Coal Type Research Articles

Effect of coal type on aerosol optical properties at Ulaanbaatar city

In this study, seasonal characteristics of aerosol optical properties at Ulaanbaatar station of the international network of SKYNET are investigated using ground-based skyradiometer from the years extending from 2017 to 2023. The aerosol optical thickness (AOT) at 500nm wavelength and Angstrom exponent (AE) were determined in winter to evaluate the decrease in air pollution in Ulaanbaatar since implementing the government's decision to use coal briquettes for household heating. During the study period, the monthly mean AOT at 500nm varied throughout the year, with the maximum value of 0.178±0.004 obtained in winter due to households burning large amounts of biomass, and the highest AOT (0.183) was obtained in February. The mean AOT was 0.21 in the winter of 2017-2019, which decreased by 15 per cent to 0.18 in the winter of 2019-2023.

Research on the Detection Principle of Coal Ash by X-Ray Transmission Based on FLUKA

This study addresses the timely and accurate measurement of coal ash content by proposing a detection model based on nuclear science technology, which is validated using FLUKA 4-4.0 simulation software. The background provided highlights the fact that coal ash content is a critical sales indicator, and its precise measurement is essential for adjusting production parameters in coal preparation plants. In terms of methodology, this study employs the widely used FLUKA4-4.0 software in the field of nuclear physics to simulate X-ray transmission through coal, investigating the impact of changes in coal type on the accuracy of ash measurements. The results indicate that, when the proportions of high-atomic-number elements in coal remain constant, the ash measurement results are accurate and reliable. However, significant fluctuations occur when these proportions change. The conclusion emphasizes the fact that variations in coal type are the primary cause of inaccuracies in ash measurement, particularly when the ratios of high-atomic-number elements are altered. This research provides a new perspective on the online measurement of coal ash content and offers theoretical support for improving measurement accuracy.

Identifying the antecedents for the green transformation of resource-exhausted cities in China

To address the issue of green transformation in resource-exhausted cities, this study employs the Policy Modeling Consistency (PMC) index model to carry out a comprehensive analysis of the effectiveness of support policies execution at both central and local levels, covering 16 policies at the different administrative levels. Meanwhile, utilizing the fuzzy-set Qualitative Comparative Analysis (fsQCA) method, the study investigates the interaction mechanisms and effects of various internal and external factors in the context of policy coordination, from the perspective of green transformation condition configurations. The findings reveal that the overall effect of supporting policies for resource-exhausted cities in China is generally commendable. Moreover, there is a notable characteristic that “the lower the administrative level, the higher the quality of the support policies”. Notably, organizational, technological, environmental, and policy conditions alone cannot solely facilitate the green transition of resource-exhausted cities. Instead, it is the harmonious configuration of these antecedents that drives successful green transformation. Additionally, the configuration of antecedent conditions for green transformation exhibits the heterogeneity of different types. Specifically, the eastern region primarily relies on technological innovation and production process improvement, while the central region focuses on both production process improvement and support policy combinations. By contrast, the western region primarily emphasizes support policy combinations and the fostering of environmental conditions. Furthermore, the green transformation of resource-exhausted cities with coal and oil types mainly depends on the combination of policy support and technological innovation. Metal, forestry, and other types of resource-exhausted cities rely on the upgrading and advancement of industrial structures. In the grouped analysis of administrative level, innovation capability, and distance to the provincial capital city, cities with higher administrative levels, higher innovation capabilities, and closer proximity to the provincial capital city are more likely to achieve green transformation. Thereby, this study emphasizes the critical role of support policies and the identification of effective pathways in promoting the green transformation of resource-exhausted cities.

