As mines become deeper,the potential for coal and gas outbursts in deep rock cross-cut coal uncovering is enhanced.The outburst precursors are unclear,which restricts the effectiveness and reliability of warning syste...As mines become deeper,the potential for coal and gas outbursts in deep rock cross-cut coal uncovering is enhanced.The outburst precursors are unclear,which restricts the effectiveness and reliability of warning systems.To reveal the evolution characteristics of coal and gas outburst precursor information in deep rock cross-cut coal uncovering,briquette specimens are constructed and experiments are conducted using a self-developed true triaxial outburst test system.Using acoustic emission monitoring technology,the dynamic failure of coal is monitored,and variations in the root mean square(RMS)of the acoustic emissions allow the effective cracking time and effective cracking gas pressure to be defined.These characteristics are obviously different in deep and shallow coal.The characteristic parameters of gas outburst exhibit stepwise variations at different depths.The RMS and cumulative RMS have stepped failure characteristics with respect to changes in gas pressure.The characteristic parameters of coal failure are negatively correlated with the average in-situ stress and effective stress,but positively correlated with the lateral pressure coefficient of in-situ stress and the critical gas pressure.The transition characteristics are highly sensitive in all cases.The critical depth between deep and shallow coal and gas outbursts is 1700 m.The expansion multiple of acoustic emission intensity from the microfracture stage to the sharp-fracture stage of coal is defined as the outburst risk index,N1.For depths of 1100–1700 m,N1≥7 denotes a higher risk of outburst,whereas at depths of 1700–2500 m,N1≥3 indicates enhanced risk.展开更多
For a low permeability single coal seam prone to gas outbursts, pre-drainage of gas is difficult and inefficient, seriously restricting the safety and efficiency of production. Radical measures of increasing gas extra...For a low permeability single coal seam prone to gas outbursts, pre-drainage of gas is difficult and inefficient, seriously restricting the safety and efficiency of production. Radical measures of increasing gas extraction efficiency are pressure relief and infrared antireflection. We have analyzed the effect of mining conditions and the regularity of mine pressure distribution in front of the working face of a major coal mine of the Jiaozuo Industrial （Group） Co. as our test area, studied the width of the depressurization zone in slice mining and analyzed gas efficiency and fast drainage in the advanced stress relaxation zone. On that basis, we further investigated and practiced the exploitation technology of shallow drilling, fan dril- ling and grid shape drilling at the working face. Practice and our results show that the stress relaxation zone is the ideal region for quick and efficient extraction of gas. By means of an integrated extraction technology, the amount of gas emitted into the zone was greatly reduced, while the risk of dangerous outbursts of coal and gas was lowered markedly. This exploration provides a new way to control for gas in working faces of coal mines with low permeability and risk of gas outbursts of single coal seams in the Jiaozuo mining area.展开更多
Currently,coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results.Owing to this,an artificial immune algorithm(AIA)was developed for coal and gas outburst prediction b...Currently,coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results.Owing to this,an artificial immune algorithm(AIA)was developed for coal and gas outburst prediction based on the Hamming distance(HD)calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm(HDAIA).The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm.The HD algorithm was used to obtain the affinity between the antibody and antigen,and the minimum HD was screened to obtain the prediction result.The collected dynamic data of the drilling cuttings gas desorption index Ki and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province,China,were used as prediction indices.The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts,and it has a good prediction of the risk of coal and gas outbursts.The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.展开更多
In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data ...In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.展开更多
基金This research was financially supported by the National Natural Science Foundation of China(51874165,51974148)Liaoning Xingliao Talent Program(XLYC1902106).
文摘As mines become deeper,the potential for coal and gas outbursts in deep rock cross-cut coal uncovering is enhanced.The outburst precursors are unclear,which restricts the effectiveness and reliability of warning systems.To reveal the evolution characteristics of coal and gas outburst precursor information in deep rock cross-cut coal uncovering,briquette specimens are constructed and experiments are conducted using a self-developed true triaxial outburst test system.Using acoustic emission monitoring technology,the dynamic failure of coal is monitored,and variations in the root mean square(RMS)of the acoustic emissions allow the effective cracking time and effective cracking gas pressure to be defined.These characteristics are obviously different in deep and shallow coal.The characteristic parameters of gas outburst exhibit stepwise variations at different depths.The RMS and cumulative RMS have stepped failure characteristics with respect to changes in gas pressure.The characteristic parameters of coal failure are negatively correlated with the average in-situ stress and effective stress,but positively correlated with the lateral pressure coefficient of in-situ stress and the critical gas pressure.The transition characteristics are highly sensitive in all cases.The critical depth between deep and shallow coal and gas outbursts is 1700 m.The expansion multiple of acoustic emission intensity from the microfracture stage to the sharp-fracture stage of coal is defined as the outburst risk index,N1.For depths of 1100–1700 m,N1≥7 denotes a higher risk of outburst,whereas at depths of 1700–2500 m,N1≥3 indicates enhanced risk.
基金the Major State Basic Research Program of China which provided for our financial support (No. 2005CB221501)
文摘For a low permeability single coal seam prone to gas outbursts, pre-drainage of gas is difficult and inefficient, seriously restricting the safety and efficiency of production. Radical measures of increasing gas extraction efficiency are pressure relief and infrared antireflection. We have analyzed the effect of mining conditions and the regularity of mine pressure distribution in front of the working face of a major coal mine of the Jiaozuo Industrial （Group） Co. as our test area, studied the width of the depressurization zone in slice mining and analyzed gas efficiency and fast drainage in the advanced stress relaxation zone. On that basis, we further investigated and practiced the exploitation technology of shallow drilling, fan dril- ling and grid shape drilling at the working face. Practice and our results show that the stress relaxation zone is the ideal region for quick and efficient extraction of gas. By means of an integrated extraction technology, the amount of gas emitted into the zone was greatly reduced, while the risk of dangerous outbursts of coal and gas was lowered markedly. This exploration provides a new way to control for gas in working faces of coal mines with low permeability and risk of gas outbursts of single coal seams in the Jiaozuo mining area.
基金supported by the National Natural Science Foundation of China(Nos.51974120 and 52274196)。
文摘Currently,coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results.Owing to this,an artificial immune algorithm(AIA)was developed for coal and gas outburst prediction based on the Hamming distance(HD)calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm(HDAIA).The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm.The HD algorithm was used to obtain the affinity between the antibody and antigen,and the minimum HD was screened to obtain the prediction result.The collected dynamic data of the drilling cuttings gas desorption index Ki and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province,China,were used as prediction indices.The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts,and it has a good prediction of the risk of coal and gas outbursts.The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.
基金financially supported by the National Natural Science Foundation of China(52174117,52004117)Postdoctoral Science Foundation of China(2021T140290,2020M680975)Science and Technology Research Project of Liaoning Provincial Department of Education(LJ2020JCL005).
文摘In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.
