Search

Understanding the molecular structure characteristics of coal at the molecular level is of great significance to realize the rational utilization and efficient conversion of coal. This paper gives insights into the acquisition of characterization parameters of coal molecular microstructure by testing and analyzing the long flame coal from Qinggangping (QGP) Coal Mine through proximate analysis, ultimate analysis, vitrinite reflectance determination, fourier transform infrared Spectroscopy test (FTIR), X-ray photoelectron epectroscopy test (XPS), carbon nuclear magnetic resonance (13C-NMR) and X-ray diffraction (XRD). The results show that benzene rings in the QGP coal are mainly connected in a disubstituted way, accounting for 36.48%. Oxygen atoms mainly exist in the oxygen-containing functional groups such as the ether C–O, C=O and –COO. Aliphatic hydrocarbons in the aliphatic group are mainly of symmetrical -CHx stretching vibration. Hydroxyl groups are mainly composed of OH–OH and OH–O hydrogen bonds, accounting for 29.21% and 21.53%, respectively. Nitrogen atoms exist in the form of C4H5N. The coal molecular is mainly of aromatic carbon structure, where the ratio of bridge aromatic carbon to peripheral carbon is 0.198. There are benzene, naphthalene and anthracene in the coal molecular structure, and the former two chemicals play a dominating role. According to the analysis results, the molecular formula of the QGP coal is finally determined as C205H181O29N3S. On this basis, the two-dimensional and three-dimensional macromolecular models are constructed with the assistance of simulation software. In addition, the 13C-NMR spectra and densities of the constructed molecular models are calculated, which verifies the rationality of the models. The macromolecular structure model of bituminous coal constructed in this study provides a theoretical model basis for the optimal surfactant. [ABSTRACT FROM AUTHOR]

High-temperature coal oxidation generates a substantial amount of CO and CO2, especially CO, rapidly, which is a primary factor leading to casualties. The oxygen concentration is the predominant factor affecting the high-temperature oxidation of coal, particularly in the generation of CO and CO2. Understanding the generation patterns and relationship characteristics of CO and CO2 under various oxygen concentrations is important for predicting high-temperature coal oxidation and ensuring personnel safety. The paper used TG–MS and FTIR to study the formation characteristics of isothermal oxidation of CO and CO2 in long-flame coal under different oxygen concentrations. The results indicate that there exists an optimal threshold for CO production when the oxygen concentration is about 9% during the isothermal temperature oxidation process at 200.00–350.00 °C. When the oxygen concentration is less than 13%, the influence of oxygen on CO2 production is weakened. As the temperature increases, the pattern of CO + CO2 production and CO/CO2 is reversed, with CO + CO2 increasing and CO/CO2 decreasing. The relationship between the total production of CO and CO2 and the loss of coal mass follows the function y = a + bx at the isothermal temperatures of 300.00 °C and 350.00 °C. An increase in oxygen concentration does not alter the linear relationship. In the isothermal oxidation process of coal, carbonyl and carboxyl groups are the key groups for CO and CO2 production. At 200.00 °C and 250.00 °C, carbonyl and carboxyl groups increase, and other groups are being converted to carbonyl and carboxyl groups while decomposing into CO and CO2. At 300.00 °C and 350.00 °C, the relative content of carboxyl groups decreases. The research findings play a fundamental role in coal mine disaster prevention and control. [ABSTRACT FROM AUTHOR]

Coal spontaneous combustion is a common problem faced by many coal mines. Spontaneous combustion in goaf releases a large amount of harmful gases, polluting the environment while causing a large amount of wasted resources, and even endangering the lives of workers. Due to the collapse of the interior of the mining area, it is impossible to measure the internal gas composition directly. In order to more accurately predict the spontaneous combustion state inside the mining airspace, this paper obtains the CO generation law and the main source of the working face through the combination of laboratory experiments and on-site monitoring. The CO concentration prediction model of the return corner is established with CO as the index gas. Finally, the safe concentration and warning concentration of the working face are calculated according to the example, which provides theoretical basis for the prediction of spontaneous combustion of coal. [ABSTRACT FROM AUTHOR]