Effects of indigenous microorganisms on the CO2 adsorption capacity of coal

The adsorption capacity of coal is a crucial criterion for CO2 sequestration in deep unminable coal seams. Previous studies have documented how indigenous microorganisms interact with supercritical carbon dioxide (ScCO2) in coal. But how these interactions affect the adsorption capacity were rarely investigated. In this study, we treated bituminous coal samples with ScCO2-H2O and ScCO2-H2O-microorganism interactions in high-pressure reactors for 120 days at 35 °C and 10 MPa, respectively. After the treatments, we characterized both coal pore types and structures. The comparisons between two different post-treatments indicate that no significant change in coal pore types but notable alterations in pore structures are observed. Specifically, the treated coal with microorganisms exhibits a significant increase in micropore specific surface area (SSA) and surface roughness than the one without them. However, changes in meso/macropore structures and overall pore complexity of the coal sample treated with microorganisms were less pronounced than in coal sample treated with ScCO2-H2O alone. FTIR spectra analysis indicated that the absorption peak intensity of oxygen-containing groups and aliphatic functional groups in the sample treated with microorganisms decreased much more significantly than the sample treated without microorganisms. The CO2 adsorption capacity slightly increased in all treated samples at equilibrium pressures below 2.5 MPa. Above this pressure, the coal sample treated with microorganisms maintained a higher CO2 adsorption capacity, while the sample treated without microorganisms exhibited lower CO2 adsorption capacity compared to untreated coal sample. The increased SSA and the reduced oxygen-containing functional groups of coal after the treatment exerted antagonistic effects on CO2 adsorption. These findings suggest that indigenous microorganisms in coal seams affect CO2 adsorption through antagonistic effects, necessitating site-specific microbial assessments.

Wind speed effect on infrared-image-based coal and gangue recognition with liquid intervention in LTCC

The commitment to achieving carbon neutrality by 2060 makes the clean transition of fossil energy production inevitable in China. As the primary fossil energy source, China produced approximately 4.66 billion tons of raw coal in 2023. Among them, in the fields of mining engineering and environmental science, the accurate recognition of gangue (one of the environmental pollutants produced by coal combustion is black-gray solid waste) from coal is crucial because it is a key step in reducing resource waste and environmental pollution. Hence, coal and gangue automatic recognition with high accuracy is very important for intelligent longwall top coal caving (LTCC) mining. A novel approach of “liquid intervention + infrared detection” is introduced to enhance the recognition accuracy when the exterior color of coal and gangue is similar. This study focuses on the infrared image recognition of coal and gangue after liquid intervention under varying wind speed conditions, and optimizes the image processing path. The effect of varying wind speed conditions on the recognition accuracy of coal and gangue after liquid intervention was analyzed, and the optimal wind speed range was determined. The morphological evolution characteristics of droplets under varying wind field conditions were studied by COMSOL two-phase flow interface. The results show that with the increase of wind speed, the surface contour of coal is gradually clear, and the overall recognition accuracy is also improved. When the high wind speed is 1.55 m/s, the recognition accuracy reaches 99.89%, and the recognition accuracy of other wind speed regions is also above 90% (within 7s). In addition, the numerical simulation can maintain the volume fraction of the droplet in a short time under the constant flow wind flow field, thus inhibiting the phenomenon that the recognition accuracy decreases with time. The simulation results are in good agreement with the physical experimental phenomena, which proves the reliability of the numerical simulation. This study is of great significance to the recognition of coal gangue types with low identification characteristics in LTCC, so as to ensure the primary economic benefits, ecological environment safety and sustainable development of coal mines.

Preparation of coal-based carbon quantum dots and their fluorescence properties

In the field of life sciences, cellular movements play a significant role in influencing the activities of organisms. Current methodologies have limitations in the monitoring of cell recognition, biological macromolecule localization, cell labeling, migration, and biosensors. Although metal–semiconductor quantum dots are commonly used, they may pose toxicity risks to the lungs and kidneys. On the other hand, carbon quantum dots possess the unique optoelectronic properties of traditional quantum dots while exhibiting low toxicity, good biocompatibility, excellent photostability, and abundant raw material sources. In this research, carbon quantum dots were synthesized using the nitric acid oxidation method. The study investigated the effects of temperature, time, and the type of low-rank coal on the synthesis of carbon quantum dots. The structure and properties of the carbon quantum dots, specifically their fluorescence characteristics, were thoroughly analyzed. The proposed synthesis approach includes the nitration of coal macromolecules and the thickening of aromatic clusters through the Scholl reaction, leading to enhanced fluorescence quantum efficiency. The findings of this study indicate that the carbon quantum dots produced through this method demonstrate a high fluorescence quantum yield, making them ideal fluorescent agents for material preparation and offering potential applications in the field of life sciences.

