To comprehend the dust pollution characteristics of the respiratory risk zone (RRZ) in the roadway, experiments and numerical simulations were conducted to investigate the effects of ventilation parameters (Air supply vent-to-dust source horizontal distance (L-s), Supply air velocity (V-s), Exhaust-to-supply air volume ratio (R-v)), and the dust emission time (T-e) on the behavior, number, and percentage of dust particle parcels within the RRZ. Regression forest models were also constructed for the number and percentage of particle parcels. The results show that, as T-e increases, the number of total particle parcels in the RRZ under different ventilation parameters initially experiences rapid growth, followed by a gradual decrease in the growth rate until the number reaches saturation. The Hill function effectively describes this pattern. Moreover, the percentage of delicate dust parcels (PM1, PM7, and PM10) exhibites the same trend as T-e increases. Random forest analysis reveals that the order of factor importance affecting the percentage of delicate dust parcels in the RRZ is L-s > V-s > R-v > T-e. Additionally, affecting the number of total particle parcels in the RRZ is L-s > R-v > T-e > V-s. The research can provide a theoretical basis for targeted dust reduction.

Long pressure short extraction ventilation and dust removal method is one of the effective methods for removing high mass concentration dust in the excavation area of coal mine comprehensive heading face. Especially, the local flow field generated by the combination of pressure and suction is conducive to the removal and reduction of respiratory dust. However, the dynamic changes in the location of dust production sources have an unclear impact on the dust removal performance of this method. By considering the movement paths and reciprocating times of the dust source location in both horizontal and vertical directions, four dust source movement paths were designed based on the long pressure short extraction test platform. Combined with the regulation of the parameters of the long pressure short extraction ventilation system, the impact of the dust source on the spatial dust mass concentration, particle size mass concentration, and particle size distribution under different movement conditions was tested and analyzed. The results show that under the same ventilation parameters, the dust mass concentration caused by the horizontal path on the respiratory belt positions of drivers and pedestrians is lower than that caused by the vertical path. In the horizontal path, when the pressure air duct is located on the side near the exhaust duct and the pressure air outlet is located in an area about 1 m in front of the driver, the particle size mass concentrations of PM1, PM2.5 and PM10 at the breathing zone between the driver and pedestrian are the lowest, and the ventilation and dust removal effect is the best. The diffusion of spatial dust is manifested as: particles with a particle size less than 2.5 µm are easily collected and removed by the exhaust flow field at the exhaust port, while the particles with a particle size greater than 10 µm will escape from the dust generation source and exhaust area to the driver and the area behind them, and mainly settle naturally. Based on the optimal dust source movement path and ventilation parameters obtained from preliminary experiments, on-site experiments were conducted on the 2304 fully mechanized heading face of a coal mine in northern Shaanxi. The results show that the total dust mass concentration at the driver's position and pedestrian breathing zone position under the lateral path decreased to 85.6 mg/m3 and 21.9 mg/m3, respectively, with the highest dust reduction rate reaching 76.9%. The mass concentration of respirable dust decreased to 15.3 mg/m3 and 10.5 mg/m3 respectively, with a maximum dust reduction rate of 85.2% and the dust removal performance was significantly improved.

In order to study the problems of unreasonable airflow distribution and serious dust pollution in a heading surface, an experimental platform for forced ventilation and dust removal was built based on the similar principles. Through the similar experiment and numerical simulation, the distribution of airflow field in the roadway and the spatial and temporal evolution of dust pollution under the conditions of forced ventilation were determined. The airflow field in the roadway can be divided into three zones: jet zone, vortex zone and reflux zone. The dust concentration gradually decreases from the head to the rear of the roadway. Under the forced ventilation conditions, there is a unilateral accumulation of dust, with higher dust concentrations away from the ducts. The position of the equipment has an interception effect on the dust. The maximum error between the test value and the simulation result is 12.9%, which verifies the accuracy of the experimental results. The research results can provide theoretical guidance for the application of dust removal technology in coal mine.

In recent years, with the deepening of the degree of coal mining and the improvement of the mechanization and intelligent level of coal dressing, the proportion of fine and micro-fine coal production has been increasing. Micro-fine grade separation has gradually become an important research direction in coal washing industry. This paper is based on a novel jet-stirring synergistic column flotation method, which integrates jet-impact mixing, impeller mixing, and dispersion into the structure of a column flotation tank. By combining with existing research foundations, we construct a corresponding physical model and conduct optimization studies on key parameters related to jetting, impeller mixing, and mechanism. The resulting fundamental theory provides clear insights for the engineering application of this innovative flotation technology. The effects of jet-stirring synergy on bubble adsorption and reagent adsorption were studied, and the flotation effect of the new flotation technology was experimentally verified, laying a theoretical foundation for the industrial application of the device.

