Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can a...Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.展开更多
Coal seam gas content is frequently measured in quantity during underground coal mining operation and coalbed methane(CBM)exploration as a significant basic parameter.Due to the calculation error of lost gas and resid...Coal seam gas content is frequently measured in quantity during underground coal mining operation and coalbed methane(CBM)exploration as a significant basic parameter.Due to the calculation error of lost gas and residual gas in the direct method,the efficiency and accuracy of the current methods are not inadequate to the large area multi-point measurement of coal seam gas content.This paper firstly deduces a simplified theoretical dynamic model for calculating lost gas based on gas dynamic diffusion theory.Secondly,the effects of various factors on gas dynamic diffusion from coal particle are experimentally studied.And sampling procedure of representative coal particle is improved.Thirdly,a new estimation method of residual gas content based on excess adsorption and competitive adsorption theory is proposed.The results showed that the maximum error of calculating the losing gas content by using the new simplified model is only 4%.Considering the influence of particle size on gas diffusion law,the particle size of the collected coal sample is below 0.25 mm,which improves the measurement speed and reflects the safety representativeness of the sample.The determination time of gas content reduced from 36 to 3 h/piece.Moreover,the absolute error is 0.15–0.50 m^3/t,and the relative error is within 5%.A new engineering method for determining the coal seam gas content is developed according to the above research.展开更多
The coal bed methane content(CBMC)in the west mining area of Jincheng coalfield,southeastern Qjnshui Basin,is studied based on seismic data and well-logs together with laboratory measurements.The results show that the...The coal bed methane content(CBMC)in the west mining area of Jincheng coalfield,southeastern Qjnshui Basin,is studied based on seismic data and well-logs together with laboratory measurements.The results show that the Shuey approximation has better adaptability according to the Zoeppritz equation result;the designed fold number for an ordinary seismic data is sufficient for post-stack data but insufficient for pre-stack data regarding the signal to noise ratio(SNR).Therefore a larger grid analysis was created in order to improve the SNR.The velocity field created by logging is better than that created by stack velocity in both accuracy and effectiveness.A reasonable distribution of the amplitude versus offset(AVO)attributes can be facilitated by taking the AVO response from logging as a standard for calibrating the amplitude distribution.Some AVO attributes have a close relationship with CBMC.The worst attribute is polarization magnitude,for which the correlation coefficient is 0.308;and the best attribute is the polarization product from intercept,of which the correlation coefficient is-0.8136.CBMC predicted by AVO attributes is better overall than that predicted by direct interpolation of CBMC;the validation error of the former is 14.47%,which is lower than that of the latter 23.30%.CBMC of this area ranges from2.5 m^3/t to 22 m^3/t.Most CBMC in the syncline is over 10m^3/t,but it is below 10m^3/t in the anticline;on the whole,CBMC in the syncline is higher than that in anticline.展开更多
The carbon content of bituminous coal samples was analyzed by laser-induced breakdown spectroscopy. The 266 nm laser radiation was utilized for laser ablation and plasma generation in air. The partial least square met...The carbon content of bituminous coal samples was analyzed by laser-induced breakdown spectroscopy. The 266 nm laser radiation was utilized for laser ablation and plasma generation in air. The partial least square method and the dominant factor bused PLS method were used to improve the measurement accuracy of the carbon content of coal. The results showed that the PLS model could achieve good measurement accuracy, and the dominant factor based PLS model could further improve the measurement accuracy. The coefficient of determination and the root-mean-square error of prediction of the PLS model were 0.97 and 2.19%, respectively; and those values for the dominant factor based PLS model were 0.99 and 1.51%, respectively. The results demonstrated that the 266 nm wavelength could accurately measure the carbon content of bituminous coal.展开更多
Gas content of coal is mostly determined using a direct method, particularly in coal mining where mine safety is of paramount importance. Direct method consists of measuring directly the volume of gas desorbed from co...Gas content of coal is mostly determined using a direct method, particularly in coal mining where mine safety is of paramount importance. Direct method consists of measuring directly the volume of gas desorbed from coal in several steps, from solid then crushed coal. In mixed gas conditions the composition of the desorbed gas is also measured to account for contribution of various coal seam gas in the mix. The determination of gas content using the direct method is associated with errors of measurement of volume of gas but also the errors associated with measurement of composition of the desorbed gas. These errors lead to uncertainties in reporting the gas content and composition of in-situ seam gas. This paper discusses the current direct method practised in Australia and potential errors and uncertainty associated with this method. Generic methods of estimate of uncertainties are also developed and are to be included in reporting gas content of coal. A method of direct measurement of remaining gas in coal following the completion of standard gas content testing is also presented. The new method would allow the determination of volume of almost all gas in coal and therefore the value of total gas content. This method is being considered to be integrated into a new standard for gas content testing.展开更多