Microwave-induced alterations in the structure of coals at different metamorphic stages

Despite the global trend toward decarbonization, coal continues to play a significant role in energy production, as current renewable energy sources cannot fully meet the increasing global energy demand. The main objective of decarbonization is to reduce greenhouse gas emissions, especially carbon dioxide. Enhancing coal usage efficiency can help decrease these emissions. Electromagnetic activation of bituminous coal is already employed for dehydration, ash removal, desulfurization, and improving the grinding process of raw coal. This research aims to study the specifics of coal electromagnetic activation using microwave radiation and its effects on the physical and chemical processes occurring in coals at different metamorphic stages. The results suggest that this electromagnetic treatment leads to the destruction of hydroxyl groups and the formation of free hydrogen. Coals at higher metamorphic stages heat up faster due to their higher structural density. Coals at lower metamorphic stages take longer to heat up and require more processing time to be effective, while lower power levels are necessary to avoid mechanical breakdown. Activation time and coal particle size are significant factors; their optimal combination provides the maximum effect. These findings contribute to optimizing microwave treatment parameters for different coal types, potentially enhancing coal utilization efficiency and reducing environmental impact.

Optimization of Low-Calorific Coal Application at Different Loads in 600 MW Supercritical Thermal Power Plant with the PROMETHEE-GAIA Method

This study examined the 600 MW supercritical unit of a 1200 MW imported coal-fired thermal power plant in Çanakkale, Türkiye. Coal blends consisting of low-calorific domestic coal (4087 kcal/kg) and high-calorific imported coal (5954 kcal/kg) were combusted at the single mill and burner level to analyze unit parameters at different loads. Initially, input parameters, levels affecting unit parameters, and output parameters influenced by different coal types were identified and prioritized. Using criteria weights determined by the entropy method, the optimal load and domestic-imported coal blend ratio were determined using the Preference Ranking Organization Method for Enrichment Evaluation-Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) multicriteria decision-making method. The optimization study concluded that a 450 MW load with a 14.6% domestic coal feed rate is the most suitable alternative.

Damage Status and Failure Precursors of Different Coal Impact Types Based on Comprehensive Monitoring of Infrared Radiation and Acoustic Emission

Different coal failure impact types exhibit different damage statuses and failure modes, resulting in distinct signal characteristics of infrared radiation (IR) and acoustic emission (AE). This paper combines IR and AE monitoring methods to innovatively establish coal damage and failure precursor warning models and obtains the IR and AE precursor characteristics for different coal failure impact types. This research shows that there is a good correspondence between IR and AE timing and spatial distribution of different coal impact types. As the impact tendency increases, the intensity of IR and AE signals increases with coal failure, and the AE positioning points and IR high-temperature areas tend to concentrate. The coal body gradually changes from tensile failure to shear failure. The shear cracks in the failure stage of coal with no, weak, and strong impact are 39.9%, 50.9%, and 53.7%, respectively. The IR and AE instability precursor point of coal with no, weak, and strong impact occurred at 55.2%, 66.3%, and 93.4% of coal failure, respectively. After the IR and AE combined instability precursor point, the dissipated energy and combined damage variable increase rapidly, and the coal body will undergo instability and failure. The research results provide a theoretical basis for comprehensive monitoring of coal body failure and rock burst.

Multi-scale quantitative characterization of three-dimensional pores and fissures in deep coal and study of the evolution laws

Native pores and fissure in deep coal bodies directly influence the deformation and destruction of coal seams. The pore structure characteristics of coal tend to be complicated because of the damage caused by tectonic stress; hence, it is necessary to study the development of pore fracture in coal. In this study, two types of coal samples—primary structural coal and tectonically deformed coal—were selected from the 12403 comprehensive mining face in the Shangwan Coal Mine, part of the Shendong Coal Mine Group. The developmental characteristics, distribution patterns, and morphological and structural differences of pores and fissures in these types of structural coal were examined using X-ray diffraction, scanning electron microscopy, mercury intrusion porosimetry, and computed tomography scanning, combined with digital image processing technology. A fractal model for the evolutionary characteristics of the pore-fracture network in a coal body is proposed. The characteristics of the pore-fracture network under different measurement accuracies are also investigated. The results showed that the primary structural coal tended to develop small fissures of simple morphologies, with poor fissure connectivity and connection strength. In contrast, the morphologies of fissures in tectonically deformed coal were complex, with better fissure connectivity and connection strength, and a higher fissure rate than that of primary structural coal. A fractal model was developed to describe and characterize the geometrical features of the pore-fracture network at various measurement accuracies. The dominant pore-size segments that can be characterized are 50 nm ≤ r ≤ 10 μm and r > 10 μm, respectively. The fractal dimensions of porosity and pore-fracture network volume increase as measurement scales decrease. The accuracy and applicability of the fractal model in predicting the porosity and volume fractal dimension of the pore-fracture network across various scales were confirmed.

