IR@CIMFR - Central Institute of Mining and Fuel Research (CSIR)
Not a member yet
2618 research outputs found
Sort by
Applicability of Dump Slope Rating Systems in Indian Coal Mines
No full textThe opencast mining method generates a large quantity of waste materials and imposes a challenge of handling them efficiently without causing any danger to the lives of the miners and economics of the mining company. This study primarily focuses on generating and comparing a Dump Slope Rating System introduced by the British Columbia Mine Waste Rock Pile Research Committee (BCMWRPRC, 1991) and a Dump Slope Rating System developed by Hawley and Cunning in 2017. These rating systems considered various regional settings, foundation conditions, stability analysis, material quality, construction method, geometry and performance of dump slope for assigning the rating system to the mine dump slopes. Mine field visits and numerical simulations were carried out to quantify different mining parameters. The rating system developed by Hawley and Cunning (2017) considered 22 parameters which are 11 more than the rating system developed by BCMWRPRC in 1991. A total of three mines were studied for assigning the rating system to the mine dump slopes. Mine A and Mine C are situated in Dhanbad District, Jharkhand, India and Mine B is situated in Nagpur District, Maharashtra, India. According to the rating system developed by BCMWRPRC (1991), Mine A and Mine B fall in the moderate hazard class, and Mine C fall in the low hazard class. According to the rating system developed by Mark Hawley and John Cunning in 2017, Mine A and Mine B fall in the high hazard class, and Mine C fall in the moderate hazard class. This change in hazard class is due to the intense number of factors considered by the rating system developed in 2017. A more detailed investigation of the dump slope is possible by encouraging the number of parameters essential for stabilizing the dump slope of the mine
The effect of hydrogen adsorption on Ti2AlV (110) surface: First-principle density functional theory study
No full textThe titanium alloy material is still the most promising material application in the gas turbine, energy, chemical, and biomedical
industries because of its unique and its exceptional strength-to-weight ratio. However, corrosion and the effects of hydrogen
embrittlement (HE) are still major and critical factors to material failure and restriction in many applications. First-principle density
functional theory was used in the current study to examine the hydrogen adsorption on the surface of Ti2AlV. Adsorption at different
surface sites was used to investigate the effect of hydrogen on the surface of Ti2AlV (110) by calculating adsorption energy, work
function, and charge density distribution. All the adsorption energies were found to be negative, indicating an exothermic process
and spontaneous reaction. More importantly, the effect of Van der Waals forces and dispersion correction was investigated on all
the adsorption sites, with all sites showing the adsorption energies strength o
Utilization of Indian low volatile medium coking coal for preparation of metallurgical coke
No full textCoke is essential to the iron-making process, as the efficient function-
ing of a blast furnace depends on the use of high-quality coke.
However, India has limited reserves of prime coking coal, which is
critical for producing such coke. The challenges associated with
using high-ash Indian coal in coke production have led to increased
reliance on imported coal, thereby escalating production costs and
weakening the global competitiveness of the Indian steel industry.
This dependency further lowers the energy efficiency of coke produc-
tion due to the significant energy required for coal transportation. To
address this limitation, the blending of Indian-origin low-volatile med-
ium coking (LVMC) coal offers a promising approach to reduce depen-
dence on high-grade coal while minimizing the impact on coke
quality. In this study, three different Indian LVMC coals were blended
with prime coking coal, and three distinct coke preparation methods
were evaluated to optimize both blend proportions and processing
techniques. The results indicate that up to 50% LVMC coal can be used
in the blend while still achieving good coke properties, making this
a viable strategy for sustainable coke production in the Indian steel
industry
Geochemical processes and groundwater quality assessment in the Yamuna-Hindon interfluve region of Bagpat district, Western Uttar Pradesh, India
No full textThe present research work aims to understand the geochemistry of groundwater resources of the Yamuna—Hindon interfluve region of Bagpat district, Western Uttar Pradesh, India. The region is a part of Indo-Gangetic belt, one of the world's most fertile and intensely farmed areas. To investigate the geochemical processes governing groundwater quality, a total of 105 groundwater samples were collected during pre-monsoon season and analyzed for various physico-chemical parameters, namely, pH, electrical conductivity (EC), total dissolved solid (TDS), total hardness (TH), turbidity, major anions (HCO3−, SO42−, F−, Cl−, NO3−), cations (Ca2+, Mg2+, Na+, K+) following the methods outlined in the American Public Health Association (APHA). The dissolved heavy metals (Fe, Mn, Zn, Pb, Cu, Cr, Ni, As, Se, Co, Cd and Al) in groundwater were analyzed by ICP-MS following the instrument manual. The analysis results revealed that the groundwater is pre-dominantly neutral to mildly alkaline in nature. The major cation chemistry majorly followed the occurrence pattern of Na+ > Mg2+ > Ca2+ > K+, while for anions it was HCO3− > Cl− > SO42− > NO3− > F−. The data plotted on Piper triangular diagram indicated that Ca2+-Mg2+-HCO3− and Na+-K+-HCO3−-Cl− were major hydrogeochemical facies. Weathering of rock-forming minerals mainly governed the groundwater geochemistry in this region, although part of the cations associated with Cl−, F− and NO3− may originate from anthropogenic sources. TDS, TH, turbidity and F− were identified as the major parameters that violated the prescribed limits for drinking water. Most of the heavy metals were found within the drinking water prescribed limits except for Fe, Mn, Al and Se. Elevated salinity, %Na, and magnesium hazard (MH) at certain sites limit its suitability for agricultural use. The assessment of selected organochlorine and organophosphorus pesticides in five samples indicated presence of lindane, β-endosulfan and DDT isomers in few samples. However, a detailed investigation of possible pesticide contamination in this intensive agriculture area is required before drawing any final conclusions
Separation of coal combustion residue for critical element extraction and other bulk uses.