Based on analysis of regularity of stacking coal,discrete element simultaneous simulation is adopted to predict the process of unloading coal,which is proved to be effcient in the prediction of ash content.The results...Based on analysis of regularity of stacking coal,discrete element simultaneous simulation is adopted to predict the process of unloading coal,which is proved to be effcient in the prediction of ash content.The results show that the altitude of new irregular coal is equal to the income coal volume divided by area of cabin.The distribution of infnitesimal flow velocity helps to induce the motion equation of infnitesimal element,which provides the mathematical model for computer simulation.Swarm,a computer programming language,is utilized in this study.Adaptive infnitesimal stacking algorithm helps settle the diffculties in attainment of infnitesimal elements.The result of simulation is similar to the actual situation,which can accurately predict the ash contents of current time and cumulative time.Coal movement in the cabin is a new project,the result of which can also be applied to other solid particles and the widespread of the result will be highly valued.展开更多
The coal filter cake is a product of fine coal after floatation which has an ash content of 7-13%, water content of 30±2%, and a particle size of less than 1 mm. The ash content was measured by the intensity of t...The coal filter cake is a product of fine coal after floatation which has an ash content of 7-13%, water content of 30±2%, and a particle size of less than 1 mm. The ash content was measured by the intensity of the single backscattered gamma-ray, and its accuracy is mainly dependent on the energy of the gamma-ray. The 238Pu low energy photon source is selected in this work. The energy of its gamma-ray is 15 keV, which can result not only in the best sensitivity, but also in the lowest contribution to the environment radiation. The root mean square deviation of the ash measurement is±0.33% (±1σ).展开更多
The square root relationship of gas release in the early stage of desorption is widely used to provide a simple and fast estimation of the lost gas in coal mines. However, questions arise as to how the relationship wa...The square root relationship of gas release in the early stage of desorption is widely used to provide a simple and fast estimation of the lost gas in coal mines. However, questions arise as to how the relationship was theoretically derived, what are the assumptions and applicable conditions and how large the error will be. In this paper, the analytical solutions of gas concentration and fractional gas loss for the diffusion of gas in a spherical coal sample were given with detailed mathematical derivations based on the diffusion equation. The analytical solutions were approximated in case of small values of time and the error analyses associated with the approximation were also undertaken. The results indicate that the square root relationship of gas release is the first term of the approximation, and care must be taken in using the square root relationship as a significant error might be introduced with increase in the lost time and decrease in effective diameter of a spherical coal sample.展开更多
基金financial supports from National Natural Science Foundation of China(No.62205172)Huaneng Group Science and Technology Research Project(No.HNKJ22-H105)Tsinghua University Initiative Scientific Research Program and the International Joint Mission on Climate Change and Carbon Neutrality。
文摘Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.
基金the National Natural Science Foundation of China(51774119,51374095,and 51604092)the primary research projects of critical scientific research in Henan Colleges and Universities(19zx003)+1 种基金Program for Innovative Research Team in University of Ministry of Education of China(IRT_16R22)State Key Laboratory Cultivation Base for Gas Geology and Gas Control(Henan Polytechnic University)(WS2018A02)。
文摘Coal seam gas content is frequently measured in quantity during underground coal mining operation and coalbed methane(CBM)exploration as a significant basic parameter.Due to the calculation error of lost gas and residual gas in the direct method,the efficiency and accuracy of the current methods are not inadequate to the large area multi-point measurement of coal seam gas content.This paper firstly deduces a simplified theoretical dynamic model for calculating lost gas based on gas dynamic diffusion theory.Secondly,the effects of various factors on gas dynamic diffusion from coal particle are experimentally studied.And sampling procedure of representative coal particle is improved.Thirdly,a new estimation method of residual gas content based on excess adsorption and competitive adsorption theory is proposed.The results showed that the maximum error of calculating the losing gas content by using the new simplified model is only 4%.Considering the influence of particle size on gas diffusion law,the particle size of the collected coal sample is below 0.25 mm,which improves the measurement speed and reflects the safety representativeness of the sample.The determination time of gas content reduced from 36 to 3 h/piece.Moreover,the absolute error is 0.15–0.50 m^3/t,and the relative error is within 5%.A new engineering method for determining the coal seam gas content is developed according to the above research.