The mechanism and prevention of rockburst induced by the instability of the composite hard-roof coal structure and roof fractures

Despite the implementation of prevention and control measures, rockburst still occur. The characteristics of composite coal were analyzed by combining monitoring and early warning means. Two types of composite coal in the hard roof were distinguished based on their distinct initiation and destruction processes despite experiencing the same rockburst event. Rockburst caused by structural instability was studied in different mining stages, and the following conclusions were considered. 1) The mechanical model of the instability of composite coal in hard roof was established for different mining stages to reveal rockburst mechanisms. The high-speed advance of working faces changed the fractured structure of upper hard rocks in the pressure-relief protection range. The sudden fracture of long cantilever structures caused dynamic load disturbance to high-stress coal, which resulted in rockburst in the insufficient mining stage. The failure and deformation of coal under high-stress static loads created conditions for the instability and fractures of roof. The disturbance of low hard rock strata aggravated the deformations of damaged coal. Therefore, rockburst appeared in the full mining stage. 2) Structural instability and roof fracture caused by different factors were the main causes of rockburst for composite coal in hard roof in different mining stages. Relevant prevention and control measures were formulated to ensure the safety of working faces, which provided references for the prevention and control of rockburst in working faces under similar conditions.

Response Patterns of Acoustic Wave Characteristics to Reservoir Petrophysical Parameters in Different Bedding Directions: A Case Study of Low- and Middle-Rank Coals in Xinjiang, China.

The physical properties of coal reservoirs, important parameters for evaluating the production potential of coalbed methane (CBM) resources, can be assessed nondestructively and in real-time using acoustic wave technology. In this study, we collected 48 low- and middle-rank coal samples oriented in different bedding directions from seven typical coal mines, encompassing the Zhunan, Tuha, and Kuqa-Bay coalfields in Xinjiang, China. We clarified the characteristics of the physical parameters (apparent density, fracture, porosity, and permeability) and acoustic wave of coal variations through acoustic wave, porosity, and permeability experiments, revealing the response law of acoustic wave characteristics to the physical parameters of coal. The results indicated that the acoustic wave velocity and dynamic elastic modulus (E d) of coal samples oriented in the perpendicular bedding direction are larger than those oriented in the parallel bedding direction; however, the dynamic Poisson's ratio (μd) of coal samples oriented in different bedding directions does not significantly differ. The existence of fractures significantly reduces the acoustic wave velocity and E d of the coal. The greater the apparent density of coal, the tighter its structure, resulting in a faster acoustic wave velocity. The larger the porosity of coal, the greater its internal voids, leading to a more pronounced attenuation of acoustic energy and a slower acoustic wave velocity. The more developed and interconnected the bedding fractures of coal bodies oriented in the parallel bedding direction, the higher their permeability, resulting in a smaller decrease in acoustic wave velocity. Conversely, the more developed the bedding fractures of coal bodies oriented in the perpendicular bedding direction, the more pronounced their attenuation of acoustic wave velocity. Finally, the regression equations for E d with the square of P-wave velocity (V P 2) and μd with the square ratio of V P to S-wave velocity (V P 2/V S 2) were established for coal. The study findings can help evaluate and predict the reservoir quality of coal seams, assess CBM, and improve the safety and efficiency of its extraction.

An investigation of CO generation path upon loading coal from perspective of macromolecular simulation.

