Prevalence of nano-sized coal mine dust in North and Central Appalachian coal mines - Insights from SEM-EDS imaging.

The increasing prevalence of coal mine dust-related lung diseases in coal miners calls for urgent and meticulous scrutiny of airborne respirable coal mine dust (RCMD), specifically focusing on particles at the nano-level. This necessity is driven by expanding research, including the insights revealed in this paper, that establish the presence and significantly increased toxicity of nano-sized coal dust particles in contrast to their larger counterparts. This study presents an incontrovertible visual proof of these tiny particulates in samples collected from underground mines, utilizing advanced techniques such as scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The intricate elemental composition of nano-sized coal dust identified through EDS analysis reveals the presence of elements such as silica and iron, which are known to contribute to lung pathologies when inhaled over prolonged periods. The outcomes of the statistical analyses reveal significant relationships between particle size and elemental composition, highlighting that smaller particles tend to have higher carbon content, while larger particles exhibit increased concentrations of elements like silica and aluminum. These analyses underscore the complex interactions within nano-sized coal dust, providing critical insights into their behavior, transport, and health impacts. The nano-sized coal dust could invade the alveoli, carrying these toxic elements from where they are impossible to exhale. The revelation of nano-sized coal dust's existence and the associated health hazards necessitate their incorporation into the regulatory framework governing the coal mining industry. This study lays the groundwork for heightened protective measures for miners, urging the invention of state-of-the-art sampling instruments, comprehensive physicochemical profiling of RCMD nanoparticles, and the pursuit of groundbreaking remedies to neutralize their toxic impact. These findings advocate for a paradigm shift in how the coal mining industry views and handles particulate matter, proposing a re-evaluation of occupational health standards and a call to action for protecting coal miners worldwide.

Effects of intrinsic properties, particle size, bulk density, and specific gravity on thermal properties of coal dusts.

Thermal properties of pulverized coal govern the heat transfer and greatly influence the coal dust explosion and spontaneous combustion processes. This study measures the thermal properties of five coal samples at six distinct particle sizes using an advanced thermal property analyzer. The thermo-physical properties of coal dust positively correlated with the particle size. Thermal conductivity, diffusivity, and specific heat capacity increased with the ash percentage, bulk density, and specific gravity of coal dust. In contrast, they negatively correlated with the fixed carbon and volatile content of coal. Empirical relations between the thermo-physical properties were developed. The thermal conductivity, diffusivity, and specific heat capacity of coal dusts varied in the range of 0.091-0.147 W/mK, 0.125-0.164 mm2/s, and 0.715-0.945 MJ/m3K, respectively. With increase in particle size from < 38 to 500-1000 µm, thermal conductivity, thermal diffusivity, and specific heat capacity increased in the range of 25.60-32.89%, 9.76-22.11%, and 9.57-20.80%, respectively, for different coal samples.

Methane migration and explosive fringe localisation in retreating longwall panel under varied ventilation scenarios: a numerical simulation approach.

Methane-based inflammable underground coal mine environment has led to catastrophic losses in the past. Migration of methane from the working seam and desorption region above and below the seam causes explosion hazard. In this study, the computational fluid dynamics (CFD)-based simulations of a longwall panel in a methane-rich inclined coal seam of the Moonidih mine in India established that the ventilation parameters greatly influence the methane flow in the longwall tailgate and porous medium of the goaf. The field survey and CFD analysis revealed that methane accumulation on the "rise side" wall of the tailgate is attributable to the geo-mining parameters. Further, the turbulent energy cascade was observed to impact the distinct dispersion pattern along the tailgate. The numerical code was used to investigate the changes in ventilation parameters made to dilute the methane concentration in the longwall tailgate. Methane concentration in the tailgate outlet decreased from 2.4 to 1.5% as the inlet air velocity increased from 2 to 4 m/s. The oxygen ingress into the goaf increased from 0.5 to 4.5 lps as the velocity was increased, causing the explosive zone in the goaf to expand from 5 to 100 m. Amongst all velocity variations, the lowest level of gas hazard was observed at an inlet air velocity of 2.5 m/s. This study, thus, demonstrated the ventilation-based numerical method to assess the coexistence of gas hazard in the goaf and longwall workings. Moreover, it provided impetus to the necessity of novel strategies to monitor and mitigate the methane hazard in U-type longwall mine ventilation.

Prediction of spontaneous combustion susceptibility of coal seams based on coal intrinsic properties using various machine learning tools.

