Innovative and Responsible Mining for Inclusive Growth

Geosciences are critical to addressing global challenges, including sustainable energy production, climate change mitigation, and resource security. Optimizing the exploration and management of a variety of energy resources, including fossil fuels, geothermal sources, and critical minerals required for renewable technologies, requires an in-depth understanding of Earth's complex systems. The inherent complexity, multi-scale, multi-dimensional, multi-variate, and variable quality of geoscience data, which comes from diverse sources like satellite imagery, seismic surveys, borehole measurements, and laboratory analyses, poses a grand analytical challenge. Overcoming these challenges necessitates the development of robust methodologies for data integration, uncertainty quantification, and advanced computational frameworks, alongside holistic and interdisciplinary collaboration. A number of exponential technologies offer a transformation path for advancing geosciences. Some of these technologies, such as Big Data, cloud computing, artificial intelligence (AI), generative AI, augmented and virtual reality (AR/VR), the Industrial Internet of Things (IIoT), drone and robotic systems, wearable devices, and the Metaverse, are maturing rapidly. Although these technologies have some limited uses in the geosciences, their full potential to speed time-to-value has not yet been fully realized. Utilizing the maturity and accessibility of these emerging technologies and tools is essential to finding hidden inefficiencies and unknown insights and creating innovative solutions in geosciences. To that end, this paper presents transformative pathways to maximize the value of Geosciences with these emerging technologies, exemplified by practical case studies.

The Gujarat Mineral Development Corporation’s (GMDC) Ambaji mine, situated within the Proterozoic Aravalli Fold Belt, is a promising site for base metal exploration, particularly copper (Cu), lead (Pb), and zinc (Zn). The region's polymetallic mineralization is primarily associated with metamorphosed Volcanogenic Massive Sulphide (VMS) deposits. Indicator minerals and pathfinder elements are crucial tools in mineral exploration, aiding in the identification of ore deposits by revealing geochemical anomalies associated with mineralization. This pilot study was carried out to assess concentration of indicator minerals and pathfinder elements within the Ambaji mining region. Field sampling, petrographic analysis and advanced characterization, including X-ray diffraction (XRD), inductively coupled plasma-optical emission spectrometry (ICP-OES), and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM–EDS) were conducted. These methods facilitated the characterization of ore-forming processes, identification of hydrothermal alterations, and differentiation between mineralized and barren zones. The ICP-OES study identified chalcopyrite (CuFeS₂) and covellite (CuS) as primary ore minerals, while hematite, magnetite, chlorite, and biotite were recognized as major alteration minerals formed due to wall rock alteration by mineralizing fluids. Additionally, pyrite and staurolite were identified as key indicator minerals for copper mineralization in the region. The geochemical data analysis revealed a reasonably good trend between the concentration of pathfinder elements and proximity to ore deposits. The SEM–EDS spectrum shows presence of MgO, Al2O3, SiO2, K2O, CaO, FeO compounds. Moreover, the pilot study indicates a consistently distributed Cu-Pb–Zn mineralization trend within specific lithological units, making a cause for acceptance of geochemical indicators in exploration. The study not only confirms the application of these indicators for base metal exploration in the Ambaji region but also provides a methodological framework for similar mineral-rich regions globally. Additionally, it emphasizes the need for continued research in understanding fluid-rock interactions and their role in ore deposition within the Aravalli Fold Belt.

A Certified Reference Material (CRM) is essential for ensuring accurate analytical measurements, serving as a benchmark for verification, validation, and calibration of test procedures. In accordance with ISO 17034:2016, a CRM for high ash coal was developed to support quality assessments in the coal industry. This CRM provides precise property values, including ash content, volatile matter, and gross calorific value (GCV). An initial batch of 700 candidate reference material bottles was prepared and stored under controlled conditions (18 °C) to maintain homogeneity and stability. The homogeneity study addressed within-unit and between-unit heterogeneity, with two-way ANOVA analysis confirming no significant variations (p-value > 0.05). Additionally, short-term and long-term stability studies were conducted. An Inter-Laboratory Comparison (ILC) study involving ten laboratories across the country was conducted to assign the property value. After considering the uncertainty associated with homogeneity, stability and the property value assignment, the tentative values are GCV (4213 ± 41 kcal/kg), ash content (40.74 ± 1.93 %) and volatile matter (27.45 ± 3.51 %). This indigenous reference material for high ash Indian coal strengthens coal analysis standards, ensuring enhanced precision and consistency for researchers and industry professionals.

Sonic travel time well logging of exploration boreholes is acquired to derive a reliable equation to interpret the roof rock strength (uniaxial compressive strength) above the coal seams. Because sonic velocity logs are relatively inexpensive and easy to obtain during exploration, the technique can provide us with an abundance of strength data and can be used to predict the underground strata control design. However, the technique depends on reliable correlations between the uniaxial compressive strength (UCS) and the sonic travel time response of the same lithounit. The development of a correlation between the physico-mechanical properties (UCS) of lithounit’s from three boreholes and their response in relation to sonic travel time is described in this work. The study is conducted for Jamadoba colliery in Jharia Coalfield, where sonic travel time logs were compared with uniaxial compressive strength for a broad range of rock types. The relationship between UCS and sonic travel time for the entire set of data indicates that sonic travel time decreases as rock strength increases. The R2 value for this equation is 0.81 out of 152 samples, indicating a strong correlation between sonic travel time and laboratory tested UCS data. The objective of this study is to predict the strength of the roof rock above the coal seam prior to mining so that necessary safety precautions can be taken during excavation.

The arcuate shaped Anorthosites of Sittampundi Anorthosite Complex (SAC), is clearly observable from satellite images. Chromites occur as lense shaped outcrops in the anorthosite and related mafic rocks. Mapping chromite through remote sensing image analysis in this region is challenging due to the sparse occurrence of the host rock. In this study, we utilised the Spectral Mixture Analysis (SMA) technique on AVIRIS-NG datasets to generate the spectral abundance of chromite hosted pyroxenite rocks. For this purpose, the indicative spectral features of the abundant rock type, i.e. anorthosite and its difference from the pyroxenite/metagabbro were analysed and characterised in the laboratory. Based on the diagnostic absorptions, the image endmembers were corelated with the field and the laboratory-derived spectral characteristics of the rock endmembers and the most abundant minerals associated with each rock type. Although, the sparse occurrence of anorthosite and pyroxenite outcrops in the study region poses a challenge, a greater challenge arises from the significant vegetation cover, which results in intimate mixtures of rock, soil and vegetation in the image data. Therefore, to derive the fractional abundance of chromite hosted pyroxenite, vegetation is considered as one of the endmembers. Non-linear spectral mixing approach resulted in better mapping of sparse outcrops of chromite hosted rocks. This new approach helps in mapping mineral prospects in vegetation-covered regions without applying masking.

Extreme environmental conditions can significantly affect the structural integrity of geological materials, with high temperature exposure playing a key role in altering rock mechanical properties. Understanding these effects is crucial for applications in geotechnical engineering and geothermal energy extraction. This study explores the effect of thermal damage on the roughness of extensional cracks in Gondwana Supergroup sandstone subjected to temperatures ranging from 100 to 700 °C. After thermal treatment, the mechanical strength of the sandstone was assessed using Brazilian tensile tests. The roughness of extensional cracks was analysed using Fast Fourier Transform (FFT). Fracture traces were digitized and examined with FFT to extract frequency components of fracture surfaces. From the Fourier Power Spectrum, fractal dimension and Hurst coefficient were calculated, revealing a correlation between increasing temperature and fracture complexity. Results demonstrate that thermal stress increases fracture roughness, with higher fractal dimension values observed at elevated temperatures. Thermal damage promotes the development of microstructural flaws, which in turn influence crack propagation by increasing surface roughness. These findings provide valuable insights for assessing the integrity of fire-damaged rock and improving geothermal reservoir design. The application of FFT-based fractal analysis presents a novel and quantitative method for characterizing thermally induced rock damage.

Paramanahalli gold deposit is located approximately 7 km west of the Chitradurga Shear Zone (CSZ) within the Chitradurga Greenstone Belt (CGB) in Karnataka. CGB has undergone three major deformation phases. The first phase (D1) is characterized by isoclinal folding and the development of foliations within the meta sedimentary rocks. The regional “Chitradurga Fold” and N-S trending CSZ are the outcome of D2 deformation. During the third phase (D3) of deformation, the D1 and D2 structures were refolded into E-W trending warps and kink folds. Gold mineralization at Paramanahalli is confined within the altered, deformed metabasalt, Banded Iron Formation (BIF), and quartz veins. Folds and faults are the major deformational features, which are observed during fieldwork and within the megascopic samples. Microstructures such as strain fringes, rotation of foliation, mica fish, kink bands, micro folds, quartz pods, and pinching/swelling of veinlets infer ductile deformation. Brecciation of quartz and displacement of quartz-magnetite bands within the BIF are evidence of brittle deformation. Gold mineralization in Paramanahalli is associated with D2 deformation, where CSZ played an important role as a conduit for ore-bearing hydrothermal fluids. The detailed understanding of such types of deformational features can be useful to understand gold metallogeny within the CGB and provide valuable inputs for future exploration and resource discovery.

Late Oligocene coal-bearing horizons are found in the state of Arunachal Pradesh, Nagaland, and Assam and are associated with Tikak-Parbat Formation of Assam- Arakan Basin, northeast India. The Makum coalfield in Assam exhibits a considerable thickness of these late Oligocene coals. In the present study, organic geochemistry and petrography were applied on Makum coals for evaluating the source, thermal maturity, and hydrocarbon generation potential of organic matter. Petrography reveals that vitrinite constitutes the dominant maceral group (80.2–92.5% on a vmmf basis), followed by liptinites (4.0–15.4% vmmf) and inertinites (2.6–6.1% vmmf). High TOC (65.02–76.79%) and S2 (225.74–305.42 mg HC/g rock) values from Rock–Eval pyrolysis depict that the studied coal samples are prolific hydrocarbon source rocks. High hydrogen index (HI: 305–424 mg HC/g TOC) and low oxygen index (OI: 2–7 mg CO2/g TOC) classify the organic matter of the studied samples as hydrogen-rich Type II to II/III kerogen. Furthermore, petrographic study shows sufficient quantity of hydrogen-rich liptinite macerals. Rock–Eval Tmax (419–436 ºC) and vitrinite reflectance (0.49–0.52%) values with a very low production index (PI: 0.01–0.05) suggest that the coals are thermally immature. High HI and genetic potential (GP) values depict that these late Oligocene coals may produce mixed oil and gas upon maturation.

Carbon Capture and Storage (CCS) is crucial for reducing CO₂ emissions, and depleted oil and gas fields offer ideal storage sites due to their well-understood geology and proven trapping capacity. This study outlines key geological screening criteria for CCS site selection, focusing on reservoir quality, caprock integrity, structural stability, and long-term risks. Effective storage requires evaluating reservoir porosity, permeability, and depth to ensure optimal CO₂ injectivity and capacity. A reliable caprock (e.g., shale or salt layers) is essential to prevent leakage, while structural assessments must address fault stability and fracture risks. CO₂ trapping mechanisms—structural, solubility, residual, and mineral—vary by geology and must be optimized for secure storage. Geomechanical (pressure changes) and geochemical (rock-fluid interactions) factors further influence long-term containment. Monitoring via seismic surveys and gas analysis helps track CO₂ migration and detect leaks. Despite their advantages, challenges remain in predicting long-term storage behavior. Advanced modeling and pilot projects are needed to refine risk assessments. By integrating reservoir characterization, structural analysis, and geochemical evaluations, CCS in depleted hydrocarbon reservoirs can safely contribute to global decarbonization, leveraging existing data and infrastructure for efficient implementation.

