Indian Coal Research Articles

Flotation and oil agglomeration of high ash LVMC coal after deshaling by jigging: a comparative study using different collectors

ABSTRACT LVMC coal with an ash content of 38.70% was initially crushed to −4.0 to +0.50 mm and deshaled using a laboratory model Batac Jig. The resulting concentrate was further size reduced to −0.50 mm and combined with the original fines obtained during crushing. Froth flotation (of −0.50 mm size) and oil agglomeration (of −0.075 mm size) tests conducted using different collectors at varying dosages revealed that the combination of jigging and froth flotation processes can effectively reduce the ash content down to 26.29%. At this stated ash, an acceptable yield of 54.67% was obtained with the highest Separation Efficiency value of 18.20 when the Mahua oil collector was used. Kinetic studies of batch flotation indicated that both ash and non-ash components of the coal reported to concentrate at the same rates, demonstrated by the relative ratios of rate constants ( K N A K A ) , which was equal to 1.0404. It is concluded in the paper that the collector mahua oil outperformed in both the flotation and oil agglomeration processes. The uniqueness of the paper is the approach of beneficiating the newly graded high-high, high-NGM Indian coal using cost-effective bio-degradable oils after deshaling in a Jig. The characteristic size distribution curve plotted for the agglomerates is defined mathematically.

Numerical Analysis of Biomass Blending with Indian Coal in 210 MW Boiler

Numerical Analysis of Biomass Blending with Indian Coal in 210 MW Boiler

Molecular level structure development of Indian coal using experimental, ML and DFT techniques

The nature of interaction between coal surface and reagent affects fine coal flotation yield. Hence, it is crucial to understand the nature of coal surface to improve flotation yield. Therefore, determination of coal surface at the molecular level is important to optimize the flotation performance. But coal is a complex material, and its molecular presentations depend highly on its geographical origin. In this work, various experimental techniques such as ultimate analysis, proximate analysis, TGA, FTIR and solid state 13C NMR were used to characterize the structure of low-rank Indian coal. The data extracted from these experiments were then used to develop molecular level presentation of coal using machine learning (ML) and DFT. Five stable molecular level structures were proposed for this Indian coal. DFT calculated FTIR spectra matches reasonably with the experimental FTIR data. Finally, a 3D model of the coal sample was developed, and reactive force field (ReaxFF) molecular dynamics simulations were performed for thermal decomposition analysis.

Quantitative Analysis of Functional Groups in Different Metamorphic Grade Indian Coals using FTIR technique

To enhance the clean and economical feasible utilization of coal and reduce the coal or rock dynamic disaster, one should have an in-depth knowledge of coal macromolecular structures. In view of this, the chemical and physical structural properties of low to high-rank six Indian coals using Fourier transform infrared spectroscopy (FTIR) are presented. Four absorbance bands were identified in the subtracted spectrum for the functional group’s semiquantitative analysis in coal: hydroxyl, aliphatic, oxygen-containing, and aromatic structures associated with absorbance band at 3000–3600 cm-1, 2800–3000 cm-1, 1000–1800 cm-1, and 700–900 cm-1 respectively. Spectrum deconvolution technique was used to determine the FTIR structural parameters of coal samples. To investigate the structural transformation during coal metamorphism, correlations with Vitrinite reflectance (Ro) were formed. The present study will help to predict the conversion of organic groups to valuable liquids and gases from those samples. The macromolecular structure of coal could be useful to design the synthesis methods for carbon-based materials.

Machine learning approach for the prediction of mining-induced stress in underground mines to mitigate ground control disasters and accidents

The bord and pillar method is commonly employed in Indian underground coal mines, and the extraction rate varies between 30 and 65%. During pillar extraction, pillars are subjected to severe stress conditions. Due to this, the natural state of stress equilibrium is disturbed, resulting in severe strata control problems leading to sudden, unpredictable failure such as a premature collapse of pillars, severe roof or side fall, and sometimes leading to serious/fatal injury or burial of machinery. This paper deals with the prediction of mining-induced stress during pillar extraction using Machine Learning (ML) techniques like Random Forest and Multilayer Perceptron. The various factors used for the formulation of the models for predicting the mining-induced stresses are Depth of the workings (H), Panel width/length (W/L), Pillar width/working height (w/h), Goaf length, and Area of extraction. This paper highlights the importance of operational parameters rather than geological parameters. The Correlation coefficient (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}) of mining-induced stresses for the case studies discussed in the paper is 0.85 for Random Forest and 0.76 for Multilayer Perceptron, which shows Random Forest results have a comparative edge over Multilayer perceptron. With this developed prediction models, the stress conditions of pillars can be predicted.Graphic abstract

