摘要
为了探索国能集团煤灰白度特征和灰成分的关系,借助SPSS统计分析软件进行了相关性分析,以灰白度为因变量,煤灰各成分为自变量建立多元线性回归模型。结果表明,煤灰白度与灰成分中的K_(2)O、Fe_(2)O_(3)、MnO_(2)、Al_(2)O_(3)具有很强的相关性,而与P_(2)O _(5)没有相关关系;建立了含Al_(2)O_(3)、Fe_(2)O_(3)、K_(2)O三个自变量的模型,可以解释白度的94.6%变化原因,模型具有一定的统计学意义,为煤灰白度特征和灰成分关系的探究提供参考。
In order to explore the characteristic relationship between coal ash composition and ash whiteness of Guoneng Group,a correlation analysis was carried out with the help of SPSS statistical analysis software.A multiple linear regression model was established with ash whiteness as the dependent variable and coal ash composition as the independent variable.The results showed that K_(2)O,Fe_(2)O_(3),MnO_(2) and Al_(2)O_(3) in coal ash had a strong correlation with ash whiteness,while P_(2)O_(5) had no correlation with ash whiteness,and the established model contained three independent variables,namely Al_(2)O_(3),Fe_(2)O_(3) and K_(2)O,which could explain the 94.6%change in whiteness.The model is statistically significant.This study provides a reference for the exploration of the relationship between the whiteness characteristics and ash composition of coal ash.
作者
许琴
XU Qin(South China Branch,China Energy Coal Trading Go.,LTD.,Guangzhou,Guangdong 510610,China)
出处
《煤炭加工与综合利用》
CAS
2024年第8期127-130,136,共5页
Coal Processing & Comprehensive Utilization
关键词
煤灰成分
灰白度
SPSS
多元线性回归
相关性
coal ash composition
ash whiteness
SPSS
multiple linear regression
correlation