Coal Quality Research Articles

Improvement of Brown Coal Quality through Variation of Acacia Wood Waste Biochar Composition in Producing Alternative Solid Fuel

Improvement of Brown Coal Quality through Variation of Acacia Wood Waste Biochar Composition in Producing Alternative Solid Fuel

Analisis Cara Penanganan Penurunan Kualitas Batubara di Stock ROM Ulak Pandan Sampai Jetty SDJ di PT Bumi Merapi Energy

Coal quality analysis is increasingly important considering the increasing global energy demand. Coal, as the main source of energy, has varying qualities, affecting combustion efficiency and environmental impact. Two commonly used analysis methods are proximate analysis and ultimate analysis, which provide information about the composition of coal. Previous research has shown that chemical analysis affects energy efficiency and environmental impact. This study aims to analyze the quality of coal using both methods and evaluate its characteristics for industrial applications. The benefits of this research include guidance for coal producers and users, as well as contributions to reducing the environmental impact and sustainability of the energy sector. This study uses a descriptive approach with qualitative and quantitative methods, focusing on Seam E coal at PT Bukit Asam Tbk. Primary data includes coal handling, temperature, and quality, while secondary data includes location maps and rainfall data as a reference. This study resulted in the conclusion that 1) Factors that affect the decline in coal quality at mining sites and stockpiles include the mining process, working front conditions, loading, processing in the stockpile, sampling, and handling in the ROM to Jetty SDJ. 2) Efforts made include contamination avoidance, fine coal handling, stacking management, and blending process.

Experimental and molecular dynamics study on combustion characteristics of non-stick coal

To investigate the combustion characteristics of non-stick coal and the formation patterns of main combustion products and radicals, this study initially conducted a TG-DSC experiment of non-stick coal. Subsequently, non-stick coal was characterized and analyzed using XPS and 13C NMR experiments, and a molecular model with the formula C208H199O23N3S was constructed. Subsequently, the ReaxFF MD method was employed to simulate the combustion molecular dynamics of the non-stick coal periodic mixing model under varying oxygen content and temperature conditions. The analysis focused on elucidating the formation patterns of free and primary products during combustion. Experimental findings indicate that the combustion of non-stick coal progresses through stages, including water evaporation and desorption (30–145.65 °C), dynamic equilibrium (145.65–181.76 °C), oxygen weight gain (181.76–263.56 °C), thermal decomposition (263.56–379.14 °C), combustion (379.14–562.04 °C) and burnout (562.04–800 °C). The ignition temperature of non-stick coal was determined to be 379.14 °C. The simulation results indicate that an increase in temperature and oxygen content further enhances the relatively stable peak production of CO2. The peak increase of CO production is weaker than that of CO2. The rise in temperature also promotes the formation of H2O, while the increase in oxygen content initially enhances and subsequently inhibits the peak production of H2O. H radicals, O radicals, and OH radicals are primarily generated through the decomposition reactions of small molecular compounds or other free radicals. Free radical polymerization and the decomposition of small molecular compounds represent the primary pathways for the formation of CO, CO2, and H2O.

Research on a coal seam modeling construction method based on improved kriging interpolation

To address the issues of large anomalous triangulations, invalid interpolations, and uneven boundary interpolations in kriging interpolation, we propose research on a coal seam modeling construction method based on improved kriging interpolation. The work methodology assumes that by introducing kriging interpolation and analyzing its problems, we improve the interpolation method via a local interpolation algorithm for large anomalous triangulations, an optimization algorithm for locally redundant interpolation points, and a nonuniform boundary adaptive local interpolation algorithm. These improvements allow the interpolation method to better reflect the variability and realistic nature of coal seams. The research results indicate that applying this method to the construction of the Dananhu No. 2 open-pit mine coal seam model has improved the issue of coal seam transition stiffness, such as abnormal large-area triangulation in areas with significant elevation differences. This approach appropriately reduces the memory space usage without altering the coal seam morphology (which saves approximately 27,000 KB of memory, equivalent to the space occupied by 4 out of 21 coal seams). It has also prevented inaccuracies in boundary line positioning and transitions caused by too low a density of points on the coal seam reserve boundary line, resulting in smoother model transitions at the boundaries that better align with the actual coal seam change trends, the error rate in coal quality estimation decreased by 62.69%. This study provides data support for mining planning and reduces costs. This method can be extended to the construction of all mine models.