CO is a hazardous and pollutant gas that can be produced in many scenarios of coal-related operations. The study mainly investigated CO production process and mechanism when coal is subject to external forces. The effects of coal type, particle size, temperature, and inlet atmosphere on CO production from coal body fragmentation were investigated through coal loading experiments. Materials Studio software was used to carry out coal macromolecular mechanics simulation and molecular dynamics simulation, and the gas production mechanism of coal under loading was explored at the molecular level. It was found that under air atmosphere, the low degree of deterioration, small particle size, and elevated temperature are all more likely to cause coal samples to fragment and decompose to produce CO. The carbonyl group in the molecular structure of coal is shed or broken free radical fragments react with oxygen which may lead to CO formation.

Comparative experimental study on the co-firing characteristics of water electrolysis gas (HHO) and lean coal/lignite with different injection modes in a one-dimensional furnace

Zero-carbon fuels (H2/NH3) replacing coal co-firing is a promising way to address carbon emissions. Hydrogen co-firing helps improve combustion stability at low loads and control pollutant emissions. This study used a safer water electrolysis gas (HHO) for the multi-coal co-firing adaptability test. The effects of HHO injection mode and flow rate on combustion intensity, flue gas emissions, and fly ash characteristics were investigated. The results showed that HHO and lean coal/lignite co-firing could significantly increase the combustion temperature. The maximum temperature increases after co-firing were 108 °C and 95 °C. As the HHO increased, the CO2 in lignite co-firing increased, and CO and unburnt carbon decreased, while the opposite results were observed for lean coal (LC). At the 2200L/h HHO co-firing, CO2 decreases by 14.46 % and 27.22 % for LC premixed co-firing (PC) and staged co-firing (SC). Compared to SC, PC is more conducive to promoting the complete combustion of coke. The NOX emissions of HHO/LC co-firing are lower than those of pure coal, reaching the lowest value of ∼ 9 ppm under SC. As the HHO increases, the NOX of Lignite-PC first increases and then slowly decreases, while Lignite-SC has the opposite effect. Although various NOX emission trends are observed under different conditions, the results are dominated by a common mechanism − the competition between NOX reduction caused by HHO gas and the increase in co-firing temperature on NOX generation. The NOX of SC is all lower than that of PC. The SO2 emission from the LC co-firing decreases with the increase of HHO, while the SO2 from lignite increases. The SO2 of LC-SC can be reduced from ∼ 142 ppm to 0 ppm. HHO gas injection inhibits the release and oxidation of sulfur in lean coal. The differences in coal types lead to different changes in SO2 of co-firing. HHO gas reduced the SO3 content of lignite fly ash by 38.01 %. The high-temperature reduction atmosphere promotes the decomposition of sulfates and increases SO2 emissions. HHO co-firing refines the particle size of fly ash. The ultra-fine and micron particle matter (PM) increases, and the content of PM>100μm in Lignite-SC decreased by 10.32 %.

Prediction of coal and gas outburst hazard using kernel principal component analysis and an enhanced extreme learning machine approach

In order to enhance the accuracy and efficiency of coal and gas outburst disaster risk prediction, a novel approach combining kernel principal component analysis (KPCA) with an improved whale optimization algorithm (IWOA) optimized extreme learning machine (ELM) is proposed for precise forecasting of coal and gas outburst disasters in mines. Firstly, based on the influencing factors of coal and gas outburst disasters, nine coupling indexes are selected, including gas pressure, geological structure, initial velocity of gas emission, and coal structure type. The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27 software, followed by extraction of the principal components of the original data through kernel principal component analysis (KPCA). The whale optimization algorithm (WOA) was enhanced by incorporating adaptive weight, variable helix position update, and optimal neighborhood disturbance to augment its performance. The improved Whale Optimization Algorithm (IWOA) is subsequently employed to optimize the weight ф of the Extreme Learning Machine (ELM) input layer and the threshold g of the hidden layer, thereby enhancing its predictive accuracy and mitigating to some extent the issue of "over-fitting" associated with ELM. The principal components extracted by KPCA were utilized as input, while the outburst risk grade served as output. Subsequently, a comparative analysis was conducted between these results and those obtained from WOA-SVC, PSO-BPNN, and SSA-RF models. The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters, with results consistent with actual situations. Compared to other models tested, the model's performance showed an increase in Ac by 0.2, 0.3, and 0.2 respectively; P increased by 0.15, 0.2167, and 0.1333 respectively; R increased by 0.25, 0.3, and 0.2333 respectively; F1-Score increased by 0.2031, 0.2607, and 0.1864 respectively; Kappa coefficient k increased by 0.3226, 0.4762 and 0.3175, respectively. The practicality and stability of the IWOA-ELM model were verified through its application in a Shanxi Province coal mine where predicted values matched actual values exactly. This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.