Spontaneous combustion of coal leading to mine fire is a major problem in most of the coal mining countries in the world. It causes major loss to the Indian economy. The liability of coal to spontaneous combustion varies from place to place and mainly depends on the coal intrinsic properties and other geo-mining factors. Hence, the prediction of spontaneous combustion susceptibility of coal is of utmost importance for preventing the risk of fire in coal mines and utility sectors. Machine learning tools are pivotal in system improvements in relation to the statistical analysis of experimental results. Wet oxidation potential (WOP) of coal determined in the laboratory is one of the most relied indices used for assessing the spontaneous combustion susceptibility of coal. In this study, multiple linear regression (MLR) and five different machine learning (ML) techniques, such as Support Vector Regression (SVR), Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB) and Extreme Gradient Boost (XGB) algorithms, were used to predict the spontaneous combustion susceptibility (WOP) of coal seams based on the coal intrinsic properties. The results derived from the models were compared with the experimental data. The results indicated excellent prediction accuracy and ease of interpretation of tree-based ensemble algorithms, like Random Forest, Gradient Boosting and Extreme Gradient Boosting. The MLR exhibited the least while XGB demonstrated the highest predictive performance. The developed XGB achieved R2 of 0.9879, RMSE of 4.364 and VAF of 84.28%. In addition, the results of sensitivity analysis showed that the volatile matter is most sensitive to the changes in WOP of coal samples considered in the study. Thus, during spontaneous combustion modelling and simulation, volatile matter can be used as the most influential parameter for assessing the fire risk of the coal samples considered in the study. Further, the partial dependence analysis was done to interpret the complex relationships between the WOP and intrinsic properties of coal.

Development of Empirical and Artificial Neural Network Model for the Prediction of Sorption Time to Assess the Potential of CO2 Sequestration in Coal.

Geological sequestration of CO2 in a coal seam is considered an attractive option to reduce the carbon footprint. It has an additional advantage of enhancing the recovery of coalbed methane, which has less sorption affinity toward coal in comparison to CO2. Desorption of gases from coal is controlled by various parameters, including reservoir depth and coal rank. A representative factor for desorption and diffusion in coal is the sorption time. It is an indicator which helps in estimation and evaluation of gas movement in the coal seam. Coals exhibiting high sorption time allow greater quantities of CO2 injection and hold potential for CO2 sequestration. Therefore, reliable and cost-effective estimation of sorption time is very important prior to investment in projects related to CO2 sequestration. Generally, proximate and gas content analyses are part of the preliminary analysis of coal for the assessment of its potential as a coal-bed methane reservoir. In this study, data generated using these analyses were found very useful for estimating the sorption time and CO2 sequestration potential of coal. The coal samples were collected from different depths of the Mand Raigarh coalfield for testing, and an empirical equation and artificial neural network (ANN)-based model have been developed to predict the sorption time of coal. The developed empirical equation predicts the sorption time with a coefficient of determination value of 0.88 and a root mean squared error value of ±1.07 days. Furthermore, the developed ANN model has been found to be very efficient in prediction with a correlation coefficient value of 0.97.

Utilization of ferrous slags as coagulants, filters, adsorbents, neutralizers/stabilizers, catalysts, additives, and bed materials for water and wastewater treatment: A review.

Solid waste is currently produced in substantial amounts by industrial activities. While some are recycled, the majority of them are dumped in landfills. Iron and steel production leaves behind ferrous slag, which must be created organically, managed wisely and scientifically if the sector is to remain more sustainably maintained. Ferrous slag is the term for the solid waste that is produced when raw iron is smelted in ironworks and during the production of steel. Both its specific surface area and porosity are relatively high. Since these industrial waste materials are so easily accessible and offer such serious disposal challenges, the idea of their reuse in water and wastewater treatment systems is an appealing alternative. There are many components such as Fe, Na, Ca, Mg, and silicon found in ferrous slags, which make it an ideal substance for wastewater treatment. This research investigates the potential of ferrous slag as coagulants, filters, adsorbents, neutralizers/stabilizers, supplementary filler material in soil aquifers, and engineered wetland bed media to remove contaminants from water and wastewater. Ferrous slag may provide a substantial environmental risk before or after reuse, so leaching and eco-toxicological investigations are necessary. Some study revealed that the amount of heavy metal ions leached from ferrous slag conforms to industrial norms and is exceedingly safe, hence it may be employed as a new type of inexpensive material to remove contaminants from wastewater. The practical relevance and significance of these aspects are attempted to be analyzed, taking into account all recent advancements in the fields, in order to help in the development of informed decisions about future directions for research and development related to the utilization of ferrous slags for wastewater treatment.