In the recent past a paradigm shift in terms of resource utilization and beneficiation practices is experienced by the Indian mining and mineral sector. Depleting iron ore reserves coupled with increasing demand for high grade fines to improve blast furnace performance in terms of productivity and reduced slag rate necessitate intensive beneficiation of iron ore. Iron ores of different regions exhibit unique beneficiation and metallurgical characteristics. Sometime even different iron ore geotypes within a single leasehold are quite different in terms of mineralogy and textural attributes which make them respond to specific beneficiation technique. The Iron Ore Group in Singhbhum-Keonjhar region are hosted by a laterally extensive thick banded iron formation (BIF) in a folded greenstone belt succession of Paleoarchean age. The massive hard, flaky-friable, blue dust and lateritic varieties of iron ores associated with banded hematite jasper (BHJ) and shale have been traditionally mined. However, such high-grade reserves are fast depleting therefore it has now become imperative to perform detailed characterisation, beneficiation and metallurgical studies of the low-grade ore present in the region. The paper presents the characterization of low-grade iron ores from the region through volumetric chemical analysis, X-ray fluorescence (XRF), X-ray diffraction (XRD), optical microscopy, scanning electron microscopy (SEM), electron probe microanalyzer (EPMA) and SEM–EDS based automated mineralogy. Automated mineralogy based on SEM–EDS liberation analyser named “Maps Min-3.28” is used to find out the Modal, elemental deportment, grain size, mineral association and liberation characteristics. The effects of elemental variation in different phases and textures are also discussed in relation to the beneficiability of low-grade ores.

Sublevel open stoping is widely used in many underground metal mines both in India and abroad. The major risk associated with this method, in terms of both stability and economy, is overbreak. For assessing stability, numerical analysis is adopted in many mines. The significance of varying geological and geotechnical parameters in geotechnical design, especially numerical analysis is often ignored or underestimated. Geological information about the host rock and other concomitant geological disturbances are the major factors to assess any potential failure. A significant challenge in geotechnical analysis arises from the fact that most existing design approaches—be they empirical, analytical, or numerical—are mainly developed for isotropic rock mass conditions. As a result, the influence of anisotropy is often underestimated or entirely neglected to simplify the design process and conform to conventional practices. Nevertheless, anisotropy can have a profound impact on the stability of underground excavations and must be carefully considered during geotechnical design. In many instances, practical observations indicate that anisotropy may exert a greater influence on failure mechanisms than stress regime. This paper demonstrates the importance of lithological contacts in stope design through numerical modelling in FLAC3D, using the Improved Unified Constitutive Model (IUCM) as the constitutive model. By comparing the results of an isotropic model with one that considers weak lithological contacts, it is found that accurate geological modelling and geotechnical characterization are crucial for any critical stope design. The paper also illustrates the adoption of IUCM as a constitutive model and validates its effectiveness with field results, highlighting the benefits of using IUCM in daily geotechnical model preparation.

The success of highwall mining in multi-seam conditions depends largely on proper mining sequence and web pillar design. Highwall web pillars, being long and slender, are more susceptible to failure. Mining coal seams with < 3 m parting causes geotechnical issues like strata instability, seam interactions, stress concentrations, spalling, pillar collapse, and subsidence. Parting stability is governed by several factors such as cover depth, parting thickness, eccentricity between roadways, extraction ratio, in-situ stress, and rock competency. Pillar failure, whether progressive or instantaneous, is primarily controlled by the width-to-height (w/h) ratio; undersized pillars—spalling extends inward, eventually leading to collapse. Additionally, failure of thin partings may lead to heightened pillars with low w/h ratios, making them unstable. A case study on the proposed highwall mining at Gare Palma IV/1 mine, operated by M/s Jindal Power Limited, has been discussed. Highwall mining is planned for three coal seams viz. IX, VIII, and VII(T + M)—measuring 5 m, 4 m, and 6 m thick, respectively. The cover depth in the mining area varies from 78.43 to 108.75 m. The IX seam, the uppermost seam, lies 2 m above the VIII seam, while the VIII seam is separated from the VII(T + M) seam by 9 m. The study aims to design an extraction methodology for coal seams with low parting.

Extraction of coal using Continuous Miner (CM) technology in Indian coalfields has been found to be satisfactory up to 300 m depth under easy-to-difficult caveable overlying strata. However, the presence of geological discontinuities remains one of the major challenges for the safe development of unsupported cut-out distance by CM. The CM technology is also deployed at depths exceeding 300 m in a few mines (Churcha RO underground mine, SECL; VK-7, VKP Mine, and Shantikhani, SCCL), where strata mechanics issues affect the performance of underground structures, including loss of life and natural energy resources. At the greater depths, redistribution of in-situ stresses impacts the efficiency of the bord and pillar (B&P) method of mining. Around 128 billion tons of coal are available at depths of 300–600 m in different Indian coalfields, which require suitable technology and methodology for safe and efficient extraction. The CM technology has been successfully deployed at depths of up to 600 m in Australia to extract coal using an alternative method to the B&P method. An attempt has been made to alleviate the challenges associated with CM workings. This paper also presents a possible novel mining method for deeper cover at an SCCL mine using CM technology.

Efficient coal extraction from underground mines is largely dependent on the stability of coal pillars, especially in the presence of geological discontinuities that weaken their strength. In Indian coalfields, traditional empirical formulas for pillar strength often neglect these discontinuities, focusing instead on pillar dimensions and coal strength. This study employs numerical simulations to evaluate the impact of multilayer weak bedding planes, placed at various positions within the pillar, on its strength. Initially, single-layer weak beds with thicknesses between 0.2 and 0.5 m were modeled at different positions (roof-interface, middle, and floor-interface) of the pillar, showing significant strength reductions, particularly at the roof interface. Expanding the study to multilayer weak beds (up to five layers) with varying thicknesses further demonstrated that both the number and position of these beds critically influence pillar strength. The results underscore the necessity of accounting for geological discontinuities when designing coal pillars, as their presence can substantially reduce load-bearing capacity and compromise mine safety.

Depillaring a panel surrounded by old goaved-out area experiences strata mechanics challenges in terms of instability of barrier pillars and production pillars due to re-distribution of the abutment load. The study focused on panel 89LW of Churcha RO Underground Mine, surrounded by goaf in three sides, and being extracted using continuous miner (CM) based mass production technology. A comprehensive numerical modelling, field experimentations and strata monitoring study is conducted for safe extraction. Numerical modelling results revealed that the barrier pillar bears the majority of the abutment load from the adjacent goaved-out panels. It is observed that the maximum absolute stress is developed in case of unsettled goaf compared to the settled goaf. Drill yield experiment confirmed that the panel is not prone to coal bump. The support system is efficiently stabilizing the immediate roof as no strata movement observed through dual height tell-tale (DHTT). Maximum vertical induced stress of 98.59 kg/cm2 over snook when the pillar was extracted. Around 50% of the panel is successfully extracted till date and the field and numerical modelling studies revealed that no impact of old settled goaf noticed. This study will be helpful for mining industry during CM-based mechanised depillaring under similar geo-mining conditions.

Spalling in coal pillars leads to structural instability in deep underground mines, especially as depth increases. This study investigates spalling during seam development at CM Panel No. 95 LE, Seam V, Churcha Mine (RO), SECL, situated at a depth of 385 m, with a 6.0 m width and an average gallery height of 3.8 m. A finite difference-based numerical modelling approach assessed spalling under various geo-mining conditions. Varying depths from 60 to 600 m. Results, supported by field observations, carried out parametric analysis, show spalling begins at around 240 m and intensifies up to 600 m. At depths below 150 m, pillars remained stable with no deformation. Increased depth correlates with rising rock load height (RLH), indicating higher stress concentration. RLH values recorded at 240 m, 360 m, 500 m, and 600 m were 2.92 m, 3.15 m, 3.73 m, and 4.12 m, respectively. Field investigations confirmed spalling up to 1 m, highlighting the need for confinement in affected areas. GRP bolts were used to stabilize spalled sections. The Factor of Safety (FOS) increased from 2.03 (unbolted) to 2.55 (bolted), emphasizing the effectiveness of bolting support in enhancing coal pillar stability.

The movement of strata or ground failure, encompassing “Fall of Roof” and “Fall of Sides”, is one of the major causes of accidents in the underground coal mines in India. Although a lot of research has been carried out in India and abroad to address the stability of the roof and the required roof support measures, the stability of the sides has not received similar treatment in India. Despite being one of the major causes of accidents, the stability of the sides has not been given proper attention. However, as underground mining progresses to deeper horizons and mechanised single- lift extraction methods begin to be extensively adopted with increased height of extraction, the instability and vulnerability issues of the sides need focused attention. At deeper horizons, the direction and magnitude of horizontal stress play a major role in the stability of the sides, especially the interface between the pillar and the roof. The presence of massive, difficult-to-cave strata within the caving horizons, hidden slips in the coal seam, weak strata, fault zones, etc., often exacerbate the issue at hand. In the recent past, several instances of side falls have occurred at a few deep underground coal mines, causing fatal accidents leading to loss of human lives and man-hours. A few mitigation measures such as re-orientation of the galleries, leaving larger barrier pillars and installation of side supports have been adopted and found to improve the stability of sides. The present paper discusses the risks posed by side fall hazards and their mitigation measures elucidated using the case of an accident caused by a side fall that occurred at a deep underground coal mine in the recent past.

Progressive goaf advancement during extraction of coal in Continuous Miner (CM) workings induces significant stress redistribution, deformation, and failure zones within the active mining zone (AMZ). Analysing these effects on underground workings is essential to understand the global stability of mine. Thus, this paper presents combined approaches utilising field investigations and numerical modeling with FLAC3D to examine the effects of real-time progressive goaf advancement in CM workings. The simulation results indicated that induced stresses mounted up at the immediate goaf-adjacent pillars, leading to increased stress concentration at the pillar edges and gradual stress dissipation towards the pillar core. Vertical displacement in the roof intensifies with advancing goaf, with junctions experiencing the highest deformation due to stress concentration and weakening of the immediate roof strata. Further, an attempt was made to identify the caving zone and its angle based on varying geomining effects during extraction. Thus, these findings provide critical insights into the behaviour of underground coal structures under dynamic mining conditions, emphasizing the importance of effective ground control strategies for safer and more efficient coal recovery.

The mining industry has a high fatality rate and a number of non-fatal accidents that make it one of the most hazardous occupations in the world. These accidents often result in deaths, injury, destruction of machinery, and directly impact the productivity of the mines. Therefore, prediction of root cause of these accidents is crucial to take appropriate preventive action plan. In this study, the probabilistic relationship among the accident root causes, effects of accidents, and some additional parameters are developed using Bayesian network (BN) model and construct the conditional probability table (CPT). We consider 224 accidents cases which occurred in Indian underground mine from 2010 to 2023. The three-axiom based sensitivity analysis is used to validate the developed BN model as well as identify the most sensitive parameter that are responsible of the occurrence of accident. Additionally, the proposed research work will help us to develop an intelligence decision support system for safety governance and identify root causes to improve mine monitoring and safety.

