control, environmental concerns, and limited penetration depth – and proposes future directions, including hybrid acid systems, AI-integrated process optimization, and environmentally friendly alternatives. This review provides a theoretical foundation and practical guidance for the application of acidizing technology in CBM extraction.
The goal of this paper was to inspect the influence of capillary water saturation on the specific strain energy of sedimentary rocks. The relationships between the determined specific strain energies in an air-dried state and in a state of capillary water saturation for detrital rock were exponential, with a strong correlation among the variables. For silt, the relationships were described by a second-degree polynomial function, and the correlation was medium or weak. The relationships for kinetic and dissipated energy were described with a linear function. Pearson correlation coefficients indicated a strong correlation among the variables for detrital rock and a medium or weak correlation for silt. No clear differences were found in the percentage contributions of individual empirical specific energies in the values calculated for the total and dissipated energy. In the case of kinetic energy, capillary-saturation led to a decrease in this parameter. The test results may find application in assessing the possibility of rock mass degradation in active mine areas bordering the flooded workings of closed mines, as well as in active mines with major aquifer water inflows, which have a significant influence on the level of geomechanical and water hazards.
The oil industry has used many enhanced oil recovery (EOR) strategies to increase crude oil production from reservoirs, particularly as production begins to fall. With a significant quantity of hydrocarbon reserves still existing in the reservoirs, improved oil recovery techniques have the potential to increase oil production, with wettability alteration playing a significant role. To date, there has been no comprehensive investigation of wettability in Brunei's sandstone reservoir. The goal of this research is to determine wettability by contact angle measurement and data mining approaches based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. Contact angle tests were conducted on thirty sandstone samples, yielding an average contact angle of 89.2 degrees, suggesting intermediate wettability. Data analysis involves evaluating various enhanced oil recovery methods by conducting an extensive literature review and examining case studies. The brine used in the study was a 3% NaCl solution with a density of 1.05 g/cm3 and viscosity of 1.5 cp, and the synthetic oil had a density of 0.85 g/cm3 and viscosity of 15 cp. The TOPSIS algorithm identified cationic surfactant flooding as the most suitable option for modifying wettability to increase oil recovery. The results emphasize the efficacy of cationic surfactants in changing wettability and enhancing oil recovery in intermediate-wet sandstone reservoirs.
Accurate estimation of coal pillar strength is essential for ensuring safety and operational efficiency in underground mining. Current assessment methods often face limitations in addressing time-dependent failure mechanisms, geological discontinuities, and dynamic loading conditions. This paper identifies future research directions aimed at enhancing the reliability of pillar strength evaluations. One important focus is the development of advanced numerical models that account for time-dependent behaviours such as creep and fatigue. Incorporating multi-scale modelling techniques, which connect micro-scale material responses to macro-scale structural performance, may lead to more precise predictions. The integration of real-time monitoring systems measuring stress, deformation, and environmental factors into predictive models can enable continuous assessment and proactive management. When combined with machine learning algorithms analysing large datasets and recognizing patterns, these approaches can optimize predictive accuracy and maintenance strategies. Improved geological characterization using advanced mapping technologies, such as geophysical surveys, is critical to account for weak planes, fractures, and faults. Additionally, long-term field monitoring and laboratory experiments are necessary to validate and refine models. Establishing standardized regulatory guidelines will help ensure consistency, particularly in challenging mining environments. Collaboration between academia and industry is essential to drive innovation and develop robust, reliable methods for coal pillar strength estimation.
While mining can drive economic growth, create jobs, and promote infrastructural development, it also poses risks the environment, human health, and social well-being. Therefore, to achieve sustainable development, responsible mining is crucial. This study empirically examines the role of institutional quality in the mining-sustainable opment nexus in Sierra Leone. With the use of the planetary pressures-adjusted human development index (PHDI) comprehensive measure of sustainable development, the study is able to integrate economic, social, and environmental dimensions. The autoregressive distributed lag (ARDL) model confirms the existence of both short-run and long dynamics. The results of the study show that the contribution of mining to sustainable development depends institutional quality, as framed by the resource curse hypothesis and institutional theory. The results indicate that institutions worsen the adverse effect of mining on sustainable development in Sierra Leone. Policy reforms strengthen institutions and promote economic diversification will help regulate mining and transform mineral into sustainable development. This study is an advancement of the discourse on mining and sustainable development amidst weak institutions with insights from a resource-dependent economy.