Physico-chemical characteristics of pulverized coals and their interrelations-a spontaneous combustion and explosion perspective.

Characteristics of pulverized coals have significant influence on the spontaneous combustion and explosion processes. This paper presents an experimental and theoretical framework on physico-chemical characteristics of coal and their interrelations from spontaneous combustion and explosion perspectives. The chemical properties, morphology, bulk density, particle size, and specific surface area of pulverized coals from nine different coal subsidiaries of India are vividly investigated in five distinct sizes. Moreover, the effects of particle size on bulk density, specific surface area, and N2 adsorption capacity of pulverized coals are critically analyzed. With decrease in particle size, the bulk density of pulverized coals decreased, and the specific surface area and N2 adsorption capacity increased. The relationships of bulk density and specific surface area of pulverized coals with particle size are established. Moreover, the specific surface areas determined by both the particle sizing and BET methods are compared, and correlation factors between them are determined. This study generated insightful coal characteristic data, which can be useful for furthering research on spontaneous combustion and explosion involving pulverized coals.

Dust pollution hazard and harmful airborne dust exposure assessment for remote LHD operator in underground lead-zinc ore mine open stope.

Underground mines embroil several occupational hazards, including airborne dust generation from various mining operations. Line-of-sight remote Load Haul Dumper (LHD) mucking is adopted to draw the blasted muck from unsupported open stopes in underground metalliferous mines. Assessment of particulate matter (PM) concentrations and remote LHD operator's exposure is crucial for devising appropriate dust control measures. In this study, PM generated due to mucking in longhole open stope by line-of-sight remote LHD during downcast airflow was measured using real-time aerosol spectrometers. The particulate concentrations at upstream and downstream of dust source were analysed for various particle sizes as well as occupational dust types, such as alveolic and thoracic. The airborne dust concentration of ≤ 10 µm (PM10), ≤ 5 µm, and ≤ 1 µm (PM1) size at operator's location in downstream was measured 71.3%, 28.5%, and 3.0%, respectively. The alveolic and thoracic dust types, respectively, were determined 25.1% and 74.2% in downstream and 48.9% and 84.6% in upstream total airborne dust concentration (311 ± 246 µg/m3). Dilution of airborne dust generated due to muck sliding inside the stope was analysed with time. Moreover, dust concentrations under typical airflow scenarios encountered in open stope were simulated using Ventsim software to identify the potential dust exposure hazard for remote LHD operator. The simulation revealed that downcast airflow causes maximum exposure of harmful airborne dust for remote LHD operator. This study enhanced the understanding of exposure potential of airborne dust during remote LHD mucking. Moreover, it emphasised adoption of tele-remote-operated LHD and automated mucking operation in open stopes.

Sustainable Transportation, Leaching, Stabilization, and Disposal of Fly Ash Using a Mixture of Natural Surfactant and Sodium Silicate.

The present study evaluates the transportation, leaching, and stabilization ability of novel saponin extracted from the fruits of Acacia auriculiformis. To enhance the dispersing behavior of the fly ash slurry (FAS) at a lower dosage of sodium silicate, A. auriculiformis was incorporated in FAS. In addition to the rheological study, an attempt has been made to remove heavy metals through leaching for the safe disposal of FAS. Critical factors such as the fly ash (FA) concentration, saponin dosage, surface tension, ζ potential, temperature, and combination of saponin and sodium silicate, affecting the rheology of FAS, were extensively studied. The addition of a nonionic natural surfactant saponin has been proved to enhance the wettability of FA particles by decreasing the surface tension of FAS. The obtained rheology results were compared with the stabilization yield of the previously reported commercial surfactant cetyltrimethylammonium bromide. The incorporation of sodium silicate in the FAS system was found to be phenomenal in the settling and stabilization of FAS, thereby developing reaction products like sodium aluminum silicate (N-A-S). This facilitates the sustainable disposal of FA preventing air pollution after dewatering. The formation of N-A-S was further supported by scanning electron microscopy (SEM) and X-ray diffraction (XRD) studies.

An improved mathematical model for prediction of air quantity to minimise radiation levels in underground uranium mines.