基金supported by the National Basic Research Program of China(Nos.2009CB219603,2010CB226800,2009CB724601 and 2012BAC10B03)the National Natural Science Foundation of China(Major Program)(Nos.50490271 and 40672104)+2 种基金the National Natural Science Foundation of China(General Program)(No.40874071)the National Science&Technology Pillar Program in the Eleventh Five-Year Plan Period(Nos.2012BAB13B01 and2012BAC10B03)the Key Grant Project of Chinese Ministry of Education(No.306002)
文摘The coal bed methane content(CBMC)in the west mining area of Jincheng coalfield,southeastern Qjnshui Basin,is studied based on seismic data and well-logs together with laboratory measurements.The results show that the Shuey approximation has better adaptability according to the Zoeppritz equation result;the designed fold number for an ordinary seismic data is sufficient for post-stack data but insufficient for pre-stack data regarding the signal to noise ratio(SNR).Therefore a larger grid analysis was created in order to improve the SNR.The velocity field created by logging is better than that created by stack velocity in both accuracy and effectiveness.A reasonable distribution of the amplitude versus offset(AVO)attributes can be facilitated by taking the AVO response from logging as a standard for calibrating the amplitude distribution.Some AVO attributes have a close relationship with CBMC.The worst attribute is polarization magnitude,for which the correlation coefficient is 0.308;and the best attribute is the polarization product from intercept,of which the correlation coefficient is-0.8136.CBMC predicted by AVO attributes is better overall than that predicted by direct interpolation of CBMC;the validation error of the former is 14.47%,which is lower than that of the latter 23.30%.CBMC of this area ranges from2.5 m^3/t to 22 m^3/t.Most CBMC in the syncline is over 10m^3/t,but it is below 10m^3/t in the anticline;on the whole,CBMC in the syncline is higher than that in anticline.
基金supported by National Natural Science Foundation of China(No.51276100)the National Basic Research Program of China(973 Program)(No.2013CB228501)the financial funding from the U.S.Department of Energy,Office of Basic Energy Sciences,Chemical Science Division at Lawrence Berkeley National Laboratory(No.2013CB228501)
文摘The carbon content of bituminous coal samples was analyzed by laser-induced breakdown spectroscopy. The 266 nm laser radiation was utilized for laser ablation and plasma generation in air. The partial least square method and the dominant factor bused PLS method were used to improve the measurement accuracy of the carbon content of coal. The results showed that the PLS model could achieve good measurement accuracy, and the dominant factor based PLS model could further improve the measurement accuracy. The coefficient of determination and the root-mean-square error of prediction of the PLS model were 0.97 and 2.19%, respectively; and those values for the dominant factor based PLS model were 0.99 and 1.51%, respectively. The results demonstrated that the 266 nm wavelength could accurately measure the carbon content of bituminous coal.
文摘Gas content of coal is mostly determined using a direct method, particularly in coal mining where mine safety is of paramount importance. Direct method consists of measuring directly the volume of gas desorbed from coal in several steps, from solid then crushed coal. In mixed gas conditions the composition of the desorbed gas is also measured to account for contribution of various coal seam gas in the mix. The determination of gas content using the direct method is associated with errors of measurement of volume of gas but also the errors associated with measurement of composition of the desorbed gas. These errors lead to uncertainties in reporting the gas content and composition of in-situ seam gas. This paper discusses the current direct method practised in Australia and potential errors and uncertainty associated with this method. Generic methods of estimate of uncertainties are also developed and are to be included in reporting gas content of coal. A method of direct measurement of remaining gas in coal following the completion of standard gas content testing is also presented. The new method would allow the determination of volume of almost all gas in coal and therefore the value of total gas content. This method is being considered to be integrated into a new standard for gas content testing.
基金the financial support provided by the National Natural Science Foundation of China(No.51174202)Jiangsu Natural Science Foundation of China(No.20100095110013)
文摘Based on analysis of regularity of stacking coal,discrete element simultaneous simulation is adopted to predict the process of unloading coal,which is proved to be effcient in the prediction of ash content.The results show that the altitude of new irregular coal is equal to the income coal volume divided by area of cabin.The distribution of infnitesimal flow velocity helps to induce the motion equation of infnitesimal element,which provides the mathematical model for computer simulation.Swarm,a computer programming language,is utilized in this study.Adaptive infnitesimal stacking algorithm helps settle the diffculties in attainment of infnitesimal elements.The result of simulation is similar to the actual situation,which can accurately predict the ash contents of current time and cumulative time.Coal movement in the cabin is a new project,the result of which can also be applied to other solid particles and the widespread of the result will be highly valued.
文摘The coal filter cake is a product of fine coal after floatation which has an ash content of 7-13%, water content of 30±2%, and a particle size of less than 1 mm. The ash content was measured by the intensity of the single backscattered gamma-ray, and its accuracy is mainly dependent on the energy of the gamma-ray. The 238Pu low energy photon source is selected in this work. The energy of its gamma-ray is 15 keV, which can result not only in the best sensitivity, but also in the lowest contribution to the environment radiation. The root mean square deviation of the ash measurement is±0.33% (±1σ).
文摘The square root relationship of gas release in the early stage of desorption is widely used to provide a simple and fast estimation of the lost gas in coal mines. However, questions arise as to how the relationship was theoretically derived, what are the assumptions and applicable conditions and how large the error will be. In this paper, the analytical solutions of gas concentration and fractional gas loss for the diffusion of gas in a spherical coal sample were given with detailed mathematical derivations based on the diffusion equation. The analytical solutions were approximated in case of small values of time and the error analyses associated with the approximation were also undertaken. The results indicate that the square root relationship of gas release is the first term of the approximation, and care must be taken in using the square root relationship as a significant error might be introduced with increase in the lost time and decrease in effective diameter of a spherical coal sample.