Response of nano-pores and heterogeneity of tar-rich coal to microwave pyrolysis

ABSTRACT In-situ pyrolysis of tar-rich coal can alleviate the external dependence of oil and gas in China, and achieve efficient and safe exploitation of tar-rich coal. In fact, the nano-pores and heterogeneity of tar-rich coal undergo significant changes during pyrolysis, but there is currently limited research, which seriously restricts the efficient utilization of tar-rich coal. In this study, the pore and heterogeneity characteristics of tar-rich coal were studied by microwave pyrolysis experiment, gas adsorption results and multifractal theory, and the pyrolysis evolution mechanism of pores of tar-rich coal was proposed. The results show that microwave pyrolysis significantly promoted the development of nano-pores in tar-rich coal. The nano-pore size distribution of tar-rich coal after pyrolysis showed obvious multifractal characteristics, and the influence of sparse area on nano-pore size distribution was more obvious, while the nano-pore distribution heterogeneity increases initially and subsequently drops with temperature. Moreover, the development of nano-pores in tar-rich coal occurred in three stages. Stage I, the total pore volume changed little, increasing only from 0.06851 to 0.09463 ml/g, and thermal cracking is the main reason for pore development. Stage II, numerous pores were produced as a result of the pyrolysis products’ generation, and total pore volume grew rapidly increased to 0.331 ml/g. Stage III, liquid hydrocarbons and precipitated volatile products blocked nano-pores, so that the total PV began to decrease. The total pore volume was only 0.19967 ml/g at 1000°C. This study provides guidance for the investigation of pore evolution during pyrolysis and enriches the theory of in-situ pyrolysis of tar-rich coal.

Study on the evolution rules of coal deformation and failure characteristics caused by multiple mining disturbances

During coal mining operations, the coal will be deformed and damaged due to multiple mining disturbances (MMD), often resulting in disasters, like rock burst. To understand the evolution rules of coal deformation under MMD and its final fracture characteristics after impact dynamic load loading, reduce the adverse effects of mining disturbances, and improve disaster prevention and control capabilities, quasi-static uniaxial cyclic loading-unloading (L-U) and dynamic axial compression tests were conducted on large-sized coal-like samples. During the tests, three-dimensional (3D) laser scanning and acoustic emission (AE) monitoring technology were utilized to accurately capture the full-field deformation and AE response data, facilitating a systematic analysis of deformation and fracture characteristics. The results show that: (1) Under the cyclic L-U effect induced by MMD, each loading cycle causes compression deformation with partial recovery during unloading, presenting an overall “wavy” variation trend. (2) The maximum load is the most critical factor affecting the damaged coal deformation, with smaller load resulting in less overall sample deformation. (3) After the impact dynamic loading, the damaged samples suffered large-scale impact splitting failure, with the compressive-shear layer failure mainly occurred inside the holes. (4) Lower loading during cyclic L-U process correlate with reduced damage degree, and smaller debris particles with a higher fractal dimension when impact failure occurs, indicating a more severe impact failure. (5) With multiple cycles of L-U, the cracks inside the sample gradually extend and expand from around the hole to the outside. The greater the load and the number of cycles, the more serious the crack damage will be. (6) In the practical mining process, it is crucial to reinforce roadway interiors while minimizing low-loading cyclic disturbances induced by MMD. The study has obtained the deformation evolution rules and failure characteristics of coal under MMD, providing a theoretical basis for the prevention and control of corresponding engineering disasters.

Dynamic mechanical characteristics of coal in front of the mining face under different mining layouts

To study the dynamic mechanical characteristics of coal in the area affected by mining in front of the mining face under different mining methods, an improved split Hopkinson pressure bar (SHPB) was used to apply different static loads to different positions. Then, dynamic mechanical tests were conducted on coal at different positions on the mining face, analyzing the dynamic response under strong dynamic load disturbances, under three different mining layouts. Within the range of static water pressure to the peak support pressure, the dynamic strength of coal gradually increases with increasing distance from the mining face. The dynamic strength is the smallest at the peak support pressure stress, and under strong external disturbances, instability and failure are increasingly likely to occur at the peak stress. Under the same loading rate conditions, the dynamic strength of the peak stress in coal is as follows: Protective coal-seam mining (PCM) > Top-coal caving mining (TCM) > Nonpillar mining (NM).

Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload?

The gross calorific value (GCV) of coal is pivotal in shaping policies across various sectors of the Indian economy. It plays a crucial role in classification and valuation of coal and is a major factor in determining electricity tariffs charged by thermal power plants. With coal production escalating year-on-year to meet India's increasing electricity demand, there is significant rise in coal testing activities along the pit-to-power supply chain at multiple points and by multiple testing agencies often driven by sector-specific policy requirements. While laboratory testing accurately determines GCV, it is costly and time-consuming due to the reliance on expensive equipment and skilled personnel. Global researchers have previously devised a plethora of empirical formulae predicting GCV based on its correlations with easy-to-measure properties like moisture and ash content. However, the applicability and utility of these formulae to the prevalent policy matrix of coal and power sector remain to be explored. The introduction of independent third-party assessment of coal quality by Coal India Limited in 2016 has generated a vast dataset of coal sample-test results, offering an opportunity to reassess existing empirical formulae, test their alignment with existing policies, and explore possibility of a unified, region-neutral formula for rapid GCV prediction with a special focus on alleviating the current overload in coal testing.

Combined effect of ball-milled ash and ammonium dihydrogen phosphate on fouling, slagging, and combustion characteristics of high-sodium zhundong coal

Combined effect of ball-milled ash and ammonium dihydrogen phosphate on fouling, slagging, and combustion characteristics of high-sodium zhundong coal

Exploration of Changes in Coal Pore Characteristics and Gas Adsorption Characteristics Based on Influence of Stress

As the mining depth increases, the effect of stress on the gas adsorption of coal gradually becomes significant. There are significant differences in the pore volume, specific surface area, and adsorption characteristics of coal before and after stress. In this study, the porosity variation characteristics of coal were studied using axial and confining pressure loading processes, and volumetric stress was introduced to characterize the pore variation law of coal under triaxial stress. By calculating the stress values at different burial depths, gas isothermal adsorption experiments were conducted on coal under different stress effects. The Langmuir equation, D-A equation, and Freundlich empirical formula were used to fit the adsorption experimental results. Combining experiments and models to predict the adsorbed and free gas content under stress, we described the gas adsorption law of coal under different stress effects.

Research on coal-rock boundary identification based on the morphological sobel algorithm

Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation; to a certain extent, subsequent intelligent mining is limited. A morphological Sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological Sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological Sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the Sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

Study on the Thermal Effect of Spontaneous Combustion of Coal-Based Activated Carbon By-Products Oxidized and Carbonized Powders

ABSTRACT To investigate the spontaneous combustion thermal effect of the by-products oxidized powder (OP) and carbonized powder (CP) produced during the preparation of coal-based activated carbon (CBAC). First, the changes in composition and pore structure of OP and CP compared with raw coal (RC) were measured by coal quality analysis and nitrogen adsorption experiments. The ability of OP and CP to physically adsorb oxygen was determined. Then, differential scanning calorimetry (DSC) was used to study the whole process thermal effect of the spontaneous combustion of OP and CP. Finally, the thermal effect of OP and CP during low-temperature oxidation process was further explored by the C80 micro-calorimeter, and the thermodynamic characteristics were also probed. The results show that the content of fixed carbon and carbon elements in OP and CP are significantly greater than that in RC, which makes the pore structure of both complicated. In addition, the total pore volume, average pore size, and specific surface area of OP and CP are greater than that of RC, which is more conducive to the physical adsorption of oxygen by both. No transition platform is caused by the decomposition of intermediate products on the DSC curve of CP. The characteristic temperatures of CP (except T D10) are lower than that of OP, indicating that the spontaneous combustion reaction of CP started earlier than that of OP. However, part of the combustible substance of CP was consumed in the dry distillation process, leading to the thermal release and maximum thermal release rate being smaller than that of OP. The apparent activation energy (E a) of stages H2 and H3 in the low-temperature oxidation process of OP is 75.04 and 11.42 kJ mol−1, while that of CP is 83.11 and 30.91 kJ mol−1. The threshold of CP entering stages H2 and H3 is higher than that of OP. Nevertheless, from the pre-exponential factor (A), the low-temperature oxidation process of CP is more rapid.