Sandwich Belt high angle conveyors offer a direct continuous bulk transport path from within the open pit to the surface. This system has the technical characteristics of conventional conveyors (unlimited capacity, ability to handle primary crushed ore and waste with conventional belts that can be scraped clean and utilization of all conventional conveyor equipment subject to the rules of their design) but without any limitation on the conveying angle. As with conventional conveyor haulage many of the mining and bulk transport schemes require various degrees of portability and mobility. All of the portability and mobility schemes required have already been demonstrated repeatedly throughout the world at the mining operations that used only the conventional open troughed belt conveyor haulage. This writing describes the adaptation of such portability and mobility schemes at the Dos Santos Sandwich belt high angle conveyors. From 1981 to the present, it tracks the various installations with reference to past articles addressing mobility in mining (1983); Cananea Mobile HAC in Heap Leaching (2002); a Mobile Snake Ship Loader (2006); Queensland Energy Resources QER mobile/portable high angle conveyor system at end-wall (2010); the FMG-UHAC (2011) and finally the mobility schemes for current mining projects in India.

Human reliance on the environment persists, evolving from direct resource extraction in the Stone Age to complex industrial processes today. The mining industry, a key provider of raw materials, faces increasing pressure to mitigate its environmental impact, particularly concerning lubricant consumption. Lubricants are critical for maintaining machinery efficiency, but their demand contributes to environmental degradation. To address this, decarbonization strategies are essential. This paper investigates the application of electrostatic lube cleaning technology as a pivotal solution for enhancing mining machinery efficiency and promoting sustainability. This innovative technology extends oil lifespan and reliability, reducing oil replacement frequency and new oil purchases. By effectively removing contaminants through electrostatic separation, it surpasses traditional filtration methods, ensuring optimal lubricant performance even in the harsh mining environment. This approach aligns with the principles of the Circular Economy, focusing on oil reclamation and minimizing waste. We explore the benefits of on-site electrostatic lube cleaning, including improved machine reliability, reduced operational costs, and significant environmental impact reduction. Furthermore, this study contextualizes these advancements within the frameworks of cradle-to-gate, cradle-to-grave, and Nano, Micro, and Macro-Circularity, providing a comprehensive understanding of sustainable lubricant management in the mining sector.

The Indian coal mining industry grapples with significant challenges in optimizing productivity, particularly in overburden removal to access coal deposits. Difficult mining conditions, a shortage of skilled operators, reluctance to adopt new technologies, and stringent regulatory norms have hindered advancement in this sector. Compared to more digitally advanced mining practices in countries like Australia, Canada, and the USA, India's adoption of such technologies has lagged, resulting in missed opportunities for productivity enhancement. In response, Tata Steel has taken a pioneering role by investing in digital infrastructure within its Raw Material Division. This includes initiatives such as sensor installation, CCTV connectivity, fleet management systems, e-permit systems, contract labor management, and connected machines and workforce. These efforts have culminated in the establishment of the Integrated Raw Material Supervision Center (iRMSC) at Tata Steel Jamshedpur, the first of its kind in India. The iRMSC facilitates remote monitoring of assets, real-time data analysis, and the development of models to enhance equipment health and productivity, while promoting collaboration across various Raw Material locations and central functions. By analyzing key performance indicators (KPIs) and implementing AI-driven features, the center provides continuous feedback to mine operators, significantly improving productivity and operational efficiency. Future initiatives include the introduction of an AI-based decision support system designed to identify and mitigate productivity losses swiftly. These efforts exemplify Tata Steel's commitment to harnessing digital technologies and innovations in the mining sector, with the potential to markedly transform the Indian coal mining landscape.

Global mining practices are evolving rapidly, prioritizing efficiency and sustainability through innovative technologies. This study focuses on integration of Unmanned Aerial Vehicle (UAV) technologies with advanced geospatial technologies to optimize geological resource modelling and mine profile analysis at Tata Steel's mines in India. Utilizing photogrammetric drones (RGB Imagery) and structure from Motion techniques, high resolution 3D Point Cloud Data is generated for detailed terrain analysis using Agisoft Metashape software. This data is further processed to create high resolution Digital Surface Model and Orthomosaic. The study analysed multi-temporal datasets collected over four months to support advanced geological modelling and Mine Monitoring. The quality check using Ground Control Points from DGPS survey yielded a Root Mean Square Error of 1.24 cm across X, Y and Z planes. Filtered Point Cloud Data is used to generate high resolution Digital Terrain Models and supports advanced post–processing for precise multi-temporal volumetric analysis. The Reduced Filtered Point Cloud Data is integrated into Maptek software for 3D modelling and spatial analysis. This study highlights the efficacy of UAV survey as a cost effective, precise, and efficient tool for geological applications in the mining sector, offering enhanced precision in updating the resource model and mine profile analysis.

Tunnelling projects across the world are growing rapidly, particularly in India growing at significant rate with 75 projects worth Rs. 49,000 Cr under construction and major new tunnelling projects worth Rs 1.1 lac Cr to be started soon. This makes essential to address the accidents and other incidents occurring in tunnels. In this paper, analysis of multiple case studies is taken into account to find out the causes of tunnel accidents, and its after-effects especially on the long-term stability of the tunnels after counter measures are taken to restabilize such tunnels. The focus of paper is to identify the contributing factors and suggest preventive/safety measures to control and respond to these incidents. Many accidents have been recorded in tunnels in the recent past. For instance, recently, a section of Silkyara-Barkot tunnel located in state of Uttarakhand, India collapsed trapping of 41 workers. Analysis of various tunnel accident-cases reveals that maximum accidents are attributed to negligence, may be in terms of insufficient geological investigation along the tunnel alignment, faulty deformation monitoring and late action for countermeasures etc. By understanding these causes and addressal can help in reducing the risk of accidents and improve long-term stability and safety of the tunnels. The primary aim of this study is to learn lessons from the fatal accidents and suggest some guidelines/measures to be taken in order to minimize/avoid such tunnel accidents in future.

Open-pit mining accounts for nearly 90% of the total available mines in India for various minerals. The depth of the open-pit mine in India ranges from few metres to as deep as 400 m. As the depth of open-pit mine increases, slope stability becomes a significant concern due to time-dependent deformations that heighten the risk of failure if slopes are inadequately designed. Slope failure and land subsidence can adversely affect the safety of men & machineries including environment and economics of the mine. An effective slope monitoring system is necessary to manage the geotechnical risks associated with open-pit mining that produces critical information and facilitates in understanding the slope behaviour with time. The slope monitoring practice helps mine operators in managing and mitigating the risks of a slope failure by forecasting the event of failure before it occurs. For better identification of potential slope hazards, it is better to have some early warnings of an impending slope failure by setting out alarming and threshold limits for the moving slope. This study examines the current slope monitoring methods and techniques used in open-pit mines across India.

Gas and dust explosions present serious hazards to underground coal mine workers. During mining operations, methane (CH₄) gas is released and can accumulate in areas with inadequate ventilation. Coal dust deposits can become airborne, creating explosive dust clouds. The ignition of CH₄ or the detonation of explosives can disperse coal dust into the air, triggering powerful secondary explosions. Similarly, gases released from oil fields are highly explosive. Electrical and electronic equipment used in coal mines or oil fields must adhere to explosion-proof requirements. Lithium-ion batteries are gaining popularity due to their high energy density and fast-charging capabilities. However, these batteries are generally unsuitable for direct use in hazardous areas. To be used safely in such environments, Li-ion batteries must comply with the design requirements of relevant safety standards. Batteries used in hazardous locations must meet the general requirements of IS/IEC 60079–0, along with specific safety standards such as IS/IEC 60079–1 for flameproof enclosures or IS/IEC 60079–11 for intrinsic safety, depending on the type of protection required. This paper examines the IEC standard requirements and improves the reliability of Li-ion batteries for safe usage in hazardous areas of underground coal mines. Several analytical and simulation have been conducted to assess the safety levels of Li-ion batteries with different loads evaluating their suitability for applications in hazardous environments.

Open cast mining operations require continuous pumping to manage groundwater inflow, making reliable power supply essential for both dewatering and operational safety. This study proposes a Hybrid Bifacial Solar PV Grid-Interconnected Pumping System designed to reduce carbon emissions and ensure uninterrupted power. Data from various pumps installed in an open cast mine totaling a load of approximately 5.81 MW was analyzed. Assuming 24/7 operation, the total energy demand is estimated at 139,454 kWh/day. A bifacial solar PV plant rated at 20.6 MWp is proposed to meet 65% of this demand, leveraging 6.5 effective solar hours/day, 12% bifacial gain, and 80% system efficiency. The remaining 35% is supplied by the grid to ensure night-time and low-irradiance reliability. An energy management system (EMS) and battery storage of 2–4 MWh are also recommended. Performance was simulated using both SAM and PVsyst tools, with annual energy outputs and performance ratios compared to ensure accurate modeling. This hybrid model offers an economically and environmentally sustainable solution, significantly reducing carbon emissions in the mining sector.

Hyperspectral imaging has been demonstrated to possess a high discrimination performance and is currently employed in a multitude of applications in the field of resource development. However, the data structure is intricate, and machine learning is indispensable for data processing and analysis. In this study, we applied 10 machine learning models (CNN, Tree, Discriminant, Logistic Regression, Naïve Bayes, Support Vector Machine, KNN, Ensemble, (shallow) Neural Network, and Kernel Approximation) to four major hyperspectral datasets commonly used as benchmarks (Indian Pines, Salinas, Pavia University, and Botswana) to compare their classification performance. The results indicated that the Support Vector Machine (SVM) demonstrated the highest performance on the Indian Pines dataset, whereas the Neural Network exhibited the best performance across the other datasets. In contrast, CNN showed moderate performance on these datasets. These findings suggest the necessity of selecting appropriate machine-learning models based on the specific characteristics of the datasets. In general, newer machine learning models such as CNNs tend to have higher computational costs and pose challenges in terms of interpretability. This study demonstrated that classical machine learning models can, in some cases, outperform state-of-the-art models, highlighting the potential of traditional approaches in achieving superior performance under certain conditions.

An underground mining environment necessitates the implementation of an advanced system that integrates Internet of Things sensors, wireless communication networks, and artificial intelligence to continuously monitor and predict hazardous conditions in real-time. There are four primary communication technologies applicable in underground mining working environments: leaky feeder communication system, Wi-Fi communication system, optical fiber cable (OFC)-based communication system, and digital fusion solution that integrates leaky feeder, Wi-Fi, and OFC systems. The leaky feeder system is known for its reliability and minimal signal loss in underground settings; however, it is limited by its bandwidth capacity. In contrast, the Wi-Fi and OFC systems offer greater bandwidth, facilitating the integration of many sensors, CCTV, and communication devices, but they are susceptible to signal loss and damage to the OFC due to the harsh underground conditions. To mitigate the shortcomings of each technology and harness the benefits of individual technology, the implementation of digital fusion technology is beneficial. The digital fusion technology combines the leaky feeder system with Wi-Fi and OFC system, aiming to create a reliable, fail-safe, high-speed, efficient, and multifunctional communication network that improves communication, monitoring, supervision, safety, and productivity. The paper enumerates digital fusion technology that ensures dependable communication in underground mines.

Evaluating mining works tenders is a critical yet labor-intensive process that requires assessing bids’ technical, financial, and regulatory compliance aspects. Traditional methods often involve manual scrutiny, which is time-consuming, prone to human bias, and inefficient when dealing with large volumes of tenders. This paper proposes an AI-driven automated system for tender evaluation, leveraging Natural Language Processing (NLP) and Machine Learning (ML) to enhance accuracy, fairness, and efficiency. The system is designed to extract and analyze relevant information from tender documents, handling diverse formats using NLP-based text parsing. Machine learning algorithms evaluate bids against predefined criteria, ensuring objective assessment while reducing manual intervention. The system also facilitates processing many tenders simultaneously, allowing organizations to optimize procurement workflows. Furthermore, the proposed solution is designed to seamlessly integrate with existing enterprise systems, ensuring ease of use for employees and stakeholders. By automating the tender evaluation process, the system minimizes human errors, accelerates decision-making, and enhances transparency in bid selection.