Characterizing pulverized coal combustion for high-ash content Indian coal

ABSTRACT The present study performs three-dimensional (3D) numerical simulations of coal combustion of four samples with varying ash content in a drop tube furnace (DTF) to mimic the particle heating rates observed in industrial furnaces. The numerical framework adopted is validated with prior experimental and simulation results using a 3D cylindrical geometry and a very good match is obtained for the axial temperature distribution and particle burnout rate. The combustion performance of coal samples with varying ash contents of 2.3, 16.6, 24.1, and 37.2 wt%, is explored through temperature profiles, burnout rates, particle tracking, and mass fractions of various products of combustion. A user-defined function is used to specify the 14 species, 10-step solid particle combustion with a modified eddy dissipation concept model for volatile combustion. The range of ash content considered is for Indian coal which has a high degree of heterogeneity, making the evaluation of its combustion performance, a challenge. The present work aims to provide benchmark set of results showing the evolution of the evaporated, charred, and volatile mass for the three ash contents, which has not been attempted before. It is found that the high-ash content Indian coal samples have a tendency to produce less . A reduction in the is obtained for the coal sample with ash compared to that with ash. The results suggest that the combustion performance is enhanced when the ash content falls within an optimum range of .

Parametric optimisation and performance evaluation of gasifier-CI engine on dual fuel and dual feed material gasification

Gasification-based energy derived is one of the appealing resources for producing heat and power generation. The objective of this paper is to conduct an experimental study on downdraft air co-gasification of Madhuca longifolia wood and Indian low-grade coal (50:50 wt %) to generate producer gas and its application in dual-fuelled mode CI engine. Besides, the optimisation using the response surface methodology (RSM) has been applied to Gasifier-Engine to determine the best engine performance and emission response by optimising the input variables, i.e. gasifier equivalence ratio, engine compression ratio, and engine load. The RSM-based analysis predicted that optimum input operating parameters could be attained for co-gasification for Equivalence ratio (ER), compression ratio (CR), and engine load (EL) at 0.43, 16, and 100%, respectively. Based on optimal input variables, the performance response values of the gasifier-engine are 29.52% Brake thermal efficiency (BTE), 3.30 kW Brake power (BP), 0.3851 kg/kWh Brake specific fuel consumption (BSFC), 0.0186 vol% CO, 0.958 ppm UHC, 0.9768 vol % CO2, and 109.415 ppm NOx. The results show that the projected regression model has a high accuracy of R2 > 90% and a high correlation coefficient of 0.8927.

Gross Calorific Value of Indian Coals and its Correlation with Ash Content

The Indian coals are a sub-bituminous variety, high in ash content and low in calorific value. The high ash content in Indian coals makes Indian coals more heterogeneous. The moisture and the ash content have a direct impact on the calorific value of the coals. In the present work the moisture, ash content and GCV have been analyzed for the coals obtained from various locations in India and the relationship between ash content and GCV was established on a dry basis. The variation in GCV to ash content was statistically quantified for the coals from each mine and overall mines.

Flow characteristics of microwave treated Indian coal: A deep learning modelling

To meet the escalating energy needs of the growing population, the power stations are gradually shifting towards abundant low-quality coals and lignites. This study proposes a sustainable methodology to utilize such low-quality coals by cutting down the coal pretreatment costs. Instead of utilizing the treated coal as a whole, this study proposes to blend the treated coal with the untreated coal for further utilization. These blended samples are then used to prepare coal water slurry, with concentrations varying in the range of 10 to 60% by mass. The rheology of the blended samples shows a reduction in viscosity by ∼ 23% when the blended sample contains 50% treated coal. The sustainability of the process is verified by a trained artificial neural network, that includes an extensive validation of predicted results with experimental data and Irvine (1988) model. The artificial neural network indicates a maximum of 11.5% reduction in the headloss when the blended sample is transported at 50% solid concentration. The specific enthalpy consumption is found to be the minimum for the blended samples transported at 50% solid concentration.

