为缩短煤自燃倾向性的鉴定时间,首先利用工业分析仪及程序升温试验装置,测得各煤样煤质指标值及不同温度下煤自燃指标气体含量,并通过CO体积分数确定各煤样低温氧化临界温度点;然后再通过Arrhenius公式拟合得出温度与耗氧速率间的方程,并求解出各煤样临界温度前后不同阶段的表观活化能,通过Pearson相关系数法进行煤质指标值与煤样临界温度前后表观活化能之间的关联分析,并计算其相关系数;最后选取相关系数最大的煤质指标值,建立用于计算煤样表观活化能的多元线性回归模型,分析并预测煤自燃危险性.结果表明:煤质指标中不同成分与临界温度前后表观活化能间的相关系数有较大差异,其中挥发分与临界温度前后表观活化能的负相关系数最大,分别为-0.893 和-0.977,燃料比与临界温度前后表观活化能的正相关系数最大,分别为0.956 和0.968.所建立的多元线性回归模型,其拟合度可达0.912 5 和0.933 0.
In order to shorten the identification time of coal spontaneous combustion tendency,firstly,industrial analyzer and temperature programmed test device were used to measure the coal quality index value and the content of coal spontaneous combustion indicator gas at different temperatures.The critical temperature point of low-temperature oxidation was determined by CO volume fraction.Then,the equation between temperature and oxygen consumption rate was fitted through the Arrhenius formula,and the apparent activation energy of each coal sample at different stages before and after the critical temperature was solved.The correlation between the coal quality index value and the apparent activation energy before and after the critical temperature was analyzed by Pearson correlation coefficient method,and the correlation coefficient was calculated.Finally,the coal quality index value with the largest correlation coefficient was selected,and a multiple linear regression model for calculating the apparent activation energy of coal samples was established to analyze and predict the spontaneous combustion risk of coal.The results show that the correlation coefficients between different components of coal quality and the apparent activation energy before and after the critical temperature are significantly different.The negative correlation coefficients between volatile matter and the apparent activation energy before and after the critical temperature are the largest,which are-0.893 and-0.977 respectively.The positive correlation coefficients between fuel ratio and the apparent activation energy before and after the critical temperature are the largest,which are 0.956 and 0.968 respectively.The fitting degree of the established multiple linear regression model can reach 0.912 5 and 0.933 0.