Coal dust explosions are severe safety accidents in coal mine production,posing significant threats to life and property.Predicting the maximum explosion pressure(Pm)of coal dust using deep learning models can effecti...Coal dust explosions are severe safety accidents in coal mine production,posing significant threats to life and property.Predicting the maximum explosion pressure(Pm)of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions.In this study,a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations(Cdust),resulting in a dataset of 70 experimental groups.Through Spearman correlation analysis and random forest feature selection methods,particle size(D_(10),D_(20),D_(50))and mass concentration(Cdust)were identified as critical feature parameters from the ten initial parameters of the coal dust samples.Based on this,a hybrid Long Short-Term Memory(LSTM)network model incorporating a Multi-Head Attention Mechanism and the Sparrow Search Algorithm(SSA)was proposed to predict the maximum explosion pressure of coal dust.The results demonstrate that the SSA-LSTM-Multi-Head Attention model excels in predicting the maximum explosion pressure of coal dust.The four evaluation metrics indicate that the model achieved a coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute percentage error(MAPE),and mean absolute error(MAE)of 0.9841,0.0030,0.0074,and 0.0049,respectively,in the training set.In the testing set,these values were 0.9743,0.0087,0.0108,and 0.0069,respectively.Compared to artificial neural networks(ANN),random forest(RF),support vector machines(SVM),particle swarm optimized-SVM(PSO-SVM)neural networks,and the traditional single-model LSTM,the SSA-LSTM-Multi-Head Attention model demonstrated superior generalization capability and prediction accuracy.The findings of this study not only advance the application of deep learning in coal dust explosion prediction but also provide robust technical support for the prevention and risk assessment of coal dust explosions.展开更多
Aluminum powder explosion accidents occurred frequently,but the mechanism of aluminum powder explosion is unclear.Therefore,the inhibitive effect of aluminum powder explosion plays a key role.To evaluate the inhibitio...Aluminum powder explosion accidents occurred frequently,but the mechanism of aluminum powder explosion is unclear.Therefore,the inhibitive effect of aluminum powder explosion plays a key role.To evaluate the inhibition capacity of different kinds of carbonates and phosphates:Na H2PO4,(NH4)2HPO4,NH4H2PO4,KHCO3 and Na HCO3 on aluminum deflagrations,a standard 20-L spherical chamber was used to determine the explosion severity,characterized by the maximum explosion pressure(Pmax).New parameters have been proposed:the minimum significant inert concentration(MSIC)and the minimum complete inert concentration(MCIC),which characterized the effect of inert.Experimental results showed that from the minimum significant inert concentration(MSIC)and the minimum complete inert concentration(MCIC),phosphate can have a significant inhibiting effect.40%Na H2PO4 can totally inert the aluminum explosion,and 50%(NH4)2HPO4or 50%NH4H2PO4 can also suppress the explosion.Through simulation,phosphate mainly acts via a chemical inhibition pathway,which inhibits the reaction of aluminum powder and oxygen by catalyzing the recombination of H atoms and O atoms.Carbonate performs inhibition in chemically,producing CO2,diluting the oxygen around the aluminum powder.Studies indicated that the explosion pressure of the mixture decreases as the concentration of inert dust increases.However,when the concentration of carbonates was low,SEEP(suppressant enhanced explosion parameter)phenomenon was found.This research work has a potential industrial application in high hazard aluminum working condition,which can help decrease the explosion pressure and reduce the accident loss.展开更多
The experiment of gas and coal dust explosion propagation in a single lanewaywas carried out in a large experimental roadway that is nearly the same with actual environmentand geometry conditions.In the experiment,the...The experiment of gas and coal dust explosion propagation in a single lanewaywas carried out in a large experimental roadway that is nearly the same with actual environmentand geometry conditions.In the experiment,the time when the gas and coal dustexplosion flame reaches test points has a logarithmic function relation with the test pointdistances.The explosion flame propagation velocity rises rapidly in the foreside of the coaldust segment and comes down after that.The length of the flame area is about 2 timesthat of the original coal dust accumulation area.Shock wave pressure comes down to therock bottom in the coal dust segment,then reaches the maximum peak rapidly and comesdown.The theoretical basis of the research and assemble of across or explosion is suppliedby the experiment conclusion.Compared with gas explosion,the force and destructiondegree of gas and coal dust explosion is much larger.展开更多
基金funded by the Research on Intelligent Mining Geological Model and Ventilation Model for Extremely Thin Coal Seam in Heilongjiang Province,China(2021ZXJ02A03)the Demonstration of Intelligent Mining for Comprehensive Mining Face in Extremely Thin Coal Seam in Heilongjiang Province,China(2021ZXJ02A04)the Natural Science Foundation of Heilongjiang Province,China(LH2024E112).