Rock properties are vital for applications in in mining, geotechnical engineering and various rock engineering projects. The P-wave velcoity is a key indicator for evaluating the integrity and stability of rock formations, critical for tasks like tunnel excavations, slope stabilization, and other mining operations. It also serves as an essential parameter in designing foundations for structures such as dams, bridges, and other rock-based constructions. The accurate determination of P-wave velocity depends on obtaining high-quality cored rock samples, but challenges like sample preparation, costs and time limitations have driven increased use of computational approaches for estimation. Earlier studies often relied on laboratory-based experiments and indirect techniques to estimate rock properties, including P-wave velocity. In the present study, a novel method is proposed to predict the P-wave velocity (Vp) of igneous rocks, specifically granite, by utilising the ball mill grinding characteristics which is an unconventional and reliable approach. A hybrid random forest model, enhanced through optimization with the dolphin swarm algorithm, is developed to estimate Vp based on grinding characteristics. The effectiveness of the model is evaluated in both training and testing stages using metrics such as the coefficient of determination (R2), Root Mean-Squared Error (RMSE), and Variance Accounted for (VAF), yielding values of 0.982, 168.09 m/s, and 98.22% for training, and 0.976, 138.83 m/s, and 97.7% for testing, respectively. This technique provides an efficient alternative to traditional P-wave velocity determination, eliminating the need for intricate sample preparation and expensive ultrasonic testing equipment.

An advanced spatial statistical framework employing AI approach has been built to model the grade distributions of iron ore deposits in West Singhbhum region of eastern India. The framework treats borehole data as marked point processes, using a Euclidean norm dissimilarity measure to capture spatial proximity. The inherent nonlinearity in the spatial distribution is modeled through deep kernel learning AI methods. To enable effective error reduction in machine learning estimates, features are designed to capture anisotropic spatial variability. As a result, the proposed approach provides a generalized AI framework for nonlinear and anisotropic data, supporting robust grade-tonnage estimation. The model has been applied to iron ore data collected from five different deposits in the West Singhbhum region, covering both contiguous and non-contiguous areas with significant orientation and anisotropy. The results are then compared with simulated data generated by bootstrapping the original dataset after removing spatial anisotropy. This approach offers a robust tool for resource assessment and decision-making in iron ore exploration in the West Singhbhum region.

Mineral prospectivity modelling (MPM) is essential in identifying areas with high potential for mineralization in data-scarce and geologically complex regions. Traditional rule-based and statistical approaches often require strong assumptions, manual weighting, and struggle with non-linear interactions among predictors. Here, we conduct a systematic comparison between classification and regression paradigms on a Canadian magmatic Ni (±Cu ± Co ± PGE) sulphide dataset. We apply domain-aware preprocessing—morphological dilation and Gaussian smoothing—to enrich sparse occurrence labels with spatial context. Our classification pipeline uses a Random Forest classifier with bootstrapped balanced sampling to mitigate extreme class imbalance, while our regression pipeline employs Ridge and Lasso to predict continuous prospectivity scores. We evaluate classification via precision–recall analysis and confusion matrices, and regression via cross-validated R2. Results show that regression models produce smoother, geologically coherent prospectivity surfaces, whereas classification, despite high specificity, suffers low minority recall. We discuss metric selection under imbalance, the impact of domain-aware labeling, and implications for exploration targeting, advocating for regression-driven MPM workflows.

Coal quality parameters, such as carbon content and ash yield, significantly influence the economic and metallurgical value of coal. Accurate determination of these parameters is crucial for optimizing industrial processes as they affect energy efficiency, processing costs, and pollution emissions. The standard ultimate and proximate analyses are time-consuming and resource-intensive. To offer an efficient alternative to the exhaustive standard methods, the present study attempted the use of mid-infrared Fourier Transform Infrared spectroscopy (FTIR) combined with advanced machine learning models to quickly and accurately predict coal carbon content and ash yield. Eighteen coal samples from the Johilla coalfield in Madhya Pradesh, India, was analyzed using FTIR spectroscopy across the range of 4000 to 350 cm−1. Machine learning algorithms like partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and a multimodel estimation (MME) approach utilizing the average of the three models were employed to predict carbon content and ash yield. The MME model delivered enhanced precision and robustness compared to the individual models, with an R2 of 0.962, RMSE (%) of 22.812, and MBE (%) of 4.042 for carbon content and an R2 of 0.841, RMSE (%) of 37.081, and MBE (%) of 8.039 for ash content.

Objective in any beneficiation process is to reduce contaminants from the input. This results in a product that is better than the input leading to multiple benefits both in usage and transportation. It is also pertinent to have a technology that does not involve huge diversion of community resources, like water and leaves a residue that is harmful to the community and has negative impact on the environment where the process is located. Coal beneficiation is a critical process in the coal industry to improve fuel quality and efficiency. Traditional wet methods are costly and environmentally-challenging due to water consumption and effluent generation. This paper explores a low-cost dry coal technology that utilizes principles of physics and specifically-designed air blower and nozzles to precisely separate coal from impurities. The proposed method drastically reduces capital costs, has much lower operational costs, minimizes environmental impact, and enhances coal quality without involving the use of water. The disruptive nature of this technology challenges prevailing myths regarding the limitations of dry beneficiation, particularly for high ash coal, and paves the way for more efficient and eco-friendly coal processing solutions.

The increasing generation of ash from coal-fired thermal power plants necessitates sustainable disposal strategies, particularly in mining regions where space constraints pose challenges. This study has evaluated the feasibility of integrating pond ash with overburden dumps in a large opencast coal mining project. A comprehensive assessment was carried out, incorporating field studies, route and traffic analyses, time-motion studies, and equipment performance evaluations. Key challenges identified include increased traffic congestion from ash-hauling operations, inefficiencies in mixing and dozing, and geotechnical stability concerns, particularly under wet conditions. The study also explores transportation alternatives, highlighting the cost-effectiveness of rail transport, though constrained by infrastructure, and the flexibility of road transport, albeit with congestion and safety risks. Seasonal variations, especially rainfall, were found to exacerbate dump stability concerns and runoff contamination. A detailed analysis of effect on production due to mixing of ash with overburden for an active coal mine is also presented in this paper. The study underscores the importance of strategic planning to accommodate ash with overburden mining operations.

This paper deals with the extensive laboratory studies and field trial carried out at one of the running opencast mines to evaluate the feasibility of disposing pond ash(PA) as a backfill material along with overburden (OB) in an environment friendly manner. Physico-mechanical, elemental composition, and geo-technical properties of PA, OB and its admixture was determined in laboratory. It was observed that mixing PA with OB alters its size gradation, density, angle of repose, permeability, compressibility, compaction etc. Stability analysis for different section of the final stage internal dumps revealed that a FoS of more than the statutory value of 1.5 was obtained in most of the sections. Field studies on a pilot scale PA-OB mix dump model of 15 m height to observe its behavior during pre and post monsoon period was observed. It was found out the pilot dump did not encounter much vertical movement at the top of the pilot dump, an average movement of 700 mm was observed at the toe and crest of the adjacent dump under the influence of one monsoon. The finding of this study will enlighten the practicing mining engineers about the pros and cons of dumping PA with OB in an opencast mineout.

Indian coals are reported for high mineral content but low sulfur content from Gondwana origin. Intimately mixed solid, liquid, and gaseous phases with allothigenic and authigenic origins make up coal, a sedimentary rock, which is a complex mixture of organic and inorganic materials. India's vast coal reserves contain significant amounts of ash-forming elements, the majority of issues arising during the use of coal are due to, elements in coal and seen as a nuisance creator frequently. Kaolinite, quartz, pyrite, clays, and carbonates are groups of minerals that are abundantly observed in coal. Which present both challenges and opportunities for sustainable utilization. This study explores the application of multiple thermo-processes to investigate the recovery potential of these ash-forming elements from Indian coal at a specific gravity cut of 1.80 g/cc. Through controlled thermal treatments, including combustion, gasification, and pyrolysis, we analyzed the transformation behavior of mineral matter and its potential for value- added applications. Advanced characterization techniques such as XRD and ICP-OES techniques are employed to assess the redistribution and recovery efficiency of critical elements. The study further emphasizes a zero-waste approach by exploring the utilization of recovered minerals in construction materials, metal extraction, and environmental applications. The findings provide insights into optimizing thermal processes for efficient resource recovery, contributing to sustainable coal utilization and circular economy practices.

Gas-hydrates, crystalline form of methane and water, have attracted the geoscientific community due to their natural occurrences, plausible impact on marine environment, and most importantly potential as future major energy resources. The bathymetry, seafloor temperature, total organic carbon, sediment thickness, rate of sedimentation, geothermal gradient suggest that shallow sediments along the Indian margin can host gas-hydrates. In fact, the geoscientific investigation, mainly seismic experiments, reveal signatures of gas-hydrates primarily in the Krishna-Godavari (KG) and Mahanadi basins, and Andaman regions. The methane within hydrates has been prognosticated to be more than 1500 times of country’s natural gas reserve. Extraction of these can make India energy self-contained and sustainable for many centuries. Success in test productions in Canada, USA, Japan and China shows a great hope for production of gas-hydrates. Therefore, delineation of most prospective zones, characterisation and assessment of resource potential, estimation of critical parameters, comprehending genesis and mode of occurrences, laboratory studies on the dissociation kinetics, simulation for developing the best possible extraction method, and environment impact assessment are very crucial in augmenting viable production of gas-hydrates. Here we produce the research and development of gas-hydrates in India along with their global status with regard to their exploration and exploitation.

Coal Bed Methane (CBM) represents a critical transitional energy resource for India, offering a pathway to reduce reliance on coal, enhance energy security, and mitigate methane emissions-a potent greenhouse gas. This study conducts a comprehensive comparative analysis of India’s CBM advancements against global leaders (China, the U.S., and Australia), evaluating technological innovation, policy frameworks, infrastructure readiness and environmental sustainability. With 92 trillion cubic feet (TCF) of estimated reserves concentrated in Gondwana basins, India’s CBM potential remains underexploited, producing only 2.8 million metric standard cubic meters per day (MMSCMD) as of 2024. In contrast, the U.S. and Australia produce 48.2 MMSCMD and 30.1 MMSCMD, respectively, driven by mature technologies, streamlined policies, and robust infrastructure. Using a mixed-methods approach, this study integrates quantitative analysis of production data (2010–2024) & qualitative policy reviews to identify systemic gaps. Key findings reveal that India lags in horizontal drilling efficiency (30% lower recovery than the U.S.), digital monitoring adoption (IoT usage in 15% of wells vs. 85% in Australia), and methane leakage mitigation (7.9% of national emissions, double the global average). Regulatory fragmentation, particularly overlapping coal mining and CBM licenses, delays projects by 18–24 months, while inadequate pipeline connectivity restricts commercialization. The study highlights replicable strategies from global benchmarks like China’s AI-driven drilling optimizes extraction in deep seams (>1,500 m), achieving 12 MMSCMD production. Australia’s CBM-LNG integration at Gladstone Terminal generates $45 billion annually, demonstrating export scalability. U.S. tax incentives under Sect. 45 boost private investment, revitalizing aging basins. This study recommends adopting advanced technologies like AI-driven reservoir modelling and modular LNG plants to boost recovery and export capacity, although regulatory harmonization between coal mining and CBM extraction has reduced project delays by 30% under India’s 2024 Unified Licensing Policy. Additionally, it calls for expanding the National Gas Grid to connect remote basins and prioritizing small-scale LNG facilities. Projections suggest these measures could elevate India’s CBM production to 8.5 MMSCMD by 2030, fulfilling 15% of gas demand and reducing methane emissions by 30%. The study underscores the urgency of aligning CBM development with India’s net-zero targets, advocating for public–private partnerships and international collaboration in carbon capture and storage (CCUS) technologies.