Study on the Optimization of Culture Parameters of Oxygen-Consuming Bacterium for Preventing CSC and Its Effect on the Coal Oxidation Characteristic

ABSTRACT Microorganisms can work together to prevent coal spontaneous combustion (CSC) by consuming oxygen and changing coal oxidation characteristics. In order to explore the influence of oxygen-consuming microorganisms on coal spontaneous combustion characteristics, a highly oxygen-consuming bacterium was isolated and identified from the soil near mine drainage, named ZQ-1. The oxygen consumption characteristics of microorganisms were investigated and optimized by single-factor and response surface experiments. Further, the effect of microorganisms on CSC was analyzed from both macroscopic and microscopic perspectives by using programmed warming, simultaneous thermal analysis, and Fourier-transform infrared spectroscopy (FTIR). The results revealed that the isolated bacterium was Pseudomonas aeruginosa. Its optimal culture conditions were pH = 6.98, temperature 28.15°C, and carbon nitrogen ratio (C/N) = 12.94 when the oxygen consumption rate was maximum. After bacterial action, coal showed a decrease in the release of the indicator gas CO during the combustion process, a delay in the cross point temperature (CPT) by 18.63°C, and an increase in the residual mass at the end of combustion. In addition, the average activation energy of the coal samples in the oxidative combustion stage was increased by 2.05721 kJ/mol. Finally, the FTIR results showed that microorganisms were able to destroy the reactive groups in the coal samples, especially hydroxyl, aliphatic hydrocarbon, and oxygen-containing functional groups, which resulted in the inhibition of CSC. This paper has important theoretical research value and practical significance for CSC prevention and control.

Study on I/III mixed fracture characteristics of coal in different regions of China

The fracture mechanical property of coal rock constitutes an important mechanical constraint for hydraulic fracturing of coal reservoir, and its three-dimensional fracture characteristics hold great significance for coal-bed methane mining. In this study, non-invasive methods were employed to investigate the pore structure and permeability of coal, aiming to understand the gas storage, distribution, and mobility of coal from various regions. To assess the influence of geological stress on coal during coal-bed methane mining, a three-point bending test was conducted to determine the characteristics of coal I/III mixed fractures. The experimental results indicate that with the decrease of the modal mixing parameter Me, the fracture area expends, the fracture load Pf and fracture energy Ef increase, while the fracture toughness declines. The 3D fracture criterion is utilized to predict the toughness ratio of coal rock I/III mixed fracture. The 3D-MTS and 3D-MMTSN criteria offer the upper and lower limits of prediction respectively. The 3D-MTSED criterion proves to be a more appropriate fracture criterion for analyzing the 3D fracture.

Coal-gangue sound recognition using hybrid multi-branch CNN based on attention mechanism fusion in noisy environments

The coal-gangue recognition technology plays an important role in the intelligent realization of fully mechanized caving face and the improvement of coal quality. Although great progress has been made for the coal-gangue recognition in recent years, most of them have not taken into account the impact of the complex environment of top coal caving on recognition performance. Herein, a hybrid multi-branch convolutional neural network (HMBCNN) is proposed for coal-gangue recognition, which based on improved Mel Frequency Cepstral Coefficient (MFCC) as well as Mel spectrogram, and attention mechanism. Firstly, the MFCC and its smooth feature matrix are input into each branch of one-dimensional multi-branch convolutional neural network, and the spliced features are extracted adaptively through multi-head attention mechanism. Secondly, the Mel spectrogram and its first-order derivative are input into each branch of the two-dimensional multi-branch convolutional neural network respectively, and the effective time-frequency information is paid attention to through the soft attention mechanism. Finally, at the decision-making level, the two networks are fused to establish a model for feature fusion and classification, obtaining optimal fusion strategies for different features and networks. A database of sound pressure signals under different signal-to-noise ratios and equipment operations is constructed based on a large amount of data collected in the laboratory and on-site. Comparative experiments and discussions are conducted on this database with advanced algorithms and different neural network structures. The results show that the proposed method achieves higher recognition accuracy and better robustness in noisy environments.

Estimation of gross calorific value of coal based on the cubist regression model.

