Jharia Coalfield Research Articles

Processing of low-grade Indian coking coal using air fluidized dense medium bed separator and flotation technique

ABSTRACT A dry coal beneficiation process was developed on a batch scale using a gas-solid fluidized bed separator to upgrade the low-grade coking coal from the Jharia Coalfield. Coal collected from Ena open cast mines of Kusunda area, Dhanbad was crushed to 50 mm and 25 mm sizes for dry processing using the dense medium fluidized bed separator followed by froth flotation for particles below 500 microns. Coal with a + 6 mm size fraction was processed via the dry route, while coal with a −6 mm size fraction and pre-concentrate from the dry route was further crushed to 0.5 mm for the froth flotation process using various reagents. The effect of various process parameters of the dry separator such as bed height, dense medium size, air flow rate were studied using CFD software in a simple 2D plane. The study revealed that the dry process for the −50 + 6 mm fraction achieves higher organic efficiency (90%) compared to −25 + 6 mm (81%) due to better washing characteristics. Dry deshaling of high-ash coking coal (−50 + 6 mm) can reduce the ash content from 35.3% to 29.6% with a yield of 75.4%. The flotation process using NALCO synthetic reagents is highly effective, reducing the feed ash content from 27.3% to 17.5% with a notable yield of 85.0%.

Machine learning approach for detection of land subsidence induced by underground coal fire using multi-sensor satellite data

ABSTRACT High-rank coal reserves in Jharia Coalfield (JCF, India), are invariably associated with underground coal fires and land subsidence. This study explores multi-sensor time series satellite data (Landsat 8 OLI and Sentinel-1) through machine learning (ML) to determine the regional ground deformation accompanying coal fires and their contextual relationship. The results show that the highest degree of subsidence is closely associated with the active mine benches with overburden dumps. The relationship between the coal fire and land subsidence parameters is considered as a binary classification problem, explored by calculating the probability of subsidence with a desirable categorical outcome through different ML models. The accuracy of the models is validated using performance metrics that shows that the Random Forest (RF) metrics predict the probability of deformation locations in response to the volume reduction of the burning coal fire and vertical compression due to Overburden Dump (OBD) near active mine benches. The estimated displacement trends have been used to forecast the Autoregressive Integrated Moving Average (ARIMA) method, estimated using Line-of-Sight (LOS) displacement values vary around the best fit within the 95% confidence limits. The trend shows ∼15–25% increase in subsidence compared to the cumulative subsidence.

Temporal and Spatial Dynamics of Subsidence in Eastern Jharia, India

Abstract. Jharia, one of the oldest and most significant mining areas, is facing the issue of subsidence for different reasons, such as coal fires, underground mining, and soil sensitivity towards seasonal change. This paper concentrates on the subsidence analysis of the Eastern region of the Jharia Coalfield from January 2018 to January 2021. Sentinal-1 SLC SAR imagery is used for multi-image sparse point processing and generating time series. Data processing is done using SARPROZ software. Further, the PS points inside the study area are filtered, and a classification map is drawn using ArcGIS software. The yearly percentage change of different classified zones is examined. The percentage change is significant for subsiding and uplifting zones but minimal for stable zones. Different critically and highly subsiding areas are identified by overlaying the classification zones on the topographic map. The seasonal variation of cumulative displacement is also examined, and patterns are analyzed. Different regions have different sensitivity towards seasonal variation. Overall, this paper provides insightful deformation results over Eastern Jharia for 2018–2021.

Quantifying environmental impact of unplanned mining through integrated non-invasive geophysical methods: a case study from Jharia coalfield, India

Quantifying environmental impact of unplanned mining through integrated non-invasive geophysical methods: a case study from Jharia coalfield, India

Monitoring Post-Mining Reclamation Success in Jharia Coalfield Using Geospatial Technology

Monitoring Post-Mining Reclamation Success in Jharia Coalfield Using Geospatial Technology

Mapping of old coal mine galleries near railway track using electrical resistivity tomography and magnetic approaches in Tundu, Jogidih Colliery, Jharia Coalfield, India

Mapping of old coal mine galleries near railway track using electrical resistivity tomography and magnetic approaches in Tundu, Jogidih Colliery, Jharia Coalfield, India

Mineralogical and geochemical characterization of coal debris from major coal fields in India: Implications on respirable dust hazards