This paper focuses on the methodology involving ground penetration radar (GPR) for detecting and localizing random cavities in underground limestone soil, in conjunction with geotechnical data. A GPR survey was conducted alongside field and laboratory geotechnical tests at a field where mining activities are now promoted (Saudi Arabia Northern Borders Province). The paper presents correlations between the instantaneous amplitude of the reflected electromagnetic (EM) waves and rock quantification distributed (RQD), recovery rock parameter (REC), and uniaxial compressive strength (UCS ). The GPR investigation proved to be an efficient nondestructive geophysical method for determining: 1) the 3D location of cavities, 2) their random distribution, and 3) an approximate assessment of their shapes and dimensions. These correlations serve as guideline-relations linking GPR, as a geophysical method, with common geotechnical results (including drilling issues and Standard Penetration Tests/SPT). In the context of constructing foundations in limestone with underground cavities, these correlations between GPR and geotechnical tests have been instrumental in guiding soil improvement through the cement injection technique. The proposed correlation between RQD and instantaneous amplitude can serve as a mathematical relationship to streamline future project construction decisions in karstic sites, particularly in the initial phases of construction planning and facility management.
This paper presents the results of quality testing performed on a stoker-fired laboratory boiler to present the technical potential of the boiler as a tool for optimising the combustion process of prepared solid fuels (granulate, energy blend) in the context of current demands related to the decarbonisation policy. The comprehensive testing of such fuels at a laboratory scale makes it possible to gain deep insights into the quality parameters of the fuel, flue gas and combustion waste, as well as to determine the combustion process parameters before the fuel is introduced for combustion in broader power engineering and household boilers. The emissivity measurements presented in the paper were conducted on a granulate sample and two energy blends. Furthermore, after the combustion experiment was concluded, the combustible component contents in the obtained combustion waste were determined to confirm the correct course of the combustion process. The stoker-fired laboratory boiler makes it possible to precisely reproduce the combustion process conducted in professional power engineering, but at a laboratory scale. This is thanks to the option of changing the settings by means of a computer system. The boiler can be used to test various types of solid fuels (solid biofuel, solid recovered fuel, blends, granulates).
Mining activities often cause mining-induced ground deformation, including subsidence and landslides, and related geo-environmental impacts, posing significant risks to infrastructure and safety. This study conducts a systematic assessment to identify, categorize, and evaluate AI-based methods (machine learning, deep learning, and hybrid models) for predicting and monitoring mining-induced ground deformation. The literature search was performed across major scientific databases, using predefined keywords and selection criteria, resulting in a final dataset of relevant peer-reviewed studies. The reviewed works were classified into three methodological groups: traditional machine learning, deep learning-based approaches, and hybrid methods. The results show that ML still dominates in terms of accuracy and explainability, DL has the potential to handle big data and real-time prediction, but requires large datasets and computational resources, and Hybrid combines the advantages of ML and DL to increase the efficiency of prediction and risk assessment. Besides, the study highlights the strengths and weaknesses of each approach and suggests future research directions. By systematically classifying and comparing AI-based prediction and monitoring approaches in terms of data requirements, accuracy, interpretability, and real-time capability, the presented results provide practical guidance for mine managers and engineers in selecting appropriate models for deformation risk assessment and in developing effective early warning systems for safer and more sustainable mining operations.
Methane plays a significant role in intensifying the greenhouse effect, possessing a global warming potential more than twenty times greater than that of CO2 when measured on a carbon-dioxide-equivalent basis. Hence, identifying efficient methods to mitigate methane emissions is crucial for safeguarding the environment, ensuring economic viability, and maintaining practicality. Globally, research initiatives are dedicated to crafting an effective catalyst system for methane oxidation, which involves developing catalysts, comprehending their characteristics, and determining the best way of bonding them to structural supports to enhance functionality. Notably, systems that incorporate metals from the non-noble d-block of the periodic table as the active element, supplemented by small amounts of noble metals like palladium to boost their efficacy, are showing significant promise. The VAM-PiRE project, a collaborative endeavour involving the Technology Transfer and Promotion Centre in Katowice, the Central Mine Institute National Research Institute, and Jagiellonian University in Krak & oacute;w, aims to develop a highperformance modular reactor that leverages these active catalysts. Preliminary results from this project are encouraging, suggesting that this novel strategy could be effectively upscaled and integrated into mine ventilation systems to reduce methane emissions with simultaneous cold production for airconditioning purposes.