Ventilation is the primary means of controlling radon and its daughter concentrations in an underground uranium mine environment. Therefore, prediction of air quantity is the vital component for planning and designing of ventilation systems to minimise the radiation exposure of miners in underground uranium mines. This paper comprehensively describes the derivation and verification of an improved mathematical model for prediction of air quantity, based on the growth of radon daughters in terms of potential alpha energy concentration (PAEC), to reduce the radiation levels in uranium mines. The model also explains the prediction of air quantity depending upon the quality of intake air to the stopes. This model can be used to evaluate the contribution of different sources to radon concentration in mine atmosphere based on the measurements of radon emanation and exhalation. Moreover, a mathematical relationship has been established for quick prediction of air quantity to achieve the desired radon daughter concentration in the mines.

Radon emanation from backfilled mill tailings in underground uranium mine.

Coarser mill tailings used as backfill to stabilize the stoped out areas in underground uranium mines is a potential source of radon contamination. This paper presents the quantitative assessment of radon emanation from the backfilled tailings in Jaduguda mine, India using a cylindrical accumulator. Some of the important parameters such as (226)Ra activity concentration, bulk density, bulk porosity, moisture content and radon emanation factor of the tailings affecting radon emanation were determined in the laboratory. The study revealed that the radon emanation rate of the tailings varied in the range of 0.12-7.03 Bq m(-2) s(-1) with geometric mean of 1.01 Bq m(-2) s(-1) and geometric standard deviation of 3.39. An increase in radon emanation rate was noticed up to a moisture saturation of 0.09 in the tailings, after which the emanation rate gradually started declining with saturation due to low diffusion coefficient of radon in the saturated tailings. Radon emanation factor of the tailings varied in the range of 0.08-0.23 with the mean value of 0.21. The emanation factor of the tailings with moisture saturation level over 0.09 was found to be about three times higher than that of the absolutely dry tailings. The empirical relationship obtained between (222)Rn emanation rate and (226)Ra activity concentration of the tailings indicated a significant positive linear correlation (r = 0.95, p < 0.001). This relationship may be useful for quick prediction of radon emanation rate from the backfill material of similar nature.

Assessment of (222)Rn emanation from ore body and backfill tailings in low-grade underground uranium mine.

This paper presents a comparative study of (222)Rn emanation from the ore and backfill tailings in an underground uranium mine located at Jaduguda, India. The effects of surface area, porosity, (226)Ra and moisture contents on (222)Rn emanation rate were examined. The study revealed that the bulk porosity of backfill tailings is more than two orders of magnitude than that of the ore. The geometric mean radon emanation rates from the ore body and backfill tailings were found to be 10.01 × 10(-3) and 1.03 Bq m(-2) s(-1), respectively. Significant positive linear correlations between (222)Rn emanation rate and the (226)Ra content of ore and tailings were observed. For normalised (226)Ra content, the (222)Rn emanation rate from tailings was found to be 283 times higher than the ore due to higher bulk porosity and surface area. The relative radon emanation from the tailings with moisture fraction of 0.14 was found to be 2.4 times higher than the oven-dried tailings. The study suggested that the mill tailings used as a backfill material significantly contributes to radon emanation as compared to the ore body itself and the (226)Ra content and bulk porosity are the dominant factors for radon emanation into the mine atmosphere.

Radon emanation from low-grade uranium ore.

Estimation of radon emanation in uranium mines is given top priority to minimize the risk of inhalation exposure due to short-lived radon progeny. This paper describes the radon emanation studies conducted in the laboratory as well as inside an operating underground uranium mine at Jaduguda, India. Some of the important parameters, such as grade/(226)Ra activity, moisture content, bulk density, porosity and emanation fraction of ore, governing the migration of radon through the ore were determined. Emanation from the ore samples in terms of emanation rate and emanation fraction was measured in the laboratory under airtight condition in glass jar. The in situ radon emanation rate inside the mine was measured from drill holes made in the ore body. The in situ(222)Rn emanation rate from the mine walls varied in the range of 0.22-51.84 × 10(-3) Bq m(-2) s(-1) with the geometric mean of 8.68 × 10(-3) Bq m(-2) s(-1). A significant positive linear correlation (r = 0.99, p < 0.001) between in situ(222)Rn emanation rate and the ore grade was observed. The emanation fraction of the ore samples, which varied in the range of 0.004-0.089 with mean value of 0.025 ± 0.02, showed poor correlation with ore grade and porosity. Empirical relationships between radon emanation rate and the ore grade/(226)Ra were also established for quick prediction of radon emanation rate from the ore body.