Bituminous Coal Sorption Characteristics and Its Modeling of the Main Coal Seam Gas Component in the Huaibei Coalfield, China

Wu, Gang; Ye, Zhiwei; Zhang, Lei; Tang, Jun

doi:10.3390/su15129822

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…By applying the BET equation, the specific surface area of each layer on the isothermal adsorption line can be calculated, and the interaction between the single-layer adsorption amount, V m , and the multi-layer adsorption amount, V m , can be deduced [33]:…”

Section: Calculation Of the Specific Surface Areamentioning

confidence: 99%

Experimental Study on the Microstructural Characterization of Retardation Capacity of Microbial Inhibitors to Spontaneous Lignite Combustion

Wang,

Liu,

Chen

et al. 2023

Fire

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Mine fires are one of the common major disasters in underground mining. In addition to the external fire sources generated by mining equipment and mechanical and electrical equipment during operations, coal is exposed to air during mining, and spontaneous combustion is also the main cause of mine fires. In order to reduce the hidden danger of coal mines caused by spontaneous coal combustion during lignite mining, the microbial inhibition of coal spontaneous combustion is proposed in this paper. Via SEM, pore size analysis, and NMR and FT-IR experiments, the mechanism of coal spontaneous combustion is discussed and revealed. The modification of lignite before and after the addition of retardants is analyzed from the perspective of microstructure, and the change in flame retardancy of the lignite treated with two retardants compared with raw coal is explored. The results show that, compared with raw coal, a large number of calcium carbonate particles are attached to the surface of the coal sample after bioinhibition treatment, and the total pore volume and specific surface area of the coal sample after bioinhibition treatment are decreased by 68.49% and 74.01%, respectively, indicating that bioinhibition can effectively plug the primary pores. The results of NMR and Fourier infrared spectroscopy show that the chemical structure of the coal sample is mainly composed of aromatic carbon, followed by fatty carbon and carbonyl carbon. In addition, the contents of active groups (hydroxyl, carboxyl, and methyl/methylene) in lignite after bioretardation are lower than those in raw coal, and methyl/methylene content is decreased by 96.5%. The comparison shows that the flame-retardant performance of biological retardants is better than that of chemical retardants, which provides an effective solution for the efficient prevention and control of spontaneous combustion disasters in coal mines.

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Section: Calculation Of the Specific Surface Areamentioning

confidence: 99%

Experimental Study on the Microstructural Characterization of Retardation Capacity of Microbial Inhibitors to Spontaneous Lignite Combustion

Wang,

Liu,

Chen

et al. 2023

Fire

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Machine Learning Algorithm to Predict Methane Adsorption Capacity of Coal

Li,

Busch

et al. 2024

Energy Fuels

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Accurately predicting methane adsorption capacity in coal is crucial for assessing coalbed methane resources and ensuring safe extraction. Conventional methane isotherm adsorption experiments often suffer from human error and experimental artifacts, leading to inaccurate and poorly reproducible outcomes. Furthermore, they are time-consuming to conduct, requiring specific and well calibrated experimental equipment. In this paper, a Random Forest (RF) algorithm is introduced to improve the accuracy and reliability of methane adsorption capacity prediction. Approximately 200 sets of experimental data, including parameters such as experimental temperature, equilibrium pressure, moisture, ash content, and volatile matter of coal samples, were collected and analyzed to establish a prediction model based on the RF algorithm. The robustness and reliability of the model were validated using K-Fold cross-validation and hyperparameter optimization. The results indicate that the Random Forest algorithm performs exceptionally well in predicting methane adsorption capacity, with optimal values for mean squared error (MSE) and the coefficient of determination (R 2 ), demonstrating a high correlation between predicted and actual values. Machine learning algorithms are innovatively combined with traditional experimental methods in this study. By training the model using large data sets, issues of error and reproducibility in traditional experiments are addressed, improving experimental efficiency and providing a more reliable method for evaluating coalbed methane resources. To some extent, the method can replace traditional methane isotherm adsorption experiments in coal, improving prediction accuracy and efficiency and demonstrating promising prospects for wide application.

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Experimental study on Microstructural Characterization of Retardation Capacity of Microbial Inhibitors to Lignite Spontaneous Combustion

Wang,

Liu,

Chen

et al. 2023

Preprint

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The spontaneous combustion of coal in goaf is the main cause of mine fire at present. Lignite has become a major hidden danger in mine production safety because of its easy oxidation. In this paper, an innovative strategy was proposed to inhibit the spontaneous combustion of lignite by using the principle of microbial-induced calcium deposition. Based on the optimized culture method, a novel composite inhibitor of Bacillus pasteurelli was prepared. SEM, pore size analysis and FT-IR experiments were carried out simultaneously, and the oxidation properties of lignite before and after inhibition were quantitatively characterized from the perspective of microstructure, and the flame retardant properties of microbial and chemical retardants on coal samples were compared. The results show that a large number of deposited calcium carbonate particles are obviously attached to the surface of lignite after microbial inhibition treatment, which plays a physical oxygen insulation role. At the same time, the total pore volume and specific surface area of the coal sample decreased by 68.49% and 74.01%, respectively, indicating that microbial inhibitors can effectively plug the primary pores of lignite. Based on the peak measurement of 400-4000cm-1 infrared spectrum of coal samples, it is found that the contents of active groups including hydroxyl, carboxyl and methyl/methylene in lignite molecules after microbial inhibition are lower than those in raw coal, especially the methyl/methylene content involved in the initial oxidation reaction decreased by 96.5% compared with the baseline content in raw coal. The results show that the oxidation and self-heating capacity of lignite after microbial inhibition is effectively restrained in the initial spontaneous combustion. The research results of this paper can provide effective solutions for the prevention and control of coal spontaneous combustion risk in mined-out area.

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Bituminous Coal Sorption Characteristics and Its Modeling of the Main Coal Seam Gas Component in the Huaibei Coalfield, China

Cited by 3 publications

References 35 publications

Experimental Study on the Microstructural Characterization of Retardation Capacity of Microbial Inhibitors to Spontaneous Lignite Combustion

Experimental Study on the Microstructural Characterization of Retardation Capacity of Microbial Inhibitors to Spontaneous Lignite Combustion

Machine Learning Algorithm to Predict Methane Adsorption Capacity of Coal

Experimental study on Microstructural Characterization of Retardation Capacity of Microbial Inhibitors to Lignite Spontaneous Combustion

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