Coal mining activities have major impact on the surrounding regions through flying coal dust particles. In this study, we investigate the differences in mineralogy and geochemistry between the deposited coal dust and the respirable fractions of coal dust samples collected from the coal mining regions of Jharia and Raniganj coalfields in India. The coal dust samples were investigated in detail through various analytical techniques including FE-SEM, TGA, and particle size analyzer. The proximate analysis of the coal dust indicated that the ash content is 75%, moisture 0.5%, volatile matter 8.4%, and fixed carbon 16% in the Jharia coal fields, while the respective values of Raniganj coal values are 68.3%, 2.8%, 14.6%, and 14.2%. The average carbon content in the Raniganj coal dust particles is higher than that of Jharia (22.62 vs. 21.50%) and suggest that the coal dust particles were transported from the coal mining areas. The scanning electron microscopy images suggest that the coal dust samples have spherical and irregular morphologies. The carbon and sulphur in these samples were possibly derived from the organic matter of the coal materials, which were transported by air as dust particles. The particle size distribution of the coal dust samples revealed that 75–95% of these by volume are in the fraction of <600 μm. It can be inferred that the relatively fine particles of the coal dust samples contained 70% of ash comprising of mostly silica and clay as maximum respirable dust, which is hazardous to both human life and the environment. Our study provides insights into the air pollution and health risks around mining areas.

Effects of Temperature and Rainfall Pattern on The Abundance of Orthopteran Insects in Coal Mines Ecological Restoration Areas of Jharia Coal Fields, Jharkhand, India

Ecological restoration on coal mines' overburden dumps provides a suitable environment for the study of primary succession. The Abundance and diversity study of orthopteran insects and their diversity pattern with temperature and rainfall provides a lot about the stage of succession and ecosystem complexity in the area. Orthopteran diversity of two sites covering ten years of plantation having different types of vegetation were monitored. Tropical conditions like changes in rainfall and temperature influence the abundance of insects. An increase in monsoon increases the abundance whereas as the temperature increases the abundance decreases. In herbaceous habitats, insects’ diversity increases which gives a recolonization pattern for the ecosystem. This helps to increase the livelihood and conservation of the ecosystem and directs to sustain the SDG 15 goal.

Assessment of noise pollution and associated subjective health complaints in Jharia Coalfield, India: A structural equation model analysis

Abstract Noise pollution in the mining industry has become a serious concern in recent years and has aroused extensive health complaints for the mine workers. Despite the increasing knowledge of the association between noise exposure and health effects, only a few studies have investigated the adverse health effects due to noise pollution in the mining industry. The present study aims to determine the interrelationship of mining noise (MN), demographic characteristics (DC), noise sensitivity and noise annoyance (NA) with subjective health complaints (SHC). A PLS-SEM approach has been used to assess the direct and indirect relationship between the influencing factors on the SHC of 272 people in the mines. Field measurements were made using a sound level meter to evaluate the ambient noise levels for 1 h duration during day and night hour shifts in Dhansar and Bastacolla mining areas of the Jharia coalfield. The noise maps revealed that most of the locations have noise levels higher than 70 dB(A). The statistical model suggests that NA is the most important predictor of SHC. Besides, DC and MN induce only indirect effect on SHC. The study shall facilitate in developing a decision support tool for the assessment of noise levels, induced annoyance, SHC, and suitable policy interventions for controlling noise exposure among mine workers in the coalfield.

Multiphase assessment of post-mining effect on railway structures: A case study of Jharia Coalfield

Multiphase assessment of post-mining effect on railway structures: A case study of Jharia Coalfield

A comparative evaluation of deep convolutional neural network and deep neural network-based land use/land cover classifications of mining regions using fused multi-sensor satellite data