Synergistic effect of coal and biomass gasification and organo-inorganic elemental impact on gasification performance and product gas

The current study demonstrates the synergistic effect of co-gasification of high-ash Indian coal and biomass (press mud and sawdust) blends in varying proportions in a pilot-scale bubbling fluidized bed gasifier (BFBG) with a capacity of 20 kg/h. The effect of blending on product gas yield, composition and heating value, carbon conversion, and cold gas efficiency was critically analyzed. Mineralogical transformations and catalytic effects were investigated during co-gasification, wherein minerals in ash residue, such as hematite, sylvite, chlorite, K-feldspar, CaO, and MgO, showed a catalytic role in co-gasification, increasing the product gas compositions, and reverse the trend for kaolinite and phosphate minerals. Blending coal with biomass enhances the porosity and reactivity of blends, surface morphology, gasification performance, and product gas yield. So, high ash coal and biomass are suitable for gasification. The high ash coal gasification comprises the volumetric product gas composition (%) of CO (9.79), CO2 (8.75), H2(13.6), and CH4(1.12) with the maximum blends (40%) of biomass, the volumetric composition (%) is enhanced comprising CO (17.71), CO2 (14.8), H2(19.9) and CH4(2.05) for press mud blend, and CO(15.84), CO2(14.2), H2(18.3) and CH4(2.29) for sawdust blend, respectively, and HHV increases from 3.42 (MJ/Nm3) to 5.59(MJ/Nm3).

Extraction of rare earths from coal and coal by-products as a non-traditional source

Rare earth elements (REEs) are one of the critical metals used for modern technological applications and green technologies. Due to growing demand and limited availability, there is a huge supply risk associated with REEs. The primary resources of rare earths in India are limited and associated with many processing challenges. In this scenario, it is important and timely to explore the non-traditional sources of rare earths to meet the domestic demand of country in the future. India is rich in coal deposits. The studies are very limited and there is scarcity of the research work carried out on the extraction of rare earths from Indian coal and coal by-products. Therefore, the objective of the present research article is to highlight the presence of REEs in Indian coal along with coal by-products. The objective is to identify the sweet pots of rare earths in coal and coal by-products. The concentration of total REEs is in the range of 150–200 ppm in coal samples. Furthermore, coal ash contains the concentration of total REEs which is more than 600 ppm in the samples of study area. It is aimed to review the several methods to recover rare earths that can be economically applied for the extraction of rare earths from coal and its by-products. Column leaching experiments achieve a 50% recovery rate of REEs from coal, while thermal treatment of coal enhances the recovery rate by one and a half times compared to untreated samples. Process optimization experiments demonstrate the superiority of the flocculator jar test apparatus, resulting in a significant increase in REE recovery (52% from coal samples). It was found that there is a huge work is still needed to be done for the development of an energy efficient and economical recovery of rare earths from coal and its by-products.

Mineralogical variations and geochemical distribution of rare earths and Yttrium (REY) in coal and overburden rocks of some Indian coal mines of Gondwana coalfield

Mineralogical variations and geochemical distribution of rare earths and Yttrium (REY) in coal and overburden rocks of some Indian coal mines of Gondwana coalfield

A comparative evaluation of co-gasification of blends of low grade and washed coals with high-rank coal in a fixed bed reactor

ABSTRACT Coal is the most valuable gasification resource due to its abundance and low cost for commercial applications. But utilization of low-rank Indian coals for gasification is challenging due to difficulty in washing. For utilizing low-grade high ash raw coal, blending is one of the effective methods to control the gasification performances. So, the present study explores the gasification of such a low-rank raw coal blend and compares the results with washed coal blend. The blending effects on raw gas yield (kg/kg of coal) and heating value (Kcal/Nm3) were investigated for coal blends-WH (washed coal with high-rank coal) and RH (raw coal with high-rank coal). In WH, raw gas yield is 1.210 to 1.225 (kg/kg of coal), whereas, in RH is 1.189 to 1.213 (kg/kg of coal), though the fixed carbon content of WH (40.75–41.74%) is lower than RH (44.73–45.25%). In washed coal, raw gas yield is minimum (0.75 kg/kg of coal). The heating value is lower in WH (2730–2871 Kcal/Nm3) than in RH (2884–2978 Kcal/Nm3). Statistical correlation is developed between gaseous components of raw gas and heating value; H2/CO ratio is the most significant parameter. RH gasification is economically viable than WH.