The gross calorific value (GCV) of coal is an important parameter for evaluating coal quality, and regression analysis methods can be used to predict GCV. In this study, we proposed a GCV prediction model based on cubist regression. To develop a good regression model, feature selection of input variables was performed using a correlation analysis and a recursive feature elimination algorithm. Thus, in this study, we determined three sets of variables as the optimal combination for regression models: proximate analysis variables (Set 1: moisture, standard ash, and volatile matter), element analysis variables (Set 2: carbon, sulfur, and oxygen), and comprehensive index variables (Set 3: carbon, volatile matter, standard ash, sulfur, moisture, and hydrogen). Results for comparison with multiple linear regression, random forest regression, and numerous previous prediction models, such as gradient boosting regression tree, support vector regression (SVR), backpropagation neural networks, and particle swarm optimization-artificial neural network (PSO-ANN), indicate that these seven regression models have the best fitting effect on the comprehensive index variables among the three sets of input variables. The cubist model showed higher prediction accuracy and lower error than most other models (R2, mean absolute error, root mean square error, and average absolute relative deviation percentage values are 0.990, 0.476, 0.668, and 0.086% for the proximate analysis variables; 0.992, 0.381, 0.596, and 0.140% for element analysis variables; and 0.999, 0.161, 0.219, and 0.087% for comprehensive index variables, respectively). The cubist model combines the advantages of decision tree and linear regression, which not only enables it to perform well in terms of accuracy but also makes the model highly interpretable because it is based on multiple sublinear equations. In addition, the cubist model shows obvious advantages in terms of running speed, especially compared with SVR and PSO-ANN, which require complex parameter optimization. In summary, the cubist model considers the prediction accuracy, model interpretability, and computational efficiency as well as provides a new and effective method for GCV prediction.

Chemical Characterization of Coal: Quality Control and Applications of Nuclear Analytical Methods Utilizing Neutrons (Thermal and Fast) and Low Energy Protons from Accelerators

Chemical Characterization of Coal: Quality Control and Applications of Nuclear Analytical Methods Utilizing Neutrons (Thermal and Fast) and Low Energy Protons from Accelerators

Supercritical Methane Adsorption Characteristics and Pore Structure Characteristics of Deep Middle Rank Coal

Abstract In order to accurately analyze the adsorption characteristics, pore structure, and fractal law of deep middle rank coal, high-pressure isothermal adsorption tests based on magnetic levitation high-pressure thermobalance and pore structure tests based on mercury intrusion method were carried out on coal samples collected from typical kilometer level deep wells. The fractal law of sponge model was studied. The results showed that: a. The weight method adsorption test showed “excess adsorption” phenomenon with the increase of equilibrium pressure, and the modified Gibbs surface excess adsorption theory was in line with the Langmuir adsorption model; b. The distribution of macropore volume in deep middle rank coal accounts for a dominant proportion of 57.44% to 62.47%, and the proportion of microporous specific surface area reaches 72.96% to 74.27%, indicating that microporous adsorption capacity is the strongest; c. The pore structure parameters and adsorption constants exhibit strong nonlinear characteristics.

Progressive failure characteristics of coal under uniaxial compression: A comprehensive application of micro-ct and digital volume correlation

Progressive failure characteristics of coal under uniaxial compression: A comprehensive application of micro-ct and digital volume correlation

Study on thermal decomposition and enrichment quality of coal from Mogoin gol deposit in Mongolia

The main purpose of this study is to investigate the thermal stability and the mechanism of thermal decomposition of Mogoin gol coal, the possibility of liquefaction by pyrolysis and thermolysis, and the possibility of enriching by heavy liquids to reduce the mineral content of coal and improve its quality. Under this purpose, The Mogoin gol coal was characterized by proximate and ultimate analysis, thermogravimetry, and investigated its thermal decomposition (thermolysis and pyrolysis). Thermogravimetric analysis was performed using Japanese HITACHI TG/DTA7300 instrument and pyrolysis investigation was carried out at different heating temperatures 200–700 °C with constant heating rate 20 °C/min for 80 min. On the basis of proximate and elemental analysis results, it has been indicated that the Mogoin gol coal is high-rank coking coal. The pyrolysis of Mogoin gol coal was studied by SNOL furnace at different heating temperatures and obtained from pyrolysis products such as hard residue, tar, pyrolytic water, and gas. From pyrolysis, the yield of pyrolysis tar (6.28%) was highest at 700 °C. The experiment of thermal decomposition (thermolysis) was carried out in air closed autoclave at 350–450 °C and using hydrogen donor solvent (tetraline) with different mass ratios of coal and solvent (1:1.75; 1:1.5). In the thermolysis experiment, the yield of liquid product is highest with the coal: solvent ratio of 1:1.5 at 450 °C.