The global economy heavily relies on the mining industry for essential resources such as coal, oil, gas, and metal ores. However, the intricate nature of mining operations poses significant challenges in supply chain management (SCM). This research investigates how blockchain technology can address these challenges within mining supply chain management (MSCM). Through a systematic review of existing research and projects, a conceptual blockchain model is proposed to improve mining supply chains' transparency, traceability, efficiency, and sustainability, specifically focusing on coal supply chain management in Vietnam. The model integrates distributed ledgers, smart contracts, IoT devices, identity management, and consensus mechanisms to streamline operations and maintain data integrity. Implementation through Ethereum smart contracts with Proof of Authority consensus demonstrated significant advantages over traditional systems, enabling automated inspection verification, logistics verification and management, and real-time inventory tracking. Environmental compliance monitoring is substantially enhanced through tamperproof emissions data recording and streamlined regulatory reporting. The study also identifies critical implementation challenges and provides comprehensive solutions to address scalability, regulatory implications, interoperability, and data privacy considerations. These findings provide a strategic roadmap for mining industry stakeholders to successfully integrate blockchain technology into their supply chain operations.
The aim of this study is to analyze the occupational injury data of Indian underground metalliferous mines for scrutinizing the injury proneness of different groups of mine workers. In this context, injury records from 2011 to 2022 were obtained from underground metalliferous mines situated at Eastern part of India. The data were characterized and segregated based on different individual and workplace level variables. The workplace injury is categorized as 'no injury' and 'all injury'. Subsequently, Frequency and Classification Based analysis (FCBA), Standardized Injury Rate (SIR) analysis and Logistic Regression Model (LRM) analysis were performed sequentially (FCBA-SIR-LRM) to predict the susceptibility of injuries of different worker groups and their categories. The result shows that the age, experience, occupation, workplace and mine variables have significant influence on accident susceptibility. Based on the results, potential causes of accidents were assessed and suggestions for reducing the likelihood of their occurrences were discussed in detail.
The location of an orebody known to show regional variability in space is determined only through 3D geological modeling. A reliable 3D orebody model must be obtained to ensure resource efficiency and sustainability in the mining industry. In addition, 3D geological modeling has become indispensable in intelligent mining, smart mining, and mining 4.0. In previous years, explicit methods were widely used for 3D geological modeling. However, the validity of geological models produced with these methods has been debated. It has been understood that they are inadequate in modeling vein-type mineralizations with complex geometry in particular. For this reason, implicit modeling methods, which have gained popularity recently, have been proposed for these types of deposits. This study aims to investigate the usability of the implicit modeling method in obtaining a 3D geological model. In addition, it attempts to provide an important resource to the literature for researchers who will conduct 3D modeling studies. For this purpose, a sample chrome field was selected, and 3D modeling of the ore was performed. As a result, it was determined that the implicit modeling method was close to reality and quite reliable.
The mining industry faces evolving challenges in adapting to the impacts of climate change. Among these challenges is the management of tailings. This study focuses on investigating surface disposal of non-cemented paste tailings, particularly the effect of water content, freeze-thaw cycles (FTC, defined as freezing the sample to-20 degrees C and thawing it to +20 degrees C), and ambient temperature on the mechanical characteristics and thermophysical properties of paste tailings. The potential for maintaining non-cemented tailings in a frozen state year-round is also investigated through field-scale numerical simulations. Experiment results of thermophysical properties of tailings with water content, freeze-thaw cycles and ambient temperatures varying in the 0-30 wt degrees%o range, 0-15 range and-5--15 degrees C range, respectively, suggest higher thermal conductivity of paste tailings compared with dry porous tailings, particularly at lower temperatures. The highest unconfined compression strength of 0.93 MPa is obtained for the sample with 30 wt degrees%o water content, subjected to 15 FTCs at-15 degrees C. This study suggests that, unlike frozen cemented paste tailings, frozen non-cemented paste tailings exhibit higher strength when subjected to more FTCs, making them more resilient in cold regions. Furthermore, the field-scale numerical simulation results support the practicality aspect of frozen paste tailings as a climate change adaptation strategy if combined with artificial ground freezing.