Exploring the potential of sulphur forms in Northeastern Indian coals: Implications in environmental remediation and heavy metal sensing

The Sampar Coalfield in Northeastern India is a source of plentiful coal reserves, which are burnt for energy production and industrial applications, resulting in the release of pollutants such as sulphur , arsenic, and lead, which are hazardous to the environment and public health. In this work, samples from the Sampar coalfield have been analyzed to understand the origin, distribution, and various forms of sulphur and their ability to detect toxic heavy metals. The total sulphur concentration ranged from 4.31% to 6%, with organic sulphur being the predominant form at 69.21%, followed by pyritic sulphur at 16.49% and sulphate sulphur at 14.28%. With high sulphur content, this coal indicates a marine influence in the peat-forming swamps. The samples have also been examined for petrographic and elemental analysis, which have revealed the presence of vitrinite, liptinite, inertinite, carbon, hydrogen, nitrogen, oxygen, and mineral matter. In addition, the same coal sample has also been used for electrochemical sensing-based detection of toxic heavy metals like arsenic and lead, and the findings indicate an improved efficacy. These results are expected to have significant implications in the development of effective bio-based remediation strategies in the region to mitigate the harmful effects of coal-related pollution.

Variation of δ15N in Indian coal, lignite and peat

Nitrogen (N) stable isotope ratio (δ15N) in coal organic matter (OM) provides information on the N source and dominant mechanisms affecting isotopic fractionation during coalification. However, published data on δ15N distribution in coal is rare. The present study is one of the first reports on the δ15N composition of peat, lignite, sub-bituminous and anthracite coals in India and one of the first attempts to understand the processes influencing δ15N composition at different stages of coalification from peat to anthracite. Peats were collected from the western coast of North Andaman Islands and Lake Loktak in Manipur. Plant samples were collected from the peat sampling locations. Cenozoic lignites were collected from Panandhro, Matanomadh, Umarsar and Tadkeshwar mines in Gujarat and Neyveli in Tamil Nadu. Cenozoic sub-bituminous and Permian anthracite coals were collected from Assam and Sikkim, respectively. Variation of δ15N in plants is attributed to the differences in rainfall, plant type and N sources. Lower δ15N values in peats (mean 1.19) compared to the plant samples (mean 2.77) indicate a nonlinear response of δ15N to the relative enrichment or loss of N during peat formation in Lake Loktak and decomposition of OM under anaerobic conditions leading to selective preservation of 14N in the Andaman Islands. The δ15N composition of the studied peat (−1.4–1.6), lignite (−1.4–1.8) and coals (−2.8–5.0) retains their OM source signature. Overall higher δ15N values of Cenozoic lignites compared to the Cenozoic sub-bituminous coal reflects regional differences in climate. Higher δ15N (1.3–5.0) values in Gondwana anthracites reflects the tectonic influence of Himalayan orogeny.

Desilication of high-silica Indian coal fly ash by alkali leaching with KOH and NaOH: A comparative study

ABSTRACT Indian coal fly ash generally comprises 50–60% Si O 2 , and 25–35% A l 2 O 3 can potentially replace bauxite as an aluminum resource. Aluminum recovery from coal fly ash is highly dependent on removing amorphous silica, activating the inactive mullite phase, and increasing the Al / Si ratio. This work thoroughly investigated and compared the alkali-desilication operation by two widely used alkalis, KOH and NaOH , based on four operational parameters: liquid-to-solid ratio, KOH or NaOH concentration, leaching time and temperature. Using SEM, EDX, and XRD, the morphology and phase of fly ash and solid byproducts were analyzed. NaOH was more effective than KOH in Si O 2 dissolution at all operational parameters. A 20 wt% concentration, 5:1 liquid-to-solid ratio, 2hrs leaching time, and 100°C temperature were the optimum removal conditions for silica from fly ash using NaOH . With KOH , the ideal desilication conditions were obtained at 28 wt% concentration, 3hrs leaching time, and 150°C temperature. The highest Al / Si ratios were obtained under the same optimal desilication conditions. Using KOH and NaOH , the Al / Si ratio was enhanced from 0.58 to 1.26 and 1.45, respectively. The main zeolitic products obtained were Hydroxysodalite in NaOH -treated fly ash and Linde F zeolite and Kalsilite in KOH -treated fly ash.