Coal bed methane is one of the unconventional natural gas sources that is now being explored as a potential addition to India’s energy supply. The International Energy Agency predicts that India’s demand for natural gas will increase by 60% by 2030. One of the most attractive and feasible locations for coal bed methane development is the Jharia coal field in India, where the coal seams have been classified as degree II and III gassy seams for decades. For a century, Tata Steel Limited has been mining low moisture, low to medium volatile bituminous coking coal for its steel plant’s captive use, from the Jamadoba Group of collieries located at the Jharia coalfield. The exploratory data shows that the in-situ gas content of the coal seams at shallow depths between 0 and 300 m and at greater depths between 1015–1100 m has a low gas content average of 1.87m3/ton and 1.84 m3/ton, whereas in between depth 300 m to 1015 m, the gas content has increased. The in-situ gas content is low, average 1.72 m3/ton for some of the coal seams which were fully affected by igneous intrusion that results in conversion into natural coke; the shaly coal has an average gas content of 3.86 m3/ton, followed by coal with average gas content of 4.48 m3/ton and partially heat-affected coal (transitional zone between natural coke and coal) with average. gas content of 7.38 m3/ton. The composition of the coal gas is analyzed, and the concentration of methane is greater than 94% with other alkanes as measured in different coal seams. The maceral vitrinite has the higher methane adsorption capacity; it has been analyzed that the vitrinite content for the upper and middle coal seams is greater than 40%, whereas for the lower coal seams, the vitrinite content is less than 40%. The coal seams are having vitrinite reflectance, Rr in-between 0.88% and 1.69%, which is within the window for effective methane gas generation. For the Jamadoba group of collieries at Jharia coal field, the findings are encouraging to undertake further exploration and meticulous planning for the development of the virgin coal seams for coal bed methane. Though drilling long-lasting, stable production wells through coal seam workings and exploiting the lower pack of virgin seams is a significant challenge.

Shrinkage and swelling in the matrix system of coal, lignite and shale beds are key phenomena during CO2 injection for enhanced methane recovery. Shrinkage improves permeability, while swelling reduces it, posing challenges for carbon capture, utilization and storage (CCUS), such as inefficient injection, reduced storage capacity and compromised reservoir integrity. Understanding these rock behaviours is critical to optimizing carbon storage and hydrocarbon recovery. Shrinkage and swelling are influenced by factors such as moisture content, pore distribution, mineral composition, organic maturity, temperature and pressure. In coalbed methane (CBM) and shale reservoirs, methane extraction reduces gas pressure, causing matrix shrinkage and enhancing gas flow. Conversely, CO2 injection leads to swelling due to its strong affinity for internal pore surfaces, reducing permeability and altering pore structure and fracture connectivity. To manage these effects, CO2 is often injected with N2 in tailored ratios. Laboratory-based sorption-induced strain measurements help determine optimal gas mixtures. A linear relationship exists between CO2 adsorption and strain greater adsorption leads to more swelling. Rock type, carbon content and thermal maturity also influence swelling behaviour, which is further affected by temperature and pressure. This review highlights key parameters of CO2 sorption and swelling, offering guidance to optimize gas production and reservoir performance.

Cleats and fractures in coal significantly impact mine planning, beneficiation, stress field evaluation, structural orientation, physico-mechanical strength, permeability and porosity of coalbed methane (CBM) reservoirs. Therefore, a detailed micro-level understanding of cleat-fracture trends and intensity is essential for both subsurface and surface coal handling. Cleats are categorized as face, butt, master, or tertiary based on lithotype. Measurements typically include spacing, orientation and aperture. Higher cleat frequency often correlates with greater coal seam deformation, increasing brittleness and requiring special mining precautions. Rock Quality Designation (RQD) and Rock Mass Rating (RMR) are assessed based on cleat-fracture intensity. In-situ stress analysis is crucial prior to mining to ensure mine stability and longevity. Highly cleated coals pose gas outburst risks and affect underground roof support systems, such pillars are generally avoided to prevent roof falls. Cleats also facilitate gas migration, making permeability studies vital for CBM extraction. Various stimulation techniques, thermal, mechanical, and chemical are used to enhance cleat apertures and induce fractures. In beneficiation, well-cleated coal is more friable, reducing milling power and overall processing costs. This paper highlights the importance of comprehensive cleat characterization to optimize coal mine safety, productivity and resource utilization.

Underground Coal Gasification (UCG) has immense potential for utilization of deep unmineable coals. Simulation of this complex process offers ease in decision-making with significant cost reduction required for commercialization of the technology. The paper demonstrates the prediction of syngas composition and its calorific value (CV) for certain coal by varying inlet O2 concentration $$\left( {C_{{O_{2} }} } \right)$$ C O 2 and reactor temperature (RT) during UCG applying numerical simulation technique. The gasifier is considered as a semi-batch reactor with adiabatic conditions with an initial system volume of 1 m3. The homogeneous and heterogeneous phase reactions were initialized in zero-dimension, considering pyrolysis was completed before the initiation of the process. The RT was fixed at 1200°K and three scenarios were simulated for low, equivalent and high $$C_{{O_{2} }}$$ C O 2 with respect to steam concentrations $$\left( {C_{{H_{2} O}} } \right)$$ C H 2 O . In each scenario, the ratio of inlet CO (CCO) to H2 concentration $$\left( {C_{{H_{2} }} } \right)$$ C H 2 was varied <1, 1 and >1 to study evolution of syngas. In the best case scenario at 1200°K for the equivalent $$C_{{O_{2} }}$$ C O 2 and $$C_{{O_{2} }} \,{\text{and}}\,C_{{H_{2} O}}$$ C O 2 and C H 2 O with $${{C_{CO} } \mathord{\left/ {\vphantom {{C_{CO} } {C_{{H_{2} }} < 1}}} \right. \kern-0pt} {C_{{H_{2} }} < 1}}$$ C CO C CO C H 2 < 1 C H 2 < 1 and $${{C_{CO} } \mathord{\left/ {\vphantom {{C_{CO} } {C_{{H_{2} }} > 1}}} \right. \kern-0pt} {C_{{H_{2} }} > 1}}$$ C CO C CO C H 2 > 1 C H 2 > 1 , the syngas compositions obtained were comparable, i.e., CO (31.74 mol%), CO2 (24.87 mol%), CH4 (30.83 mol%), and H2 (12.53 mol%) with a CV of 16.10 MJ/Nm3. However, the UCG process at 1300°K yielded higher CV (16.77 MJ/Nm3) with increased CO and CH4 concentration due to CO2 gasification reaction.

Conversion of carbonaceous feedstock derived syngas into methanol is a lucrative option for the valorization of fossil resources in a cleaner and more efficient manner. India has the vast reserve of coal and it is considered important for the nation’s energy security. Coal remains the backbone of India’s energy sector, and has the one of the largest coal reserves. India’s “Methanol Economy” initiative by NITI Aayog, aims to mitigate reliance on imported crude oil vis-à-vis minimize greenhouse gas emissions by utilizing methanol. In this context, the present study investigates the integrated conversion of coal-derived syngas into methanol through a multi-step process comprising syngas generation via coal gasification, followed by gas cleaning, hydrogen enrichment via the water–gas shift (WGS) reaction, and selective CO2 removal by amine solvent based approach. The final simulated syngas composition utilized for catalytic conversion to methanol comprised of H2: 51%, CO: 21%, CH4: 2.5%, CO2: 4.5, and N2: 21% obtained after coal gasification, cleaning, H2 enrichment process by water gas shift (WGS) reaction and CO2 absorption. The catalysts employed for methanol synthesis were reduced in-situ using ultra-pure H2 to ensure high catalytic activity and selectivity. The reaction parameters for methanol synthesis have been carefully optimized, with the operating temperature maintained in the range of 200–240 °C and pressure between 40–60 bar. Under these conditions, the several catalytic reactions evaluated, and a maximum space–time yield (STY) of approximately 194.7 g L⁻1 catalyst h⁻1 was observed at 60 bar and 200 °C. These findings demonstrate the high potential of using coal-derived syngas for methanol production, contributing significantly to the effective utilization of domestic coal resources in alignment with national energy and environmental goals.

The present study aimed to identify significant injury risk factors in an aluminum smelting plant by examining both local workplace hazards, individual, and organizational factors. The study included 120 injured workers and 480 non-injured workers from an aluminum smelter in eastern India. Data on personnel factors, exposure to hazards, and organizational factors were collected. Chi-square tests and logistic regression were used for statistical analysis. Bivariate analysis revealed statistically significant associations between injury occurrence and all types of hazard exposures and organizational factors (p < 0.01), except personal factors such as age and education. In the multivariate analysis, among the various hazard exposures, machinery-related hazards demonstrated the highest adjusted odds ratio (adOR = 3.08), followed by hand tools-related hazards (adOR = 1.94) and manual handling hazards (adOR = 1.88). Workers aged 25–45 years had a significantly lower risk of injury than younger workers (adOR = 0.49). Among organizational factors, poor working conditions (adOR = 3.16) and an unsafe safety environment (adOR = 2.62) were found to be significantly associated with increased injury risk. Results suggest that safety interventions should be focused on targeted engineering controls addressing high-risk machine and hand tool hazards, and systemic improvements to working conditions and safety environment.

Background: Work-related musculoskeletal disorders (WRMSDs) are widespread among mining machine operators due to prolonged whole-body vibration (WBV) exposure and ergonomic risks. A reliable predictive model is essential to accurately estimate WRMSD prevalence and support preventive strategies. Methodology: This study evaluated the predictive performance of four machine learning models—Random Forest, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN)—for identifying WRMSD risk among shuttle car operators. Data were collected from 54 operators (mean ± SD: age = 40.38 ± 7.19 years; BMI = 24.98 ± 1.99 kg/m2; experience = 12.06 ± 3.14 years). Key input variables included age, BMI, experience, posture (REBA score), frequency-weighted RMS acceleration, and vibration dose value (VDV). WRMSDs prevalence and severity were assessed using the standardized Nordic Musculoskeletal Questionnaire. Results: Significant correlations were observed between WRMSD severity and age, experience, posture, and vibration exposure [A(8), VDV(8)]; BMI was not significantly associated. Among the models, ANN achieved the highest performance (AUC-ROC: 0.9688; F1-score: 0.9143; Recall: 1.0000). Conclusion: The ANN model demonstrated superior accuracy in predicting WRMSDs and holds strong potential for proactive risk assessment in mining environments. Further model refinement and validation across diverse settings are recommended for broader occupational health applications.

