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
Calorific value of plants is an important parameter for evalu- ating and indexing material cycles and energy conversion in forest eco- systems. Based on mensuration data of 150 sample sets, we analyzed the calorific v...Calorific value of plants is an important parameter for evalu- ating and indexing material cycles and energy conversion in forest eco- systems. Based on mensuration data of 150 sample sets, we analyzed the calorific value （CV） and ash content （AC） of different parts of Masson pine （Pinus massoniana） trees in southern China using hypothesis testing and regression analysis. CV and AC of different tree parts were almost significantly different （P〈0.05）. In descending order, ash-free calorific value （AFCV） ranked as foliage 〉 branch 〉 stem bark 〉 root 〉 stem wood, and AC ranked as foliage 〉 stem bark 〉 root 〉 branch 〉 stem wood. CV and AC of stem wood from the top, middle and lower sections of trees differed significantly. CV increased from the top to the lower sections of the tnmk while AC decreased. Mean gross calorific value （GCV） and AFCV of aboveground parts were significantly higher than those of belowground parts （roots）. The mean GCV, AFCV and AC of a whole tree of Masson pine were 21.54 kJ/g, 21.74 kJ/g and 0.90%, re- spectively. CV and AC of different tree parts were, to some extent, cor- related with tree diameter, height and origin.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 impact of fly ash content on bond performance of steel bars and their surrounding concrete is studied by means of sticking strain gauges on steel bars. The average bond stress-slip curves, the steel strain-anchor ...The impact of fly ash content on bond performance of steel bars and their surrounding concrete is studied by means of sticking strain gauges on steel bars. The average bond stress-slip curves, the steel strain-anchor location curves, and the bond stress-anchor position curves of the pullout specimens with various fly ash contents are obtained. Results indicate that the bond performance of concrete and steel bars can be improved and the distribution of steel strain along the anchorage length tends to be more uniform by adding fly ash in concrete specimens, and both ultimate bond stress and ultimate slip deformation increase the most when 20% of specimens′ content is fly ash.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initiated as part of the National Biomass Modeling Program in Continuous Forest Inventory(NBMP-CFI)funded by the State Forestry Administration of China
文摘Calorific value of plants is an important parameter for evalu- ating and indexing material cycles and energy conversion in forest eco- systems. Based on mensuration data of 150 sample sets, we analyzed the calorific value （CV） and ash content （AC） of different parts of Masson pine （Pinus massoniana） trees in southern China using hypothesis testing and regression analysis. CV and AC of different tree parts were almost significantly different （P〈0.05）. In descending order, ash-free calorific value （AFCV） ranked as foliage 〉 branch 〉 stem bark 〉 root 〉 stem wood, and AC ranked as foliage 〉 stem bark 〉 root 〉 branch 〉 stem wood. CV and AC of stem wood from the top, middle and lower sections of trees differed significantly. CV increased from the top to the lower sections of the tnmk while AC decreased. Mean gross calorific value （GCV） and AFCV of aboveground parts were significantly higher than those of belowground parts （roots）. The mean GCV, AFCV and AC of a whole tree of Masson pine were 21.54 kJ/g, 21.74 kJ/g and 0.90%, re- spectively. CV and AC of different tree parts were, to some extent, cor- related with tree diameter, height and origin.
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).
基金Supported by the Program of Excellent Talents in Six Fields of Jiangsu Province(2008183)~~
文摘The impact of fly ash content on bond performance of steel bars and their surrounding concrete is studied by means of sticking strain gauges on steel bars. The average bond stress-slip curves, the steel strain-anchor location curves, and the bond stress-anchor position curves of the pullout specimens with various fly ash contents are obtained. Results indicate that the bond performance of concrete and steel bars can be improved and the distribution of steel strain along the anchorage length tends to be more uniform by adding fly ash in concrete specimens, and both ultimate bond stress and ultimate slip deformation increase the most when 20% of specimens′ content is fly ash.