A Critical Assessment of Sustainable Development in the Indian Coal Mining Sector: A Review

Abstract India’s coal production has ramped up over the years in response to rising demand from the country’s booming energy market and other industries. We’ll inevitably have to rely heavily on the energy industry. About 66% of India’s energy needs are met by the country’s thermal power plants, with 53% of that total coming from coal. India relies on the coal industry despite the fact that it causes serious difficulties for the environment, pollutes water supplies, and degrades land. In the next few decades, India would be completely depleted of its coal deposits if the current output rate of extraction is maintained. In this work, we discuss how to make long-term use of non-renewable resources possible for future generations.

Biogeochemical Controls on Methane Generation: A Review on Indian Coal Resources

Coal bed methane (CBM) extraction has astounding effects on the global energy budget. Since the earliest discoveries of CBM, this natural gas form has witnessed ever-increasing demands from the core sectors of the economy. CBM is an unconventional source of energy occurring naturally within coal beds. The multiphase CBM generation during coal evolution commences with microbial diagenesis of the sedimentary organic matter during peatification, followed by early to mature thermogenic kerogen decomposition and post-coalification occurrences. Indeed, the origin of the CBM and, moreover, its economically valuable retention within coal seams is a function of various parameters. Several noticeable knowledge gaps include the controls of coal make-up and its physico-chemical position on the CBM generation and genetic link through fossil molecular and stable isotopic integration with the parent coal during its evolution. Therefore, this manuscript reviews the origin of CBM; the influences of coal properties and micropetrographic entities on CBM generation and storage; and its genetic molecular and stable isotope compositions in India and the world’s major coal reservoirs. Moreover, analyses of and outlooks on future development trends in the exploration, production, and application of coalbed methane are also addressed. Finally, as India has the fifth largest proven coal reserves, this brief review of the recent CBM discoveries and developments provides a plausible scope for microbially enhanced CBM production from these basins.

IOT Powered Smart Strata Monitoring System for early and precise detection of Convergence in Underground Coal Mines

Roof fall, bed separation and convergence are one of the serious life-threatening problems faced by the miners in all the underground coal mines since early mining age to current mining situations. Most of the statistics regarding fatal accidents and trapped miners in underground coal mines are the results of sudden roof fall due to bed separation of overlying strata in the underground mine galleries or roadways. In Indian underground coal mines, many of fatalities were seen over the years although this rate has been gradually decreasing since past decade only by implementing some of physical and mechanical methods to detect or monitor strata convergence. With the advancement of technology, sensors become an extensive part of everyday life. Various researchers are trying to monitor, detect and prevent fatalities due to roof fall in real time scenario in underground coal mines with the use of sensors, IOT and other technology. The main aim is to Install a non-invasive laser proximity sensor in the hole drilled into the roof where the necessity arises to remotely monitor the convergence in the strata under bad roof conditions in underground coal mines and monitor them remotely with an automated display showing the details of the bed separation, roof convergence and detailed information about the roof condition remotely. Key Word: Internet of Things, Strata Monitoring, Roof Fall, Convergence, Real time, Display device, Proximity sensors, Remote Monitoring.

Pore size estimation of indian coal through low-pressure N2 adsorption

The Low-pressure N2 sorption method was used to evaluate the effect of particle size on the characteristics of the pore structure of the coal sample. Crushing led to a change in the pore structure. New pores were also developed while pulverizing the coal. Samples with 250 µm size showed the maximum specific surface area and micro- and mesopore volume. The quantity of low-pressure N2 adsorption was maximum for 250 µm particle size. Mesopore constituted most of the pore systems in the tested coal samples. However, if the analysis is performed with a smaller particle size, there might be an error of overestimating the gas reserves in actual coal seam conditions. Hence, in this study, 250 µm is suggested to better estimate the pore characteristics.