Background: Whole-body vibration (WBV) is a critical occupational hazard faced by operators in various heavy machinery industries. Along with heightened risk of musculoskeletal disorders published literature also indicates negative effects on digestive system, the genital/urinary system, and the female reproductive organs. Despite extensive research on WBV exposure, there's a significant gap in understanding its effects, particularly among females. This study addresses that gap by evaluating the vibration exposure and associated health risks among female dumper operators in Indian opencast mines. Methodology: A cross-sectional field study was conducted among 41 female dumper operators. WBV exposure was recorded using tri-axial seat pad accelerometers during routine operations. Data were analysed following ISO 2631–1:1997 standards, and health risk was evaluated based on the Health Guidance Caution Zone (HGCZ) framework. Results: The vibration A(8) values obtained ranged between 0.44 and 0.56 m/s2. The Fast Fourier transform (FFT) of the input seat pad acceleration revealed prominent peaks within the 3–8 Hz frequency range. These peaks correspond to the natural frequency of the knees, abdomen, chest, and spine when in a seated posture. The HGCZ showed that operators faced a moderate health risk in terms of their health implications. Conclusion: With the induction of women in these roles, it is also important to ensure their safety from occupational hazards, considering that the physiological characteristics of females differ widely from those of their male counterparts. Our work here offers insightful data that can guide the creation of gender-specific occupational health standards and interventions, promoting a safer and more inclusive working environment in the mining industry.

Traditional mining activities generate massive volumes of waste, contributing significantly to the degradation of the surrounding environment. To mitigate these environmental effects and achieve sustainable mining, green technology integration is critical for ensuring long-term environmental sustainability, operational efficiency, and economic performance in the mining industry, as well as responding to global environmental issues and regulatory pressures. Green technologies such as electric vehicles and renewable energy in mines have helped in lowering carbon emissions and energy consumption overall. Improved waste management has also helped make mining operations more sustainable. This study focuses on the different practices of green mining technologies, which significantly lower energy use and minimize ecological disturbances associated with traditional mining methods, and the challenges associated with their adoption. However, the implementation of these green technologies faces significant challenges. One such major challenge is the initial high financial investment, which makes the transition financially burdensome for the majority of traditional mining businesses. Successful incorporation of green mining technology not only prevents environmental and health risks but also promotes operational sustainability and economic feasibility in the mining sector, highlighting the importance of taking active steps to counter such transition issues.

For more than a century, the Jharia coalfield located in India has suffered from significant environmental deterioration, posing major health hazards to the surrounding communities. In order to properly monitor and evaluate environmental impacts, this study suggests an integrated strategy utilizing cutting-edge geospatial technologies, including geographic information systems (GIS) and remote sensing (RS). Multi-temporal satellite imagery reveals the alterations in vegetation cover, urban development, water bodies, and land surface temperature (LST) conditions across different time periods. The analysis focuses on land surface temperature (LST) and various spectral indices such as NDVI, NDWI, and NDBI to measure the degree of degradation in the region over the past ten years. The findings of these analyses indicate a notable increase in land surface temperature (LST max increased from 38.0 to 47.4 °C), but a considerable drop in vegetation health (NDVI max reduced from 0.57 to 0.32) along with surface water extent (NDWI declined from 0.21 to 0.01). Concurrently, built-up areas (NDBI) grew, a sign of ongoing environmental deterioration in the study area caused by coal fires and mining operations. The findings contribute to establishing a framework for the environment. Planning and management, providing actionable recommendations to alleviate the environmental and social consequences of coal fires in Jharia.

Background and Aims Hand-arm vibration (HAV) is a major occupational hazard for jackhammer operators, and it causes Hand-Arm Vibration Syndrome (HAVS). This study aimed to assess the impact of HAV exposure on nerve conduction velocity (NCV) in jackhammer operators working in underground metal mines in India. Methods A cross-sectional study was conducted involving 30 participants, comprising 15 jackhammer operators and 15 unexposed mine workers. The groups were matched for age, gender, body mass index (BMI), and work experience to control for potential confounding variables. HAV exposure levels were measured by using the guideline stipulated by ISO 5349-1 (2001), and Electromyography was used to measure the NCV of the median nerve. Results Jackhammer operators exposed to HAV had significantly lower NCV in the median nerve compared to the unexposed group (p < 0.05). A strong inverse relationship was observed between lifetime vibration dose (ln(LVD)) and NCV (R2 > 0.75), indicating greater nerve damage with higher exposure. Conclusions Prolonged exposure to HAV significantly reduces NCV, suggesting nerve damage and the risk of HAVS. Implementing effective vibration control measures and conducting regular health check-ups are essential to safeguard the neurological health of underground jackhammer operators.

Fatigue remains a critical challenge in the mining industry, with serious implications for both safety and productivity. According to study by Caterpillar Global Mining study that 65% of truck haulage accident happening in mining industry are due to operator fatigue (Caterpillar Global Mining, 2007. Fatigue and its contribution to haulage accidents in mining operations). This issue extends beyond occasional tiredness, representing a significant occupational risk that also leads to billions of rupees in lost productivity annually. To address this, Fatigue Management must be elevated to the level of a statutory requirement, like Safety Management Plans mandated by the Directorate General of Mines Safety (DGMS). Regularly reviewing and updating Fatigue Management Plans is essential to ensure the most effective risk management strategies are implemented, keeping pace with emerging trends and technologies. A proactive, preventative approach to fatigue is critical, moving beyond reactive measures to prevent incidents before they happen. Incorporating active Fatigue Monitoring Systems (FMS), which track operator alertness in real-time, can provide valuable data for preventing fatigue-related accidents. By embedding these systems into routine operations, the mining industry can better protect workers, enhance safety, and reduce productivity losses. Achieving optimal fatigue management is key to the industry’s goal of zero harm, ensuring a safer, more productive working environment for all.

The Indian coal mining sector is a prime mover of the national energy supply. This sector urgently needs strategically designed pathways towards net zero emissions due to increasing concern for climate change and greenhouse gas emissions (GHGs). A comprehensive approach integrating technological, operational, and policy-driven strategies is key to net-zero goals. This paper explores viable net-zero pathways for the Indian coal mining sector based on three strategic pillars: (1) decarbonization of mining operations, (2) mitigation of fugitive emissions and, (3) adoption of carbon capture and storage (CCS) technologies. Decarbonizing mining operations involves the electrification of mining equipment. Integrating renewable energy and adopting energy-efficient measures in coal mining operations could lead to reduced GHG emissions. Mitigating fugitive methane emissions from coal mining activities is also a key step for decarbonization. The fugitive methane emissions could effectively be reduced using advanced ventilation air methane (VAM) oxidation and coal mine methane (CMM) end-use technologies. This paper evaluates the applicability of these approaches in the Indian coal mining sector. Integrating CCUS technologies in the coal mining sector can reduce its carbon footprint. Deep virgin coal seams could be utilized to store large amounts of CO2 and to recover methane. A holistic approach to achieving net zero requires enabling policies, regulatory frameworks and financial incentives. Government intervention, carbon pricing mechanisms and industry collaboration are essential to drive investment in low-carbon technologies. This paper also highlights the importance of innovative technologies, policy support and cross-sectoral collaboration in achieving net-zero emissions in the coal mining sector. These strategies offer a practical road map for the Indian coal mining industry to align with national and global climate targets while ensuring energy security and economic development.

Critical minerals and rare earth elements are crucial for the development of renewable energy infrastructures, electric vehicles, and energy storage systems. With the advancement of energy transition in the digital world, there is an escalating demand for these minerals. The Indian government has launched several initiatives to promote the domestic production of critical minerals, including the National Critical Minerals Mission and the auctioning of exploration licensing for critical minerals. Khanij Bidesh India Ltd. (KABIL) identifies and acquires overseas mineral assets that are critical and strategic in nature. Recently, the government has identified several critical mineral deposits, including lithium in Jammu and Kashmir, and rare earth elements in Odisha and Andhra Pradesh. Economic extraction of critical minerals from secondary sources like e-waste, tailings, coal ash, and waste rocks can significantly reduce India’s import dependence and environmental concerns. Advanced recycling technologies can recover these valuable minerals, promoting a circular economy and supporting India’s green energy transition and net-zero emission goals. The Ministry of Mines, Geological Survey of India, Indian Bureau of Mines, and other institutions, including CSIR laboratories, play a crucial role in mapping, extraction, and recycling of critical minerals. Despite of several challenges in securing critical minerals in terms of import and environmental aspects, there are significant opportunities for India to strengthened regulatory framework and develop a robust critical mineral supply chain. By encouraging private sector participation in harnessing the domestic resources and recycling, India can reduce its reliance on imports and achieve its net-zero emission aspirations. This transition requires a coordinated effort between government institutions, private sectors, and research organizations to ensure a sustainable and responsible critical mineral supply chain in India.

As a critical and strategic metal, Cobalt (Co) is in increasing demand due to the global transition to renewable energy, playing a pivotal metal in driving this transition. The rising demand for EVs and production of wind power turbines has created an urgent need to explore new Co resources. In India, Belligutti hill, situated near Kalyadi village within the Kalyadi Schist belt (KSB) of the western Dharwar craton, Karnataka, is well known for significant copper production in the past and previous studies have reported the presence of significant Co. The economic potential of Co in the KSB was investigated through detailed petrographic study, EPMA, Raman spectroscopy and ICP-MS using the samples collected from Kalyadi mine dumps and tailings. Petrography studies revealed the presence of sulfide bearing phases, predominantly pyrite and chalcopyrite, which occurs along the foliation and also associated with the quartz-carbonate veins. The EPMA results reveals two different occurrences of Co i.e., independent Co mineral and isomorphous substitution of Co in pyrite. An independent Co mineral is identified to be siegenite based on its EPMA analysis and characteristics spectral peak in Raman spectroscopy. ICP-MS analysis of separated sulfide concentrates from mine tailings revealed a significant cobalt (Co) content of up to 1.12 wt%. These results indicate significant Co potential in the KSB.

Lithium (Li) is a critical metal in India due to its growing demand and limited global production. Its importance in achieving global sustainability goals, particularly net-zero emissions, has caused increased exploration efforts. The Amareshwar region in the Parampur schist belt of the Dharwar Craton, Southern India, is a promising site for Li exploration. The region hosts pegmatite bodies intruded at the boundary of amphibolites and granitoids, that contains spodumene (LiAlSi3O8). These pegmatites are composite, zoned, and primarily composed of quartz, plagioclase, k-feldspar, spodumene, and muscovite. Satellite surveys and existing data were used to identify potential spodumene-bearing areas. Band-ratio and RGB composite techniques on ASTER data were employed to delineate these regions. Integration of these maps with lithological, structural, and geochemical data from the National Geoscience Data Repository (NGDR) improved the accuracy of spodumene identification. Validation with Geological Survey of India (GSI) maps, which also marked Li-pegmatites, showed strong alignment, confirming the effectiveness of the generated maps. These findings proved invaluable for fieldwork where the presence of the spodumene bearing pegmatite bodies were identified. The accuracy of the pre-field maps was thus tested and it proved helpful in exploration targeting and can guide future exploration in the region.

The rising demand for lithium-ion batteries has led to the investigation of unconventional lithium sources. Indian coal, with its extensive reserves and diverse lithological features, presents a valuable opportunity for lithium extraction. Given the limited availability of lithium resources, this study focused on assessing lithium content in Indian coals and their combustion by-products. Coal samples from various regions across the country were analyzed, revealing a mean lithium concentration ranging from 6.72 mg/kg in BCCL to 17.32 mg/kg in SECL, indicating that SECL coal has the highest average lithium content. The maximum concentrations observed varied, with SECL coal showing up to 38.45 mg/kg. The overall average lithium content in the analyzed coal samples, based on 176 samples, was 10.9 mg/kg, which is below the global average of 14 mg/kg. High lithium content samples underwent sequential extraction, revealing that most lithium is found in the insoluble aluminum silicate matrix of coal ash. Additionally, analyses of fly ash, bottom ash, and mill rejects indicated higher lithium levels, particularly in fly ash. Despite the generally low concentrations of lithium in the samples, localized enrichment may occur, underscoring the need for ongoing monitoring and systematic sampling to uncover potential lithium resources.