No full textThe demand for critical and rare earth elements is surging and coal combustion residue could be an alternate source of critical elements. Data on the concentration of critical and rare earth elements (REYs) in different size fractions of fly ash would help in segregation of the ash. This study was conducted with the objective of examining the possibility of separation of coal ash into a size fraction useful for element extraction and the rest for bulk uses like cement, concrete, landfill, roads, embankments, etc. The concentration of critical elements, their partitioning in different size ash particles (>500 to 250 μm. Sequential extraction showed that most of the rare and critical elements are associated with the alumino-silicate matrix. The Al2O3 content of this ash is relatively high (25%), so there is scope for co-extraction of Al along with the rare earth elements. The ash disposal and utilization policy should consider the separation and preservation of the coarse ash fraction (>250 μm) for the extraction of critical and rare earth elements
Experimental study on pore structure evolution of thermally treated shales: implications for CO2 storage in underground thermally treated shale horizons
No full textExtracting gas from unconventional shale reservoirs with low permeability is challenging. To overcome this, hydraulic fracturing (HF) is employed. Despite enhancing shale gas production, HF has drawbacks like groundwater pollution and induced earthquakes. Such issues highlight the need for ongoing exploration of novel shale gas extraction methods such as in situ heating through combustion or pyrolysis to mitigate operational and environmental concerns. In this study, thermally immature shales of contrasting organic richness from Rajmahal Basin of India were heated to different temperatures (pyrolysis at 350, 500 and 650 °C) to assess the temperature protocols necessary for hydrocarbon liberation and investigate the evolution of pore structural facets with implications for CO2 sequestration in underground thermally treated shale horizons. Our results from low-pressure N2 adsorption reveal reduced adsorption capacity in the shale splits treated at 350 and 500 ºC, which can be attributed to structural reworking of the organic matter within the samples leading to formation of complex pore structures that limits the access of nitrogen at low experimental temperatures. Consequently, for both the studied samples BET SSA decreased by ∼58% and 72% at 350 °C, and ∼67% and 68% at 500 °C, whereas average pore diameter increased by ∼45% and 91% at 350 °C, and ∼100% and 94% at 500 °C compared to their untreated counterparts. CO2 adsorption results, unlike N2, revealed a pronounced rise in micropore properties (surface area and volume) at 500 and 650 ºC (∼30%–35% and ∼41%–63%, respectively for both samples), contradicting the N2 adsorption outcomes. Scanning electron microscope (SEM) images complemented the findings, showing pore structures evolving from microcracks to collapsed pores with increasing thermal treatment. Analysis of the SEM images of both samples revealed a notable increase in average pore width (short axis): by ∼4 and 10 times at 350 °C, ∼5 and 12 times at 500 °C, and ∼10 and 28 times at 650 °C compared to the untreated samples. Rock-Eval analysis demonstrated the liberation of almost all pyrolyzable kerogen components in the shales heated to 650 °C. Additionally, the maximum micropore capacity, identified from CO2 gas adsorption analysis, indicated 650 °C as the ideal temperature for in situ conversion and CO2 sequestration. Nevertheless, project viability hinges on assessing other relevant aspects of shale gas development such as geomechanical stability and supercritical CO2 interactions in addition to thermal treatment
Prediction of Ground Vibration at Surface for Ring Blasting in Sublevel Stoping Through Empirical Approach, k-Nearest Neighbor, and Random Forest Model
No full textThe accurate prediction of blast-induced ground vibration due to underground ring blasting is a prominent need for ensuring the safety of structures. Different site-specific empirical equations are available for the prediction of ground vibration. These empirical equations are best suited when the monitoring and blasting locations are present in the same medium. The change in the medium alters the behavior of wave propagation. Hence, existing empirical equations have limitations in peak particle velocity (PPV) prediction when the blasting location is an underground hard rock mine and the monitoring location is ground surface. This is because the underground metal mine comprises different levels having void in the form of excavated stope or paste-filled stope. It is very difficult to predict