Accurate land use/land cover (LULC) classifications using satellite data is a challenging task due to the limited spatial and spectral resolutions of different satellite data. Moreover, the challenge is more severe for LULC classification of mining regions as no standardized spectral signature is available for the detection of coal mining regions. Thus, the current study aims to design deep learning algorithms using fused data of three satellite sensors (LISS-IV, Landsat-8, and Sentinel-2A) for LULC classification of mining regions. The fused image was derived from three satellite sensors using a discrete cosine transform (DCT) with a spatial correlation approach. A comparative evaluation of deep convolutional neural network (DCNN) and deep neural network (DNN) models in LULC classification of mining regions is conducted. Moreover, the performances of the models with fused data are compared with the performance of the same model with individual sensor data. The study area chosen to execute the work is Jharia Coalfield, which comprises five key LULC types, viz. barren land, coal mining region, built-up area, water body, and vegetation. A total of 6000 image samples of 6 × 6 sizes and 216,000 pixels were used to train and validate the DCNN and DNN models, respectively. That is, the DCNN model uses the object dataset and DNN uses the pixel dataset for model training and validation. The DCNN model achieved high training and validation accuracies (99.8% and 99.2%), while the DNN model achieved relatively lower accuracies (85.3% and 81.8%). The study evaluates the performance of both models further by employing confusion matrix parameters to measure accuracy, error, precision, and recall for each class. The results reveal that the DCNN model consistently outperforms the DNN model, showcasing accuracy, error rates, precision, and recall ranging from 99.83% to 99.99%, 0.01% to 0.17%, 99.52% to 99.99%, and 99.40% to 99.99% on the training dataset, and 99.50% to 99.99%, 0.01% to 0.50%, 98.35% to 99.99%, and 98.33% to 99.99% on the validation dataset, respectively. In comparison, the DNN model demonstrates values ranging from 90.36% to 99.90%, 0.01% to 9.64%, 75.10% to 99.53%, and 66.99% to 99.99% on the training dataset, and 88.50% to 99.94%, 0.06% to 11.50%, 72.25% to 99.66%, and 62.50% to 99.99% on the validation dataset. These findings showed that the DCNN classification algorithm outperforms the DNN classification algorithm. Moreover, the comparative performances of the DCNN model with different datasets indicate that the model with fused images outperformed the model with individual sensor images.

Imaging ‘Slow Violence’ in the Jharia Coalfields of India: Disrupting Energy Modernity through Photography

The paper contributes to the field of Energy Humanities from an often under-discussed Global South perspective, foregrounding cultural productions around coal extraction as a potential tool for disrupting energy modernity. Using photographs from the Jharia coalfields in Jharkhand, India, as primary sources, we examine coal extraction as a form of ‘slow violence’. The discursive ploy of energy modernity has been to make fossil fuels an emblem of socio-economic progress. The paper adds to critiques seeking to reframe this narrative and argues that the framework of slow violence reconfigures coal mining as a form of eco-social dispossession. The paper traces how the photography of Sebastian Sardi and Ronny Sen give visibility to those spaces—both somatic and geographical—which are not considered a legitimate part of the country’s development rhetoric but are nonetheless necessary for its creation and maintenance.

A synergetic approach for quantification and analysis of coal fires in Jharia Coalfield, India

Temporal monitoring and understanding of the dynamics of coal fires in the Jharia Coalfield (JCF) are required to reduce its effect on sustainable industrial growth, environment & human safety. This research explores temporal dataset of Landsat 8 OLI Thermal Infrared Sensor (TIRS) from 2015 to 2019 to detect, map and quantify coal fire affected areas in JCF at the colliery level. The results indicated that the East Barora, Sijua, Katras, Kusunda, Kustore, Pootkee Balihari, Bastacolla, Jharia, and Lodna are intensely fire-affected collieries with a significant increase in risk area from 4.57 km2 in 2015 to 11.43 km2 in 2019. The central part of the area is highly affected. The extent of coal fire shows temporal fluctuation between 2015 and 2019, but overall exhibit a significant increase from 2.76 km2 to 7.52 km2. Sijua, Katras, Kusunda, Lodna, and Kustor occupying the central and southeastern parts of the JCF, respectively, constitute nearly ∼85% of the total fire. However, in comparison to the information inferred from the field-based knowledge, the results derived from satellite-based observations are slightly underestimated due to the reason that the coal fire-derived thermal anomalies are the function of depth, intensity and proportion of coal fire in a coarse resolution TIR pixel, structural attributes, interventions from the mining operation and regional land use planning. Further, the risk areas map out using the TIR-based approach have been integrated with the prevailing structural attributes and Landsat 8 OLI-derived surface thermal anomalies, which enabled an understanding of the dynamics of coal fire propagation in JCF.