Critical elements are necessary for the transition to clean energy technologies. As the demand of the critical elements is increasing and limited availability of the critical elements due to geopolitical issues and low ore concentration, the world is eyeing for the secondary sources for the extraction of critical elements. However, coal fly ash is a promising alternative for the secondary sources, but it depends on the coal’s deposition environment and geochemical factors. So, geochemical studies for the critical elements are necessary to target which coal mine will suit best for the extraction of critical elements. This study investigates the distribution of trace and critical elements in the Dudhichua coal mining area from Singrauli coalfield by analyzing coal samples from five major blocks. The geochemical analysis, performed using X-ray fluorescence (XRF) and Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), quantifies major oxides alongside trace and critical elements. Total REY content was higher for NCL-II (150 ppm), followed by NCL-I (143 ppm), NCL-III (123 ppm), NCL-V (114 ppm) and NCL-IV (96 ppm). Among the NCL area, four coal sample from NCL-I, NCL-III, NCL-IV and NCL-V had outlook coefficient (Cout) more than 0.7. However, NCL-II samples had REY content more than 100 ppm but Cout was < 0.7. However, beneficiation studies could be done to increase its concentration and can be considered as a promising option. During fractionation in powerplant REEs generally tends to enrich in the coal fly ash which can make them economical for extraction. Most of the REEs are negatively correlated with the Fe2O3 and positively correlated with Al2O3 and SiO2 content in coal indicating its association with the clay minerals like kaolinite in coal. This shows the potential of Singrauli coals as a source of critical elements offering opportunities for resource recovery and diversification of the supply chain. This study highlights the significance of Singrauli coalfields in India as viable repositories for critical elements which could contribute to the nation's clean energy transition and global sustainability goals.

Coal combustion produces by-products such as fly ash and pond ash, which contain valuable trace elements and rare earth elements (REYs), including Ga, Sc, La, Ce, and Nd. This study reveals the partitioning of these elements in the combustion by-products from NTPC Korba power plant, which uses coal from Gevra mines in Chhattisgarh, India. Results indicate that the ash primarily consists of SiO2, Al2O3, and Fe2O3, meeting Indian standards for use as a pozzolana in cement and concrete. In pond ash, Ba, Sr, and Cr showed the highest trace element concentrations, while La, Sm, Ce, Nd, and Y were the most abundant REYS, totalling 211–383 mg/kg. Fly ash had higher REY concentrations, ranging from 267–401 mg/kg, with La and Ce being the most prevalent. Ga and Sc concentrations notably increased across ESP hoppers, from 32.2 to 147 mg/kg for Ga and 16.7 to 24.2 mg/kg for Sc. This study accentuates coal ash as a promising secondary source for critical elements, emphasizing the potential for efficient recovery and recycling of Ga, Sc, and REYS, which could lower production costs and reduce environmental impact.

The paper presents the studies carried out for integration of 02 underground mines, viz. Parasea and Belbaid Collieries as an amalgamated project rechristened as Parasea-Belbaid Project. Both the coal mines together were producing 550 TPD (tonnes per day) of coal and the PRC (Peak Rated Capacity) of the integrated project is 6000 TPD. The ventilation system of the mine has been designed by following a 02-stage process, viz. field studies in underground and ventilation network modelling studies. The Parasea Colliery has serious fire problem in many panels close to main intake and return airways of the mine and similarly in Belbaid, there are only 02 routes existing between intake and return of the mine in most of the depillared panels. Keeping these problems in view, new infrastructures such as 02 inclines and 01 ventilation shaft have been planned in the virgin side of the property. These new infrastructures have been integrated with old workings and earlier openings in such a way that these co-exist with each other and fire in the old workings do not disturb the presently developed workings and production area of the mine. The authors also present the details of implementation of recommendations for development of this amalgamated project.

In India, more importance has been given to the assessment of gas, dust, and noise exposure during the operational process of coal mines. However, less priority is given to the thermal comfort of the miners. In India, the sub-clause 2 (d) of Coal Mines Regulations (CMR-2017) no. 153 stipulates that the wet-bulb temperature (WBT) in any underground coal mine working place should not exceed 33.5 °C, and where the WBT exceeds 30.5 °C, an arrangement is to be made to ventilate the working place with an air current moving at a speed of not less than 1 m/s. Though the Coal Mines Regulations (CMR-2017) no. 169(2) speaks about the measurement of temperature, humidity and such other environmental conditions once at least in every 30 days, it does not limit the relative humidity percentage in underground coal mines. Humidity plays a vital role in human thermal comfort.

The focus on coal production from underground mines is steadily increasing due to the rapid depletion of easily accessible surface deposits and steady increase in the demand of coal. Ventilating deep, extensive, and highly mechanized underground coal mines is currently a critical area of concern. The underground mining environment is dynamic which changes with slight variations in in-situ geological condition, engineering parameters and other bio-chemical parameters. Therefore, mining operations require a flexible and responsive ventilation system to adapt to these dynamic conditions. Technologies such as Ventilation-on-Demand (VOD) enable real-time adjustments, optimizing energy consumption by reducing unnecessary ventilation during periods of lower activity or in specific areas where less airflow is needed. In underground mines where heat stress is prevalent and mine air refrigeration is required, VOD plays a crucial role in delivering cool air precisely on where and when need basis for efficiently managing both ventilation and refrigeration costs. This study presents an investigation on the application of VOD to reduce heat stress and decrease the operational costs associated with fan power consumption, thereby promoting sustainability. The findings suggest that deploying advanced VOD systems can enhance safety, efficiency and productivity by mitigating heat stress, while potentially cutting operational electricity costs in underground coal mines.

The design of a ventilation systems in deep underground coal mines is a critical endeavor to ensure the workplace safety and productivity of miners operating in extremely challenging conditions. This paper presents a comprehensive exploration of the different strategies involved in revamping the ventilation systems for two interconnected deep underground coal mines with multi-seam workings having liabilities of upper seam sealed-off fire areas and goaved out seams. Both the deep underground coal mines of Jharia coalfield, India are connected to each other having a common ventilation scheme. The efficient ventilation system design process involves a thorough ventilation survey of both the mines, an analysis of the mine layout, geological and technological factors, etc. Presently both the mines are in operation having overall fan efficiency of 42.8%. Accordingly, the redesign of the ventilation system has been carried out through computer simulation of the combined ventilation networks to operate a single fan to reduce energy cost. The result of the computer simulation reveals that one mechanical ventilator will provide the required air quantity to operate both the mines. The above study helps to save 45.19% of air power cost in ventilation for both the mines.

The Coal mine fire problem occurs globally and cause danger to miners’ life. It dominantly contributes towards adverse impact on environment as well as leads to economic setback to the nation. It also poses several societal influences in terms of health and life hazard on populace residing nearby mine fire areas including several other mining operational complications to the coal mine operators. Detection of magnitude and extent of fire is one of the most important steps to decide measures for its mitigation. Without the knowledge of its magnitude and extent, any preventing and control measure becomes useless. The present study was conducted to prognosticate the fire conditions, its magnitude/intensity and its extent and direction of propagationin over burden dumping and its surrounding mining area at Sarubera (East) Colliery, Kuju Area, Central Coalfield Limited. The whole identified fire locations covers an area around 500 × 500 m was divided into 10 × 10 grids/nodes and temperature of each grid were measured using infra-red thermal imaging camera. The ground coordinates of all monitoring locations are determined by surface survey plan of the mine and maximum temperature of each node was recorded. On the basis of study a 3-Dimensional thermal profile of the identified zone has been generated and based on the observations drawn by temporal surface thermal mapping state, extent of fire and its rate of propagation was determined.

Indian underground coal mining is adapting to the increasing demand for coal. The underground mines are utilising diesel-powered vehicles for coal production and transportation, resulting in the generation of diesel particulate matter (DPM) and noxious diesel exhaust gases. The accumulation of DPM in the underground workings poses significant health risks to workers due to limited ventilation and pollutant dispersion. This study presents the development and validation of a computational fluid dynamics (CFD)-based dilution model to predict DPM dispersion in blind headings, with the aim of optimising the ventilation system and mitigating occupational exposure. To measure DPM levels in real time while airflow is controlled, a pilot-scale experiment was set up in the mine fire model gallery at CSIR-CIMFR, Dhanbad. This setup, which resembles a scaled blind heading, was created. The CFD model incorporated Reynolds-averaged Navier–Stokes (RANS) equations with a k-ε turbulence closure and discrete phase modelling to simulate particle transport while considering air velocity and emission rates. The CFD model was validated against the experimental data generated from the mine fire model gallery experiments, where a DPM source was provided using a diesel-powered truck available in-house. The models were simulated using different air velocities, viz. 0.5, 1.0, 1.5 and 2.0 m/s for optimisation using both main and auxiliary ventilation systems. The results revealed that an air velocity of 1.5 and 2.0 m/s can bring the DPM concentration below 100 µg/m3 at a 20% higher rate in the mine gallery as compared to the air velocities of 0.5 and 1.0 m/s. Further, an average increase in the ventilation rates by 57% reduced peak DPM concentrations by 32%. This study highlights the potential of CFD modelling as a predictive tool for designing targeted ventilation systems in confined mining environments. Results advocate for adaptive airflow management to minimise DPM exposure and offer actionable insights for improving occupational health standards in the mining industry.

Mining operations generate significant amounts of airborne respirable dust and its prolonged exposure to workers may lead to several potential health risks. Hence, a study was conducted on different dust generating operations as well as different categories of workers, viz. Loaders, Drillers, LHD (Load, haul and dump) operators, Crusher operators, Conveyor belt operator, Sag Mill operator etc. in Hutti gold mines to assess the impact of dust and free silica exposure. Two rounds of field studies encompassing, totally 112 sampling points both personal and static dust sampling were carried out to determine dust concentration and free silica level in various workplaces. Results of the collected respirable dust samples for free silica analysis reveal that free silica content of nearly 48% of samples exceed 5% which necessitates determination of case specific maximum safe exposure limit (MEL) for respirable dust generated at these working places. Accordingly, for approximately one third of the results indicate that several workplaces exceed the maximum exposure limits (MEL) for dust and free silica, particularly affecting operators engaged in drilling, LHD, rock breaking and crushing operations etc. The findings highlight the urgent need for effective dust control measures and personal protective equipment to mitigate health risks associated with prolonged exposure. The paper deals with a case study detailing field investigations, results obtained thereof and its interpretation and analysis.

When a fire occurs in an underground coal mine the fire affected panel is sealed and sometimes the whole mine is sealed to control it depending upon its site specific conditions. At this juncture, the mine operator put their utmost effort to keep the mine operative working in other panels of the mine, if available, with due care to the sealed panel. Therefore, at this point of time, it is the duty of the regulatory body to see the status of fire such that activity of the mine can be concentrated in the other part of mine, if available, or in the other ancillary work such as pumping, main fan operation, air sampling and inspection in other sealed off area, if any, etc. Thus scientific agencies put forward their report in two phases. First, gas analysis report spanning over a week or so to determine the status of fire such that other activities of the mine can be resumed. Second, a report containing gas analysis and their interpretation covering a period of six months or more than that. In recent past, a fire occurred in the loose coal of developed workings of No. 3 Seam at SRP 3&3A Incline, Godavari valley coalfield (GVCF), SCCL. Assessment of status of fire has been done in terms of gas concentration, Graham’s ratio, oxides of carbon ratio, Young’s ratio and USBM diagram showing gas explosibility. The paper deals with assessment status report in two phases considering data obtained from two isolation stoppings.

