Experimental investigation of temperature distribution and spontaneous combustion tendency of coal gangue stockpiles in storage

2021 ◽  
Author(s):  
Ang Li ◽  
Changkun Chen ◽  
Jie Chen ◽  
Peng Lei ◽  
Yulun Zhang
Keyword(s):  
Experimental Investigation ◽  
Temperature Distribution ◽  
Spontaneous Combustion ◽  
Coal Gangue

Ambient PM2.5 and Related Health Impacts of Spontaneous Combustion of Coal and Coal Gangue

2021 ◽  
Author(s):  
Wenkai Guo ◽  
Bing Chen ◽  
Guangyao Li ◽  
Mingyue Liu ◽  
Xiao Liu ◽  
...  
Keyword(s):  
Spontaneous Combustion ◽  
Coal Gangue ◽  
Health Impacts ◽  
Spontaneous Combustion Of Coal

scholarly journals Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

2021 ◽  
Vol 13 (13) ◽  
pp. 2634
Author(s):  
Qiyuan Wang ◽  
Yanling Zhao ◽  
Feifei Yang ◽  
Tao Liu ◽  
Wu Xiao ◽  
...  
Keyword(s):  
Heat Stress ◽  
Water Content ◽  
Spontaneous Combustion ◽  
Leaf Water ◽  
Coal Gangue ◽  
Leaf Water Content ◽  
Spectral Features ◽  
Lasso Regression ◽  
Stress Assessment ◽  
First Derivative

Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.

scholarly journals Peak Temperature Correlation and Temperature Distribution during Joining of AZ80A Mg Alloy by FSW – A Numerical and Experimental Investigation

2020 ◽  
Vol 66 (6) ◽  
pp. 395-407 ◽  
Author(s):  
p Sevvel ◽  
S.D. Dhanesh Babu ◽  
R. Senthil Kumar
Keyword(s):  
Experimental Investigation ◽  
Temperature Distribution ◽  
Quadratic Equation ◽  
Peak Temperature ◽  
Mg Alloy ◽  
Element Analysis ◽  
Temperature Correlation ◽  
Friction Stir ◽  
Az80a Mg Alloy ◽  
Experimental Values

A quadratic equation has been developed based on experimental measurements to estimate the peak temperature in the friction stir welding (FSW) process during the joining of AZ80A Mg alloys. The numerical simulation of the FSW process was performed by employing COMSOL software to predict and calculate the distribution of temperature on the various regions of the parent metal and the welded joints. The predicted and finite element analysis (FEA) simulating the results of the distribution of peak temperatures were found to be consistent with the experimental values. In addition to this, a parametric experimental investigation was conducted to identify the most influential process parameter that plays a significant role in the peak temperature distribution during FSW of AZ80A Mg alloy. Linear contributions by the input process parameters of FSW, namely, traversing speed, rotating tool speed and axial force on the peak temperature were observed to be 32.82 %, 41.65 % and 21.76 %, respectively.

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