Assessment of financial and environmental impacts of pre-mining methane drainage in Indian scenario: A case study using Jharia coal seams

Global coal deposits are often associated with trapped methane, whose nature and composition are similar to that of the natural gas. Compared to coal, this trapped methane is a superior as well as cleaner fuel but its fugitive emission from coal mines is also a serious environmental threat. In coal mines, sudden leakage of trapped methane leads to air contamination and gas explosion resulting into catastrophic accidents. Also, methane is a potential GHG and its fugitive emission has several adverse effects on environment such as global warming, climate change and so on. Since, GWP of methane is twenty-five times more than carbon dioxide and coal mines act as a potential source of methane emission, therefore methane handling becomes a primary responsibility for the coal dependent countries of the world for reducing the atmospheric emissions. The drainage of trapped methane from the coal seams prior to mining can be a possible solution towards reducing its fugitive emission and to utilize the same as a promising source of clean unconventional energy. The present paper discusses the environmental as well as the economic advantage of pre-mining methane drainage using the case study of highly gassy coal seams of Jharia coalfield.

SBAS-InSAR analysis of regional ground deformation accompanying coal fires in Jharia Coalfield, India

The present study explores a holistic approach toward better assimilation of the contextual relationship between coal fire-induced land subsidence in the Jharia Coalfield (JCF), India. For the process, 31 consecutive Sentinel-1A and Landsat 8 scenes of 2018 were processed to estimate mean Line-of-Sight displacement and Land Surface Temperature (LST) in JCF, respectively. The results indicated that the displacement rate in JCF significantly varies at active mine benches and overburden dump, and high degree of displacement owing to the additive compression inducted along with the volume reduction at the subsurface. The estimated displacement accounts were then spatially correlated with the thermally anomalous pixels to determine the categories of subsidence. Further, the contextual relationship between the displacements estimates (dependent variable) with a set of explanatory variables, i.e. pixel integrated LST was tested using Binary Logistic Regression. The performance of the model was cross-validated using statistical parameters derived from the confusion matrix.

Design of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region

Accurate classification of the satellite image is always a challenging task, and hence uncertainty involves in the change detection analysis in the land use for mining regions. The present study attempts to demonstrate the development of an optimized deep convolutional neural network with stochastic gradient descent momentum (DCNN + SGDM) using Linear imaging self-scanning sensor- IV (LISS-IV) data for automatic classification of satellites image to assess the land-use pattern in a mining region. An image dataset was prepared by extracting the LISS-IV image samples of five common land-use types (coal mining region, built-up area, barren land, vegetation, and waterbody). A total of 6250 image samples (1250 for each class) of 6 × 6 patch size were generated for the model training and testing. The study area selected for analyzing the model performance and impact analysis is Jharia coalfield (JCF), Jharkhand. The study area consists of 5184 × 7794 pixels. The model performance results indicated that classification accuracies on the training and testing dataset are 99.04% and 83.50%, respectively. The model performance was also compared to the performance reported in the well-established deep learning algorithms, including Alex Net, Squeeze Net, Google Net, VGG Net 16, and VGG Net 19. It was observed that the DCNN + SGDM model performs better (99.04%) than the existing deep learning models (73.8% in Alex Net, 80.3 in Squeeze Net, 93.33 in Google Net, 93.20% in VGG Net 16, and 93.00 in VGG Net 19). Moreover, the DCNN + SGDM model's performance was compared with the two pixel-based classification algorithms (support vector machine (SVM) and deep neural network (DNN)) in terms of accuracy and processing time. The comparative values of accuracies are 70.40% for SVM, 69.10% for DNN, 99.04% for DCNN on the training dataset, and the respective elapsed time of the SVM, DNN, and DCNN models are 1.716 s, 3.59 s, and 1587 s. That is, the processing time of the DCNN model is high but offers higher accuracy in comparison to the pixel-based classification algorithms (SVM and DNN).

Stability Analysis of Underground Developed Pillars of Opencast Mine Using Three-Dimensional Finite Element Method Authored

The major coal producing companies of the India are running most of the opencast mines to excavate the underground developed pillars as the underground mines possess various strata control issues, subsidence, ensures low production and makes losses. During extraction of such developed pillars, the men and machineries may trap in the developed galleries. Hence, the conditions of the pillars and galleries need to be assessed before approaching the developed pillars. In this study, a case study of Jharia Coal field is chosen for detailed stability analysis of the workings. For this, 3D numerical modelling technique is applied to assess the condition of the underground pillars and galleries. From the study, it is observed that the pillars and galleries of the workings for a maximum of 15 m from the slope surface fails severely. It is therefore, these area requires special precautions while approaching the developed coal seam/workings.