Spontaneous combustion of oxidized coal was one of the serious hazards during coal mining and utilization. However, spontaneous combustibility of oxidized coal affected by the first oxidation of coal was still unclear. In this paper, the influence of pre-heating with different oxygen concentrations on coal secondary spontaneous combustion behaviors was studied. Temperature-programmed heating experiments were carried out to prepare pre-heated coal samples by heating at the preset temperature of 80 °C in mixture atmosphere of N2 and O2 with different oxygen proportions of 0%, 5%, 10%, 15%, 20%. Pre-heated and raw coals were heated programmatically in an air atmosphere of 120 mL min−1 with a heating rate of 0.33 °C min−1. The gaseous compounds of O2, CO and CO2 were analyzed to identify spontaneous combustion behaviors of pre-heated and raw coals. The results showed that pre-heating at 80 °C promoted oxygen physisorption at low temperatures and lowered oxidability at high temperatures in the coal secondary oxidation process. Pre-heating at 80 °C with higher oxygen content reduced oxidability and exothermicity of coal secondary spontaneous combustion, but had no obvious effect on oxygen physisorption of coal low-temperature oxidation. [ABSTRACT FROM AUTHOR]

Spontaneous combustion of coal leading to mine fire is a major problem in most of the coal mining countries in the world. It causes major loss to the Indian economy. The liability of coal to spontaneous combustion varies from place to place and mainly depends on the coal intrinsic properties and other geo-mining factors. Hence, the prediction of spontaneous combustion susceptibility of coal is of utmost importance for preventing the risk of fire in coal mines and utility sectors. Machine learning tools are pivotal in system improvements in relation to the statistical analysis of experimental results. Wet oxidation potential (WOP) of coal determined in the laboratory is one of the most relied indices used for assessing the spontaneous combustion susceptibility of coal. In this study, multiple linear regression (MLR) and five different machine learning (ML) techniques, such as Support Vector Regression (SVR), Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB) and Extreme Gradient Boost (XGB) algorithms, were used to predict the spontaneous combustion susceptibility (WOP) of coal seams based on the coal intrinsic properties. The results derived from the models were compared with the experimental data. The results indicated excellent prediction accuracy and ease of interpretation of tree-based ensemble algorithms, like Random Forest, Gradient Boosting and Extreme Gradient Boosting. The MLR exhibited the least while XGB demonstrated the highest predictive performance. The developed XGB achieved R2 of 0.9879, RMSE of 4.364 and VAF of 84.28%. In addition, the results of sensitivity analysis showed that the volatile matter is most sensitive to the changes in WOP of coal samples considered in the study. Thus, during spontaneous combustion modelling and simulation, volatile matter can be used as the most influential parameter for assessing the fire risk of the coal samples considered in the study. Further, the partial dependence analysis was done to interpret the complex relationships between the WOP and intrinsic properties of coal. [ABSTRACT FROM AUTHOR]

The early prediction of violent coal-bearing strata failure using effective monitoring is crucial to avoid losses due to catastrophic failures and geological disasters to ensure safe and efficient underground deep coal mining. In this study, the early coal failure precursor was established by researching the application of critical slowing down theory (CSDT) using two infrared radiation (IR) indexes, i.e., variance IR temperature (VIRT) and variance of differential infrared image temperature (VDIIT) under different loading rates. The CSDT parameters: variance and autocorrelation, are evaluated using both indexes in different time window and lag step lengths. The test results revealed that the abrupt and significant fluctuations in variance and autocorrelation for both indexes occurred during rock deformation and before the violent damage. The autocorrelation comparatively shows an obvious reliable fluctuation due to stationarity (show no change in fluctuation before the inflection point), which can be used as a precursor for violent rock failure. It has been shown that the stress level of autocorrelation at the inflection point decreases inversely with the loading rate for both indexes. These stress levels for VIRT are 0.920, 0.890, 0.865, and 0.813 of the σmax under the corresponding loading rates of 0.1, 0.4, 0.7, and 1.0 mm/min, respectively. For VDIIT, at loading rates of 0.1, 0.4, 0.7, and 1.0 mm/min the stress levels are 0.930, 0.892, 0.870, and 0.815 σmax, respectively. Therefore, it has been recommended that the prediction performance of precursory characteristics of IR can be improved by applying the CSDT for an early prediction of rock failure. Highlights: In this study, the early coal failure precursor was established by exploring the application of critical slowing down theory (CSDT) under different loading rates. During different loading rates, the CSDT parameters variance and autocorrelation are evaluated using IR indexes, i.e., variance infrared radiation temperature (VIRT) and variance of differential infrared image temperature (VDIIT) under different window and lag step lengths. The test results revealed that the abrupt and significant fluctuations in variance and autocorrelation for VIRT and VDIIT occurred during rock deformation and violent damage. The autocorrelation comparatively shows a reliable fluctuation due to stationarity, which can be used as a precursor for rock failure. The precursor information based on autocorrelation stress level decreases (about 3.4% for VIRT and 4.2% for VDIIT) with an increase in loading rate. [ABSTRACT FROM AUTHOR]