Classification of coal gangue and identification of coal type based on first-derivative of mid-infrared spectrum

Efficiently sorting coal gangue and identifying coal types are vital operations in coal preparation, yet they are traditionally resource-consuming, labor-intensive, and potentially hazardous. This work puts forward an straightforward method employing mid-infrared spectroscopy with first derivative spectrum to address these issues. The proposed technique focuses on the delineation and enhancement of characteristic spectra to detect subtle differences among samples. The method utilizes just a few characteristic spectra of 3740–3700 cm−1, 1790–1750 cm−1, 1615–1583 cm−1, 1580–1540 cm−1, 1550–1440 cm−1, 1270–1210 cm−1 and 867–854 cm−1 to achieve 100 % high-accuracy classification of coal gangue and identification of coal types with total 250 spectra, such as bituminite, anthracite, lignite, roof sandstone and gangue, without the need for secondary sample processing or the assistance of machine learning algorithms, simplifying the process considerably. Such a strategy not only significantly improves the efficiency of coal sorting but also endorses real-time on-site detection. It offers a theoretical foundation for advanced coal separation technology and its implementation in real-world mining operations.

Competitive sorption of CH₄ and CO₂ on coals: Implications for carbon geo-storage

Geological carbon storage, particularly within coal seams, is recognized as a viable strategy for achieving net-zero emissions. However, following CO₂ injection into the coal seam, limited studies have addressed the competitive sorption dynamics of CH₄ and CO₂ on coal, despite the natural presence of CH₄ in these formations. In this work, sorption experiments were conducted using two types of coal: sub-bituminous coal and anthracite. Initially, pure CH4 and CO2 gases were employed to conduct individual sorption tests. Subsequently, the competitive sorption of CH4 and CO2 was evaluated using pre-mixed binary gas mixtures with varying CH4/CO2 ratios. Further, the displacement effect of CO2 on CH4 was investigated by injecting CO2 into coal samples that had been pre-adsorbed with CH4. The data reveal that, for both coals, the ideal selectivity calculated from pure gas measurements underestimates the corresponding values from real multicomponent systems. Anthracite demonstrates a higher selectivity for CO₂ over CH₄ during both adsorption and desorption processes when compared to the ideal selectivity calculated from pure gas sorption data. Conversely, the sub-bituminous coal initially shows lower selectivity for CO₂ than for CH₄ during adsorption, but this trend reverses and intensifies during desorption. If competitive sorption effects are neglected, coal selectivity for CO₂ over CH₄ under the examined conditions would be underestimated by a factor of 1.5 to 2.5. Due to the competitive sorption effects between CH₄ and CO₂, the Langmuir equilibrium constants for gas mixtures are influenced by compositional changes, leading to dynamic deviations from those observed under pure gas conditions. This finding contrasts with the traditional extended Langmuir model, which presumes that the equilibrium constants remain unchanged regardless of gas composition. In addition, the displacement study underscores the efficacy of CO2 in displacing CH4, with higher CO2 ratios intensifying displacement effects. The study highlights the substantial impact of real multicomponent scenarios on coal selectivity for CO₂ and CH₄, offering deeper insights into predicting competitive sorption behavior between CO₂ and CH₄ during CO₂-enhanced coalbed methane recovery and carbon storage processes.

