Prediction of oxygen concentration and temperature distribution in loose coal based on BP neural network

An effective method for preventing spontaneous combustion of coal stockpiles on the ground is to control the air-flow in loose coal. In order to determine and predict accurately oxygen concentrations and temperatures within coal stockpiles, it is vital to obtain information of self-heating conditions and tendencies of spontaneous coal combustion. For laboratory conditions, we designed our own experimental equipment composed of a control-heating system, a coal column and an oxygen concentration and temperature monitoring system, for simulation of spontaneous combustion of block coal （13-25 mm） covered with fine coal （0-3 mm）. A BP artificial neural network （ANN） with 150 training samples was gradually established over the course of our experiment. Heating time, relative position of measuring points, the ratio of fine coal thickness, artificial density, voidage and activation energy were selected as input variables and oxygen concentration and temperature of coal column as output variables. Then our trained network was applied to predict the trend on the untried experimental data. The results show that the oxygen concentration in the coal column could be reduced below the minimum still able to induce spontaneous combustion of coal - 6% by covering the coal pile with fine coal, which would meet the requirement to prevent spontaneous combustion of coal stockpiles. Based on the prediction of this ANN, the average errors of oxygen concentration and temperature were respectively 0.5% and 7 ℃, which meet actual tolerances. The implementation of the method would provide a practical guide in understanding the course of self-heating and spontaneous combustion of coal stockpiles.

Fractal analysis of thermal conduction of loose coal

For deep mining engineering, heat transfer of coal mass is a vital factor in the thermal environment of coal mines. In order to study the thermal conduction mechanism, we obtained gray images of coal mass microstructure by scanning samples with a digital microscope. With the use of Matlab, these gray images were transformed into binary images, which were then transformed into a corresponding matrix consisting only of the values 0 and 1. According to the calculation method of box-counting dimension, we calculated the fractal dimension of the loose coal to be approximately 1.86. The thermal conductivity expressions of loose coal were derived based on the simulation method of thermal resistance. We calculated the thermal conductivity of loose coal by using a fractal model and compared the calculated values with our experimental data. The results show that the test data show an encourag-ing agreement with the calculated values. Hence fractal theory is a feasible method for studying thermal conductivity of loose coal.

Dynamic mathematical model and numerical calculation method on spontaneous combustion of loose coal

Through the experiment of coal spontaneous combustion and relationship particle size with oxidation character of loose coal, some calculation formula of characteristic parameters is got in the process of coal spontaneous combustion. According to these theories of porous medium hydrodynamics, mass transfer and heat transfer, mathematical models of air leak field, oxygen concentration field and temperature field are set up. Through experimental and theoretical analysis, 3 D dynamic mathematical model of coal spontaneous combustion is set up. The method of ascertaining boundary condition of model is analyzed, and finite difference method is adopted to solve 2 D mathematical model.

Effects of caving–mining ratio on the coal and waste rocks gangue flows and the amount of cyclically caved coal in fully mechanized mining of super-thick coal seams

Aimed at determining the appropriate caving–mining ratio for fully mechanized mining of 20 m thick coal seam, this research investigated the effects of caving–mining ratio on the flow fields of coal and waste rocks, amount of cyclically caved coal and top coal loss by means of numerical modeling. The research was based on the geological conditions of panel 8102 in Tashan coal mine. The results indicated the loose coal and waste rocks formed an elliptical zone around the drawpoint. The ellipse enlarged with decreasing caving–mining ratio. And its long axis inclined to the gob gradually became vertical and facilitating the caving and recovery of top coal. The top coal loss showed a cyclical variation; and the loss cycle was shortened with the decreasing in caving–mining ratio. Moreover, the mean squared error(MSE) of the amount of cyclically caved coal went up with increasing caving–mining ratio, indicating a growing imbalance of amount of cyclically caved coal, which could impede the coordinated mining and caving operations. Finally it was found that a caving–mining ratio of 1:2.51 should be reasonable for the conditions.

Improved mesh-free SPH approach for loose top coal caving modeling

This study presents an innovative model in computational geotechnical engineering by improving the Smoothed Particle Hydrodynamics(SPH)method for simulating loose particle dynamics in coal caving processes.The improved model integrates an elastic-perfectly plastic constitutive model with the Drucker-Prager yield criterion and includes several improvements aimed at boosting accuracy,stability,and efficiency.These improvements include gravity loading coupled with particle damping,first-order stress field smoothing,and kernel gradient correction.A series of numerical experiments validates the effectiveness of the improved SPH model,demonstrating its capability to predict large deformations and track the evolution of the coal-rock interface in coal caving processes.Furthermore,the study analyzes the model's sensitivity to material parameters such as the angle of friction and material density,which aids in configuring the model for distinct coal mining situations.Results show that the non-cohesive elastic-perfectly plastic constitutive model can effectively simulate the flow behavior of granular particles,and the landslide simulation results are in good agreement with the experiments.The improved SPH algorithm with stress smoothing technique solves the problem of numerical noise,and the“double peak”stress distribution around the coal outlet is identified.The established SPH model offers an effective tool for understanding dynamics behaviors of loose top coal.Significantly,the model requires only five material parameters,which can be identified through standard experiments,avoiding the typically arduous process of parameter selection or calibration commonly existing in Discrete Element Method simulations.