文摘Coal dust explosions are severe safety accidents in coal mine production,posing significant threats to life and property.Predicting the maximum explosion pressure(Pm)of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions.In this study,a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations(Cdust),resulting in a dataset of 70 experimental groups.Through Spearman correlation analysis and random forest feature selection methods,particle size(D_(10),D_(20),D_(50))and mass concentration(Cdust)were identified as critical feature parameters from the ten initial parameters of the coal dust samples.Based on this,a hybrid Long Short-Term Memory(LSTM)network model incorporating a Multi-Head Attention Mechanism and the Sparrow Search Algorithm(SSA)was proposed to predict the maximum explosion pressure of coal dust.The results demonstrate that the SSA-LSTM-Multi-Head Attention model excels in predicting the maximum explosion pressure of coal dust.The four evaluation metrics indicate that the model achieved a coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute percentage error(MAPE),and mean absolute error(MAE)of 0.9841,0.0030,0.0074,and 0.0049,respectively,in the training set.In the testing set,these values were 0.9743,0.0087,0.0108,and 0.0069,respectively.Compared to artificial neural networks(ANN),random forest(RF),support vector machines(SVM),particle swarm optimized-SVM(PSO-SVM)neural networks,and the traditional single-model LSTM,the SSA-LSTM-Multi-Head Attention model demonstrated superior generalization capability and prediction accuracy.The findings of this study not only advance the application of deep learning in coal dust explosion prediction but also provide robust technical support for the prevention and risk assessment of coal dust explosions.
基金supported by the National Key Research and Development Program of China(No.2018YFC0808600)。
文摘Aluminum powder explosion accidents occurred frequently,but the mechanism of aluminum powder explosion is unclear.Therefore,the inhibitive effect of aluminum powder explosion plays a key role.To evaluate the inhibition capacity of different kinds of carbonates and phosphates:Na H2PO4,(NH4)2HPO4,NH4H2PO4,KHCO3 and Na HCO3 on aluminum deflagrations,a standard 20-L spherical chamber was used to determine the explosion severity,characterized by the maximum explosion pressure(Pmax).New parameters have been proposed:the minimum significant inert concentration(MSIC)and the minimum complete inert concentration(MCIC),which characterized the effect of inert.Experimental results showed that from the minimum significant inert concentration(MSIC)and the minimum complete inert concentration(MCIC),phosphate can have a significant inhibiting effect.40%Na H2PO4 can totally inert the aluminum explosion,and 50%(NH4)2HPO4or 50%NH4H2PO4 can also suppress the explosion.Through simulation,phosphate mainly acts via a chemical inhibition pathway,which inhibits the reaction of aluminum powder and oxygen by catalyzing the recombination of H atoms and O atoms.Carbonate performs inhibition in chemically,producing CO2,diluting the oxygen around the aluminum powder.Studies indicated that the explosion pressure of the mixture decreases as the concentration of inert dust increases.However,when the concentration of carbonates was low,SEEP(suppressant enhanced explosion parameter)phenomenon was found.This research work has a potential industrial application in high hazard aluminum working condition,which can help decrease the explosion pressure and reduce the accident loss.
基金Supported by the National Basic Research Program(973)(2005CB221506)the Open Research Fund Program of Shandong University of Science and Technology(MDPC0611)
文摘The experiment of gas and coal dust explosion propagation in a single lanewaywas carried out in a large experimental roadway that is nearly the same with actual environmentand geometry conditions.In the experiment,the time when the gas and coal dustexplosion flame reaches test points has a logarithmic function relation with the test pointdistances.The explosion flame propagation velocity rises rapidly in the foreside of the coaldust segment and comes down after that.The length of the flame area is about 2 timesthat of the original coal dust accumulation area.Shock wave pressure comes down to therock bottom in the coal dust segment,then reaches the maximum peak rapidly and comesdown.The theoretical basis of the research and assemble of across or explosion is suppliedby the experiment conclusion.Compared with gas explosion,the force and destructiondegree of gas and coal dust explosion is much larger.
