Research on the formation mechanism of magnetized water used to wet coal dust based on experiment and simulation investigation on its molecular structures

Construction and application of a novel WGAN-CNN-based predicting approach for dust concentration at underground coal mine working faces

To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at underground coal mine working faces. Dust concentration, wind speed, temperature, and methane concentration were collected as the original data due to their nonlinear relationship. The consistency between the generated and original data distributions was verified through PCA dimensionality reduction analysis. The predictive performance of this approach was assessed using five metrics (R2, EVS, MSE, RMSE, and MAE) and compared with three other algorithms (Random Forest Regressor, MLP Regressor, and LinearSVR). The findings indicate that a majority of the generated data falls within the distribution range of the real dataset, exhibiting reduced levels of volatility and dispersion. The R2 values of prediction results are all above 98%, and the MSE values are between 0.0007 and 0.0106. The proposed approach exhibits superior predictive accuracy and robust model generalization capabilities compared to alternative algorithms, thereby enhancing the real-time monitoring and early-warning level of dust disasters in underground coal mine. This will facilitate the realization of advanced prevention and control measures for dust disasters, showcasing a wide range of potential applications.

Numerical study on temporal and spatial distribution of particulate matter under multi-vehicle working conditions

The high growth in the use of underground diesel vehicles has led to a large number of exhaust pollutants, especially particulate matter (PM), which is a serious threat to the lives and health of underground personnel. In this paper, based on numerical simulations and field measurements, the temporal and spatial distribution of PM in the exhaust of two vehicles and the impact on the health of underground personnel was analyzed. The results showed that in both conditions, the airflow velocity between two vehicles showed a zonal distribution, and there was an airflow vortex in the chamber under the interaction of the wind. When the vehicles were running in the same direction into the wind, PM with a concentration range of 15.79-26.32 mg/m³ could reach the height of the human respiratory belt and was mainly distributed on the east side of the roadway. Therefore, underground personnel should avoid approaching the right area of the vehicle body. In addition, PM concentration around the driver position of the vehicle was still higher than the human contact limit, so the drivers of the vehicle would need personal protection. When the vehicles were running in the same direction with the wind, compared with the airflow inlet side, the amount of PM on the airflow outlet side increased more obviously with time, especially for PM with a concentration range of 21.05-31.58 mg/m³. Also, partial PM flowed into the chamber with the airflow, such that personnel should avoid being located on the downwind side of the vehicle, and personnel in the chamber should also have personal protection.

Research on Coal Dust Wettability Identification Based on GA-BP Model

Aiming at the problems of the influencing factors of coal mine dust wettability not being clear and the identification process being complicated, this study proposed a coal mine dust wettability identification method based on a back propagation (BP) neural network optimized by a genetic algorithm (GA). Firstly, 13 parameters of the physical and chemical properties of coal dust, which affect the wettability of coal dust, were determined, and on this basis, the initial weight and threshold of the BP neural network were optimized by combining the parallelism and robustness of the genetic algorithm, etc., and an adaptive GA−BP model, which could reasonably identify the wettability of coal dust was constructed. The extreme learning machine (ELM) algorithm is a single hidden layer neural network, and the training speed is faster than traditional neural networks. The particle swarm optimization (PSO) algorithm optimizes the weight and threshold of the ELM, so PSO−ELM could also realize the identification of coal dust wettability. The results showed that by comparing the four different models, the accuracy of coal dust wettability identification was ranked as GA−BP > PSO−ELM > ELM > BP. When the maximum iteration times and population size of the PSO algorithm and the GA algorithm were the same, the running time of the different models was also different, and the time consumption was ranked as ELM < BP < PSO−ELM < GA−BP. The GA−BP model had the highest discrimination accuracy for coal mine dust wettability with an accuracy of 96.6%. This study enriched the theory and method of coal mine dust wettability identification and has important significance for the efficient prevention and control of coal mine dust as well as occupational safety and health development.

Experimental study on coal dust wettability strengthened by surface active ionic liquids

The water wettability of coal dust was very important for dust control when using water-based dust suppressant materials. The coal dust wettability strengthened by surface active ionic liquid was studied in this paper. The surface activity of ten ionic liquids with different anions Cl^-, Br^-, [BF₄]^-, [NTf₂]^- and cations [HOEtMIm]⁺, [C_nmim]⁺ (n = 4, 12, 14, 16) was studied by surface tension test. The water wettability of raw coal dust can be improved individually by adding ionic liquid to water or pre-treating coal dust by ionic liquids. The wettability of lignite was improved little, but that of bituminous coal and anthracite were improved much. The dual strengthened effects of ionic liquids on coal dust wettability were studied by the wetting results between ionic liquids solutions and ionic liquid-treated coal samples. The wettability of lignite can be strengthened under the combined action of [HOEtMIm][NTf₂] and [C₁₂MIm]Br, while other dual effects were not satisfactory. All ionic liquids combination had strengthened effects on the wettability of bituminous coal and anthracite, especially the [C₁₂MIm]Br treatment and [C₁₂MIm]Br solutions together had the best dual effects. The functional groups results indicated that the hydrophilic oxygen-containing functional groups in treated coal samples increased, the hydrophobic aliphatic hydrocarbon functional groups decreased and part of ionic liquids were adsorbed on the coal surface. These changes together enhanced the wettability of coal with high coalification degrees.