Permeability evolution and gas flow in wet coal under non-equilibrium state: Considering both water swelling and process-based gas swelling

Xiao, Zhiyong; Wang, Gang; Wang, Changsheng; Jiang, Yujing; Jiang, Feng; Zheng, Chengcheng

doi:10.1016/j.ijmst.2022.11.012

Cited by 9 publications

(2 citation statements)

References 50 publications

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“…Coal mine gas accidents are a significant concern in the global coal mining safety field, posing a serious threat to both coal production and the safety of workers' lives [1,2]. Coal gas permeability refers to the ability of gas to transmit through a unit area of coal within a unit of time.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network

Wang,

Cui,

et al. 2024

Sensors

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Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing.

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Section: Introductionmentioning

confidence: 99%

Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network

Wang,

Cui,

et al. 2024

Sensors

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“…Wettability refers to the ability of a liquid to spread on a solid surface and interact with it. [1] Various physical and chemical processes, including adsorption, [2][3][4] lubrication, [5][6][7] adhesion, [8,9] dispersion, [10][11][12] and friction, [13][14][15] are closely related to surface wettability. [16] Therefore, understanding and controlling the wettability of material surfaces are of great importance for the development of new materials, the design of new functionalities, and the improvement of existing technologies.…”

Section: Introductionmentioning

confidence: 99%

Advances in the Research of Photo, Electrical, and Magnetic Responsive Smart Superhydrophobic Materials: Synthesis and Potential Applications

Wang,

Qu,

Ren

et al. 2023

Chemistry An Asian Journal

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With the rapid advancement of technology, the wettability of conventional superhydrophobic materials no longer suffice to meet the demands of practical applications. Intelligent responsive superhydrophobic materials have emerged as a highly sought‐after material in various fields. The exceptional superhydrophobicity, reversible wetting, and intelligently controllable characteristics of these materials have led to extensive applications across industries, including industry, agriculture, defense, and medicine. Therefore, the development of intelligent superhydrophobic materials with superior performance, economic practicality, enhanced sensitivity, and controllability assumes utmost importance in advancing technology worldwide. This article provides a summary of the wettability principles of superhydrophobic surfaces and the mechanisms behind intelligent responsive superhydrophobicity. Furthermore, it reviews and analyzes the recent research progress on light, electric, and magnetic responsive superhydrophobic materials, encompassing aspects such as material synthesis, modification, performance, and responses under diverse external stimuli. The article also explores the challenges associated with different types of responsive superhydrophobic materials and the unique application prospects of light, electric, and magnetic responsive superhydrophobic materials. Additionally, it outlines the future directions for the development of intelligent responsive superhydrophobic materials.

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Effects of Various Depressurization Paths on Desorption Deformation and Gas Production of Medium-Rank Coal in Qinshui Basin

Cheng,

Fu,

Kang

et al. 2024

Nat Resour Res

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Permeability evolution and gas flow in wet coal under non-equilibrium state: Considering both water swelling and process-based gas swelling

Cited by 9 publications

References 50 publications

Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network

Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network

Advances in the Research of Photo, Electrical, and Magnetic Responsive Smart Superhydrophobic Materials: Synthesis and Potential Applications

Effects of Various Depressurization Paths on Desorption Deformation and Gas Production of Medium-Rank Coal in Qinshui Basin

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