Coal mine fires present a significant global challenge, primarily due to spontaneous combustion—a slow, low-temperature, flameless process sustained by heat generated when oxygen reacts with the surface of condensed-phase fuel. This phenomenon is particularly prevalent in coal mining operations, leading to mine fires, resource depletion, and environmental harm. ​Understanding and mitigating spontaneous combustion are critical for ensuring safety and efficiency in coal mining. Various experimental studies have been conducted to monitor and provide early warnings of spontaneous combustion. Additionally, research has focused on the oxidation characteristics of coal under different conditions to better predict and prevent spontaneous combustion events. The CPT (Crossing Point Temperature) test method serves as a key tool in evaluating a coal sample's predisposition to spontaneous combustion. The laboratory conducted an assessment of inherent coal properties, including ash content, volatile matter, moisture content, and fixed carbon, expressed as percentages. Subsequently, laboratory tests on the coal samples were conducted to determine the crossing point temperature. This temperature represents the point at which the coal temperature equals that of the reference sample (bath), aiding in comprehending the susceptibility of different coal samples to self-heating. In the experimental analysis, the highest values in proximate analysis were observed for moisture content (21.48%), volatile matter (43.09%), ash content (26.84%), and fixed carbon (44.81%) among the five coal samples studied. Additionally, the crossing point temperature exhibited variations, with the maximum and minimum values recorded at 179.6 °C and 135.4 °C, respectively, for the field-collected samples. These findings contribute valuable insights into the characteristics and vulnerability of coal samples to spontaneous combustion, providing crucial information for developing preventive measures and strategies in the ongoing battle against coal mine fires.

Coal continues to be the major source of energy and various subsidiaries of Coal India Limited serve as prominent coal producers towards generation of electricity. Operational safety is a major concern as considerable danger of fire looms due to spontaneous combustion of such power coals in the process of storage and transportation. In total, sixty-six (66) coal samples obtained from various subsidiaries of Coal India such as ECL, BCCL, CCL, WCL, NCL, SECL and SCCL have been investigated to assess their inclination towards auto oxidation leading to spontaneous combustion. For this, a convenient method has been utilized comprising tubular reactors that works on the intrinsic nature of coal and is based on the guidelines of DGMS. It has been observed that on an average the coal samples from BCCL are least prone to auto oxidation, which lead to spontaneous fire, whereas the samples from SECL are most prone. Based on the average data, the proneness follows the order: SECL > ECL > NCL > CCL > WCL > SCCL > BCCL. The auto oxidation phenomenon has been correlated with the composition of coal which suggests that oxygen bearing groups play a significant role. According to the reflectance studies, it is quite evident that lower rank coals are more prone to spontaneous fire.

Rock excavation in the vicinity of sensitive structures or densely populated areas, particularly within a 100-m radius are always a great challenge. One such excavation was carried out at 9 MW Garudeshwar Weir of M/s Sardar Sarovar Narmada Nigam Limited, Gujrat. The management had imposed a condition that explosives blasting is prohibited in particular within 50 m from the retaining wall of the said project located at Narmada River close to the iconic Statue of Unity structure of India. This paper explores the potential of Plasma Blasting Technology (PBT) as a safer and more efficient alternative to chemical explosives particularly for hard rock excavation near existing structures. The said (PBT) excavation techniques were explored and implemented at the said project site successfully. The excavation plan for the powerhouse, penstock, and tail pool areas involves the use of plasma capsules to excavate the hard rock up to 10 m from the retaining wall. Beyond 50 m distance, controlled explosive blasting techniques was used. This site was vital to the blasting process because of structure distance from the blast zone and also due to the significance of the iconic structures. For both excavation methods, 3–10 holes were drilled to a depth of 3–4 m, considering safety aspect. Blast vibration monitoring was conducted at two locations near the retaining wall. Plasma capsules were used for excavation within 10–50 m of the wall, while explosives were employed for distances beyond 50 m. During the hard rock excavation, the recorded vibration level from Plasma blasting (0.5–4.909 mm/s) was much lower than the recorded vibration (1.4–11.57 mm/s) from explosives blasting. The recorded frequency range is also in higher side as compared to explosives blasting. The blast wave signature recorded from plasma and explosive blasting were analyzed for designing the blast. However, as the blasting process revealed, plasma explosives have certain limitations. The energy produced during the plasma blast used to escape through the joints and fractures in the fractured rock mass. Therefore, less fractured or intact rock mass were found to be best suited for plasma blasting.

Blasting is the most important operation in open-pit mining, but they pose significant environmental and social challenges, particularly in densely populated or ecologically sensitive regions. In India, the increasing proximity of mining activities to communities and forest areas raises concerns over ground vibrations, air overpressure, flyrock, dust, and noise emanating from the blasting operations. This paper reviews state-of-the-art tools and techniques for responsible drilling and blasting and discusses various strategies to minimize environmental impacts, enhance safety, and foster community acceptance. The key advancements include the adoption of automated and electric-powered drilling rigs, electronic detonators, and innovative techniques such as stemming plugs, air-decks, blast face muffling, and water- based dust suppression tools. The integration of artificial intelligence and data analytics enables dynamic, site-specific blast designs tailored to diverse geological and social contexts. Field studies demonstrate that precise blast design optimization, including burden adjustment, charge segmentation, and air-deck blasting, can significantly reduce ground vibrations and air overpressure while maintaining productivity. The findings highlight the critical role of regulatory compliance, technological innovation, and holistic planning in achieving sustainable and responsible blasting practices in India's mining sector.

Permitted emulsion explosives are exclusively designed for hazardous flammable environments, offer controlled detonation, and low incendivity during blasting operations. However, poor permitted explosive composition can sometimes lead to unintended operational inefficiencies and safety threats in underground coal mines. As per the United States Environmental Protection Agency (EPA) report, a methane-rich environment in underground coal mines poses significant calamities, predominantly when methane concentration reaches its flammable range in between 5 to 15% in mine air. Therefore, understanding the ignition behaviour of permitted explosives under critical methane environments is necessary to paramount a sustainable and safe underground mine blasting operations. This study aims to investigate the incendivity properties of different permitted explosive (PE-5) samples in the presence of methane gas environment within a cannon gallery. Furthermore, the deflagration studies of permitted explosive (PE-5) were examined. Additionally, detonation properties of explosive (PE-5) samples were carried out in Laboratory. Studies revealed that sample-1 of PE–5 was unsuitable for underground coal mines as per Directorate General of Mines Safety (DGMS) safety standards. Whereas, sample-2 of PE-5 was found suitable for underground gaseous coal mines considering Incendivity experiments. Moreover, this article provides an in-depth understanding of incendivity risks and detonation behaviour of permitted explosive (PE-5) samples.

Drilling and blasting are among the most efficient techniques for the development of headings in underground mines or tunnelling projects. The common drilling pattern used for smaller dimension headings in various cases is burn cut pattern. This pattern is associated with one major problem: insufficient pull. It is important to maximise pull in drivage blasts to reduce project costs and enhance efficiency. The maximisation of pull can be effectively achieved by predicting it prior to the execution of blast. Therefore, this study has been carried out to predict the percentage of pull obtained during drivage blasts in an underground metalliferous mine using machine learning algorithm. Specifically, random forest and k nearest neighbour algorithms were used for this purpose. Five parameters namely number of holes, average hole depth, explosive per hole, maximum explosive weight per delay and total explosive fired were collected from the experimental site for model development. A total of 146 datasets were collected and split into 70:30 ratio for training and testing purposes. The performance of predictive models was assessed using RMSE and R2 values. The RMSE and R2 obtained for the KNN model were 3.411 m and 0.74 respectively, while for the RF model, they were 1.731 m and 0.907 respectively. This indicates that the accuracy of pull prediction using RF model is higher as compared to the KNN model. Therefore, an RF based predictive model can be effectively used for the prediction of pull at the experimental site.

The mining industry, a critical component of mining cost is Explosive cost. This research paper aims to explore the relationships between key blast design parameters—such as explosive density, powder factor, stab holes, and overall blast design—and their effects on blast fragmentation in open-pit mining by designing different blast design in LS DYNA and SHOTPlus. The goal is to develop a more efficient blasting process that not only improves material fragmentation, thus enhancing loading and hauling efficiencies. Blast design is carried out before every blast and simulation is done to predict the fragmentation. Use of stab hole with production hole is done in different cases to improve the powder factor. Rock fracture and the ensuing fragment muck-piling are simulated using a hybrid finite-discrete element approach (FEM-DEM) under a variety of blasting conditions. To calibrate the hybrid FEM-DEM technique, the modeled cracked, crushed, and long radial fracture zones are compared to the body of existing literature. Furthermore, the hybrid model accurately simulates the process of rock fragmentation that occurs during blasting. The findings show that the hybrid FEM-DEM approach performs better at precisely simulating the dynamic fracture behavior of rock under blast impact loads than either solely continuous or discontinuous approaches.

Surface mining is a prevalent technique for mineral resource extraction and offers notable advantages in terms of safety and cost-effectiveness compared to underground mining. Blasting remains the most commonly used method for rock mass extraction in surface mining owing to its cost efficiency and adaptability to a wide range of geological conditions. However, the ground vibrations generated by blasting pose significant challenges, particularly concerning the stability of the internal overburden dumps. The impact of blast-induced vibrations on the stability of dragline dumps is of particular concern and requires a detailed investigation. In this study, a 2D numerical model was developed using the RS2 software to simulate the effects of blasting on dragline dumps. The seismic coefficient, which quantifies the influence of blast-induced vibrations on dump stability, was derived from field measurements of vibration-time data. The study found that the factor of safety of the dragline dump slope is 1.22 under static conditions. The factor of safety of the internal dragline dump due to blasting was found to be 1.18, indicating a minimal effect of blasting on the dump slope. Additionally, the study concluded that the impact of the ground vibrations induced by blasting had a negligible effect on the stability of the internal dragline dump beyond an approximate distance of 150 m. The findings underscore the importance of understanding the dynamic effects of blasting on dragline dump stability, and propose a methodology for evaluating these effects through numerical modeling.

During opencast mining of hard rock deposits, maximizing explosive energy efficiency improves fragmentation and rock displacement. High-speed videography enables millisecond-scale analysis of blast dynamics, facilitating the assessment of how explosive energy distribution influences burden movement and velocity, both of which are critical for fragmentation efficiency, safety, and overall blast performance. Determining the burden rock velocity requires high-speed videography and advanced processing software, rendering it a costly and time-intensive process. In this context, a study is proposed to predict Burden Rock Velocity (BRV) in limestone bench blasting using high-speed videography. Field visits were conducted at three mechanized limestone mines in southern India, where data from 166 blasts were collected. BRV was determined using high-speed videography and analyzed with ProAnalyst software. Eight key input parameters such as bench height (BH), total explosive charge (TEC), stiffness ratio (K), charge factor (CF), joint spacing (Js), uniaxial compressive strength (UCS), P-wave velocity (P-W), and rock density (ρ) were used to develop a predictive model for BRV based on a hybrid extreme gradient boosting (XGBoost) algorithm optimized with Ant Colony Optimization (ACO). The hybrid XGBoost–ACO model was compared with the baseline XGBoost model. Results demonstrated that the optimized XGBoost–ACO model achieved superior performance, with an R2 of 0.964, RMSE of 0.14, and MAE of 0.272, outperforming the baseline XGBoost model (R2 = 0.909).