Dynamic evolution of particulate and gaseous emissions for typical residential coal combustion

Residential coal combustion still accounts for half of the heating energy consumption in many developing countries. The dynamic variation during the combustion process importantly determines the combustion facility design and appropriate air quality assessment, which was omitted in conventional studies. This study investigated the emissions of particulate and gaseous pollutants during the combustion process for typical coal types using online monitoring. During the first pyrolysis stage with temperature climbing, the organic aerosols (OA) and gases reached peak concentration. The second fierce combustion stage had the highest temperature and produced the highest cumulative emissions, particularly a substantial amount of black carbon for coals with higher volatile content. Using higher-quality coals will undoubtedly reduce PM emissions, by a factor of 10 from bituminous to anthracite coal. However, more ultrafine particles (d < 0.1 μm) from cleaner coal may pose additional health risks. Anthracite and honeycomb coal had approximately twice the energy content and emitted more CO2 per unit mass of fuel and had more persistent SO2 emissions throughout the burnout stage. The oxygenation of OA and organic gases remained increased during combustion, suggesting the pyrolysis products underwent oxidation before being emitted. The investigation of the coal combustion process suggests the importance of reducing volatiles to control PM emissions, but the potential negative synergistic effects between PM reduction and increased carbon emissions should also be considered.

Exploring the analytical potential of total reflection X-ray fluorescence (TXRF) combined with partial least square (PLS) for simple determination of ash content in various coal types

Ash content, as a crucial indicator of coal quality, its rapid and accurate determination is pivotal to improve the energy utilization of coal and reduce environmental pollution. Traditional spectroscopic methods face significant challenges in acquiring accurate information from coal samples due to the notorious matrix effects arising from their complex composition, vast molecular structure, and diverse coal types. In this study, the feasibility of total reflection X-ray fluorescence (TXRF) combined with partial least squares (PLS) for the determination of coal ash was firstly investigated based on the TXRF being unaffected by matrix effects. Firstly, coal samples were prepared as suspensions, and the effects of sample particle size and different dispersants on the results of TXRF analyses were evaluated. The accuracy and applicability of the chosen sample preparation strategies were further validated using inductively coupled plasma mass spectrometry (ICP-MS) and two certified reference materials (CRMs). Subsequently, based on the analysis of 19 coal samples, the impact of three different predictive variables on the performance of the PLS model was investigated: (a) TXRF full spectrum normalized by the net intensity of the internal standard; (b) net intensity of characteristic peaks for 12 elements (Al, Si, K, Ca, Ti, Fe, Cr, Mn, Cu, Ni, and Sr) normalized by the net intensity of the internal standard; (c) concentrations of the aforementioned 12 elements. The results demonstrate that the PLS model constructed usingthe TXRF full spectrum normalized by the net intensity of the internal standard exhibits the best predictive capabilities, with the determination coefficient of calibration set (R2) and mean square error (MSE) of the prediction set reaching 0.9736 and 0.99 %, respectively. Moreover, the measurement accuracy of this model was six times greater than that obtained with traditional X-ray fluorescence (XRF). Presented analytical results display the possibilities of combining TXRF with PLS for coal quality evaluation.

Experimental Study and Application Prospects of Coal Dust/Methane/Oxygen Pulse Detonation Performance: Coal Dust Equivalence Ratio and Types.

In order to achieve reliable application of coal in pulse detonation combustion chambers, this study explored the influence mechanism of equivalence ratio and coal type (lignite, anthracite) on the detonation characteristics of coal/methane/oxygen methane-solid two-phase fuel. A high-energy ignition device was used to ignite the preblast methane fuel, and the high-temperature and high-pressure energy generated directly initiated the detonation of coal/methane/oxygen methane-solid two-phase fuel. The experimental results are as follows: (1) The maximum detonation pressure of coal/methane/air fuel is positively correlated with the initial pressure. With the increase of the φglobal, the maximum detonation pressure of the two types of coal gradually increases. To be specific, when the φglobal rises from 0.5 to 1.6, the sensitivity of lignite and anthracite to the maximum detonation pressure with respect to the initial pressure increases from 4.56 to 6.11 and 5.48, respectively; (2) In terms of detonation flame and maximum pressure wave velocity, anthracite shows better dynamic performance than lignite. The maximum velocity of detonation flame and pressure wave is achieved when the φglobal is 1.4. (3) In terms of detonation thrust performance, as the φglobal increases from 0.5 to 1.6, the rate of increase in average thrust of the two types of coal first increases and then decreases. Compared with lignite, anthracite has a wider time domain and higher peak value in the average thrust curve and the generated detonation wave displays a greater impulse. The experimental data and conclusions obtained in this study are of practical significance for realizing the engineering application of pulverized coal detonator power generation.