Underground mine ventilation is crucial for ensuring worker safety, air quality, and operational efficiency. Effective optimization of ventilation systems reduces energy consumption, enhances airflow distribution, and minimizes risks associated with harmful gases and fire hazards. This study proposed an integrated framework combining the Hardy Cross (HC) method, Monte Carlo (MC) simulations, and machine learning (ML) techniques to optimize the ventilation system at the Jabal Sayid mine in Saudi Arabia. The HC method was employed to provide deterministic baseline airflow calculations, while the MC simulations accounted for uncertainties in resistance values and environmental conditions, generating probabilistic distributions of key parameters. Among the five ML algorithms tested (ANN, RF, GB, SVM, and LSTM), the LSTM model demonstrated superior predictive accuracy, particularly for dynamic, time-dependent parameters. The hybrid HC-MC-ML model integrated these approaches to achieve comprehensive ventilation optimization. Results indicated a 12.8% reduction in airflow resistance, leading to enhanced fan efficiency and a significant improvement in energy consumption. For instance, fan system efficiency increased by up to 7% in combined operations, while resistance values consistently decreased across all scenarios. Additionally, the hybrid model effectively managed exhaust gas emissions, maintaining pollutant concentrations within permissible limits by dynamically adjusting airflow routes. The findings demonstrate that the HC-MC-ML framework not only improved energy efficiency but also ensured safe and sustainable mine ventilation.
This article explores the technical principles of pneumatic seismic sources, focusing on their application in underground mining operations, including environments prone to fire hazards. It introduces an intrinsically safe pneumatic seismic source as a cost-effective solution for quasi-continuous seismic excitation, useful for detecting stress concentrations in mining settings. These stress concentrations are identified through anomalies in seismic wave velocity, which may indicate potentially hazardous deformations in the rock mass. The developed system enables fully remote operation from the surface and integrates with a seismic monitoring system to capture high-resolution waveforms. The study provides an overview of the pneumatic system's technical specifications and operational framework. Preliminary tests conducted in underground conditions demonstrate the system's effectiveness. Trial shot results show a high signal-to-noise ratio for seismic waves, with frequencies reaching up to 500 Hz, recorded as they traveled through a 200-meter-long coal seam longwall.
The establishment of a coal mining downstream ecosystem focused on a sustainable and environmentally friendly economy is an important goal for many countries. One strategic step in achieving this goal is the utilization of coal fly ash (CFA) waste as a secondary source of rare earth elements (REE). The use of CFA provides a solution to the environmental and health challenges resulting from mining primary sources. Prior to developing REE extraction technology, CFA characterization must be conducted. Therefore, this study addresses the relationship between CFA characteristics and the presence of REE. Based on the ASTM C618-12 standard, CFA in Indonesia is dominated by type F, with Ce being the most abundant REE element. Potential locations for REE sources in Indonesia include power plants in Sumatra, Kalimantan, and the western part of Java. There is a significant correlation between the oxide contents of SiO2, Al2O3, and Fe2O3 with the LREE and CREE groups in CFA in Indonesia. Correlation analysis shows a strong positive relationship between LREE and SiO2 (0.90) and Al2O3 (0.95), while Fe2O3 shows a negative relationship (-0.58). However, Fe2O3 also contributes positively to the presence of CREE (0.89).
In short-term mine planning for deposits characterized by multiple variables, sequential diglines are generated with an emphasis on maintaining consistent grade distributions across mining periods. This research integrates an array of mineral variables, including deleterious elements, to facilitate the establishment of excavation geometries that enable precise grade and volume control within operational production zones. Through the systematic application of geostatistical analysis coupled with process optimization, geological risks associated with grade uncertainties are minimized, and an optimized operational sequence for the extraction of mineral blocks is pursued. The main point of this approach is maintaining grade distributions aligned with the historical mean grade, with low variability. The use of genetic algorithms optimizes block selection by incorporating the location, interaction, and efficiency of shovels as seed points. The results demonstrate that the methodology generates weekly schedules that enhance the stationarity of grade distribution, thereby improving operational efficiency and reducing risks