How to efficiently realize the separation of coal gangue is a hot issue in coal preparation. In order to solve this problem, this paper proposes research on the identification and separation of minerals in coal gangue based on dual-energy X-rays, and conducts experimental research on four single minerals and several different composite minerals in coal based on the identification technology in the sorting process of the system, and conducts a simulation study on the jet separation simulation for the separation technology in the separation process of the system. Based on the coal gangue photoelectric separation test platform, the main minerals in coal gangue were analyzed, and based on MATLAB software, the gray value of the mineral was obtained, the curve between the gray value and the thickness was made, the mass absorption coefficient of the mineral in the high and low energy regions was obtained by regression analysis, and the R value identification threshold of single mineral and composite mineral was established, which provided a theoretical basis for the accurate identification of coal gangue. The parameters of the jet nozzle were analyzed and optimized through fluid simulation, and the optimal parameters for the rapid separation of coal gangue were determined.

Controlling coal dust pollution is confronted with numerous challenges in the demanding environment of deep mining. Chemical additives, imidazolium ionic liquids (ILs), have been considered an effective strategy to boost the hydrophilicity of coal and reduce dust emissions. This work conducted experiments with molecular dynamics (MD) simulation to study the wetting characteristics of ILs ([Emim][Cl], [Bmim][Cl], and [Hmim][Cl]) with different chain-length on coal. The static wettability tests results indicated that the wettability of ILs was improved as the chain-length increased, and the wettability on bituminous coal followed the rule of water< [Emim][Cl]<[Bmim][Cl]<[Hmim][Cl]. The dynamic dust suppression experiments in the roadway showed that the dust suppression efficiency of 2% [Hmim][Cl] reached 77.5%, 2.45 times that of water. The ESP and frontier orbital energy show that the chain-length increase can enhance the ILs activity. The MD results indicated that short-chain ionic liquid ([Emim][Cl]) tends to accumulate on the surface of coal molecules, whereas long-chain ionic liquid ([Hmim][Cl]) possesses the capability to permeate coal molecules and serves as a tunneling role between coal and water, thereby enhancing the hydrophilicity of bituminous coal. The research results could be used to determine and design ILs with excellent wetting performance for improving the coal dust suppression effect.

It is wise to investigate the influence of ionic liquids (ILs) on reducing hydrophobicity and dust pollution of coal from the perspective of side chain groups. In this study, two ILs ([Emim][Cl] and [HOEmim][Cl]), were selected and applied to bituminous coal. The density functional theory results showed that [HOEmim][Cl] containing the -OH group had an energy gap of 5.2895 eV, which was lower than the 5.3873 eV of [Emim][Cl] containing only the -CH3 group. A higher chemical activity of [HOEmim][Cl] was demonstrated due to the presence of the -OH group. A molecular dynamics-based contact angle system was constructed to investigate the wetting properties of coal samples treated with different ILs at the molecular level. The simulation results indicated that, compared to untreated coal, [HOEmim][Cl] exhibited a greater ability than [Emim][Cl] to improve the wetting properties of coal. Contact angle experiments demonstrated that treatment with [Emim][Cl] and [HOEmim][Cl] resulted in a decrease in contact angle by 22.67 degrees and 25.04 degrees, respectively, compared to deionized water, providing validation for the accuracy of molecular simulations. Lastly, the structural changes of coal samples induced by different ILs were observed by FITR, which explained the differences in wetting effects.

Aiming at the problems of unreasonable air flow distribution and serious dust pollution in roadway under forced ventilation of coal mine in a heading face, this study conducted experimental research based on response surface method, and effectively reduced dust concentration by changing air flow distribution in roadway. Firstly, a single factor experiment was conducted to explore the impacts of air outlet wind speed (Va), distance from air outlet to heading face (LS), and driving height (Hd) on the dust concentration at the position of the driver (Y1) and the height of the pedestrian breathing belt (Y2). Subsequently, a comprehensive air flow control response surface test was designed based on the Box-Behnken principle to optimize the dust field, with Y1 and Y2 as the response index. Finally, the sequence of the impact of each variables on the dust concentration and the optimal air flow scheme were determined through the response surface method. The test results showed that the order of significance of Y1 was Hd > Va > LS, and the order of significance of Y2 was LS > Va > Hd. The maximum error between the test value and the predicted value in the optimization model is only 4.01%, the dust concentration at the driver's position and the pedestrian's breathing belt height decreased by 57.7% and 53.8%, respectively, and the dust-control effect was obvious.

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.

Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands. However, the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction. To address this problem, in this study, we propose an approach based on multi-source data fusion that considers the following indicators: water quality indicators, water quantity indicators, and meteorological indicators. In this study, we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland: (1) multiple linear regression; (2) backpropagation neural network (BPNN); (3) genetic algorithm combined with the BPNN to solve the local minima problem; and (4) long short-term memory (LSTM) neural network to consider the influence of past results on the present. The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method, with a satisfactory R2. Additionally, given the huge fluctuation of different pollutant concentrations in the effluent, we used a moving average method to smooth the original data, which successfully improved the accuracy of traditional neural networks and hybrid neural networks. The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.