the magnitude of PPV on the surface in such instances. Therefore, this study has been carried out to predict the PPV at surface due to underground blasting. In this paper, PPV data was recorded at surface for 207-ring blasts. Furthermore, the PPV has also been measured at different underground locations for 47-ring blasts. Different empirical equations along with k-nearest neighbor (KNN) and random forest (RF) model of machine learning technique were developed for the prediction of PPV. Most of the empirical models have higher accuracy in the prediction of PPV at an underground location. This shows that scaled distance-based empirical predictors are best suited when the monitoring and blasting media are the same. However, the empirical models do not predict PPV accurately when the monitoring location is ground surface and the blast is conducted underground. The machine learning models are better suited for PPV prediction in such cases. Based on the analysis performed for the case study site, RF model predicts PPV at surface with the highest accuracy. The coefficient of determination and root mean square error for RF model used for predicting PPV at ground surface are 0.94 and 0.438 mm/s respectively. The RF-based model is also the best suited among all the models for predicting PPV at underground locations as well
A Sustainable Combustion Process for Green Synthesis of TiO2 Nanoparticles: Applications in Photocatalytic Degradation and Electrochemical Sensing for Environmental Remediation
No full textHerein, a sustainable approach to combustion has been presented, utilizing Aloe Vera latex as a reducing agent in synthesizing titanium dioxide (TiO2) nanoparticles. The phase and composition of TiO2 have been confirmed using X-ray diffraction, while the band gap has been determined as 3.17 eV using the Kubelka-Monk plot. The photocatalytic potential of the TiO2 has been explored by assessing their effectiveness in degrading AR-88 dye, demonstrating significant activity at 500 nm. Remarkably, in a span of 120 min of UV radiation, AR-88 dye could reach an impressive photodegradation rate of 98.6 %, rendering the dye nearly colorless. TiO2 NPs have also been investigated as an electrode material in sensing paracetamol and D-glucose by coating them onto a carbon paste electrode and immersing them in a 1 M KOH solution. The cyclic voltammetry experiments reveal that the TiO2 NPs possess excellent electrochemical characteristics, with a calculated proton diffusion coefficient of 5.651×10−4 cm2 s−1. These findings underscore the potential of TiO2 as a highly desirable electrode material with remarkable electrochemical properties. This research opens new avenues for utilizing TiO2 NPs in various electrochemical applications, particularly in the detection of pharmaceutically important compounds, highlighting its versatility
Estimation of slope stability using ensemble-based hybrid machine learning approaches
No full textMining is one of the most daunting occupations gain the sector since it entails risk at any point in the operation. In its operation, the main focus is on slope stability. To avoid slope failures, work should be performed in line with both the regulations and the safety criteria. Slope stability is essential in mining activities owing to slope failure putting productivity and safety at risk. Prediction of slope failure is difficult because of the complexity of traditional engineering techniques. Through study, recent technologies have helped mining companies predict slope problems quickly and effectively. In this current research, an ensemble of machine learning intelligence algorithms was used to estimate and assess the Factor of Safety (FOS). In Ostapal Chromicte Mine, India, 79 experimental and failure slope occurrences were tracked to gather in-the-moment field data. The available data were split into training and testing sets at random to build algorithms. The five influenced factors such as the unit weight, the friction angle, the cohesiveness, the mining depth, as well as the slope angle used as input variables to estimate the FOS. Selected machine learning techniques such as Multiple Linear Regression (MLR), Decision Tree, Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and ensemble hybrid model combining eXtreme Gradient Boosting and Random Forest (XGBoost-RF) were developed to evaluate the FOS. The validity and efficiency of created models can be evaluated using standard evaluation parameters such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). The most precise model to assess the FOS across all models was discovered to be the XGBOOST-RF ensemble model, which had a high R2 of 0.931, MSE of 0.009, NRMSE of 0.069, MAD of 0.037, MAPE of 3.581 and an RMSE of 0.098