Impact of bedding planes, delay interval and firing orientation on blast induced ground vibration in production blasting with controlling strategies

Traditional techniques of production blasting and delay timing patterns are restructured to minimise ground vibrations for the geological setting of Jharia coal field, a prime coking coal belt with densely covered habitat. Blasting techniques based on delay pattern and bench face orientation are proposed in this paper to protect the structures within a distance of 100 m from active bench face while maintaining daily production and charge per delay. Seventeen single representative blast holes (signature waveforms) were detonated in the last hole at the series of production blasts. When blast vibration was monitored in the opposite direction of the firing sequence and compared to the values in the same direction and behind the bench face, it was observed that the PPVs wereless than 10 mm/s at 55 m radial distance. Also, when blast monitoring is setup near structures, across the strike of rock bed, blasting range can be designed 65 m away from structures rather than along the strike. The signature hole waveforms were generated using electronic delay detonators, and PPV values were controlled by maintaining delay intervals of 25 ms and 67 ms between holes and rows, respectively. Monte Carlo simulation (MCS)was used to generate the probabilistic data, and 5000 simulations helped to evaluate the volatility arising in the investigated blasting techniques, and it was concluded that the modified bench face design technique in 80 % of the cases generates PPV which is lower than the threshold value of 10 mm/s. The contribution to variance (CTV) and correlation coefficient (CC) approaches were used for the sensitivity analysis and it was observed that the bedding plane strike direction and radial distance are the two most critical factors impacting PPV. The proposed equation has shown high coefficient of determination (R2 = 0.85) and low root mean square error (RMSE = 4.42) as compared to other four standard equations. Suggested blasting techniques can be of immense use for conducting production blasting in a safe and productive manner.

Concentration, sources and health effects of silica in ambient respirable dust of Jharia Coalfields Region, India

BackgroundThe concentration of silica in occupational conditions is well defined and estimated around the world. Many countries in the world have developed air standards for occupational conditions based on the percent silica in ambient air. This is due to the pulmonary effect caused by silica yielding diseases like silicosis and pneumoconiosis. In India, occupational exposure to silica dust is regulated by Directorate General of Mine Safety (Tech.) (S&T) Circular No. 1 of 2004 Under Reg. 123 of Coal Mines Regulations, 1957 for any metal/non-metal mining operations estimated gravimetrically. As no silica standards are prescribed in India for non-occupational conditions, venturing into such analysis was well envisaged and perceived.MethodologyAir sampling was done at identified locations through high-volume samplers for 24 h, twice a week in pre-monsoon season (March to June) and the Whatman filter paper was sonicated at sufficient speed to isolate dust particles for energy dispersive X-ray.ResultsThe percentage of silica in “PM10” was found lowest in mining sites (15%), and highest in transportation sites (35%) and mid-value for mixed sites (24%). Thus, risk level gets magnified due to addition of finer dust generated in transportation and mixed sites than mining due to diesel driven vehicles. Burning of any fossil fuel generates high percentage of finer dust (< 2.5 µm).ConclusionsThere should be proper prescribed standard for silica for non-occupational conditions.

Neural network based uncertainty and sensitivity evaluation of electrical resistivity tomography for improved subsurface imaging

Assessment of subsurface status by resistivity technique, being an indirect approach, is pretended to be a strategic factor. Projection of full proof confirmation in this domain is always a challenge and hence outcomes are expressed in possibilities. Intervene of mathematical interpretation on resistivity data generated in the field by different arrays would offer a better choice in building-up the possibility of projecting the actual status. Thus, a study of Wenner-Schlumberger (WS), dipole–dipole (DD) and combined inversion (CI) data of three parallel profiles have been conducted, as a whole, for old and abandoned shallow depth coal mine workings in Jharia coalfield. The study recapitulates influence of sensitivity and uncertainty with depth, apart from resistivity. Statistical significance of the data has been evaluated inclusive of their inter-relationship. PCA presented an encouraging relation of sensitivity with depth. The comprehensible approach of mathematical interpretation helps in cracking a problem of uncertain prediction. Sensitivity and the extent of uncertainty are the parameters to build a strong foundation for evaluating the degree of confidence in prediction accuracy. Artificial Neural Network (ANN) tool has been used to understand the relative importance of sensitivity and uncertainty with depth. The weightage of sensitivity has been observed to be on upper side with respect to uncertainty. The importance of configuration of resistivity survey array has been emphasized based on sensitivity.