摘要
采用灰熔点较低的襄阳煤和灰熔点较高的晋城无烟煤组成的混合煤样,利用XRF、SEM、DSC、XRD、三元相图等分析方法,探究了襄阳煤对晋城无烟煤煤灰熔融温度的影响。结果表明,配煤能有效降低高熔点煤灰的熔融温度,当襄阳煤的加入量小于24%时,混合煤灰熔融温度显著降低;襄阳煤的加入量在24%-40%时,混合煤灰熔融温度变化平缓且流动温度低于1 400℃。混合煤灰中的成分在1 000-1 200℃发生一系列的化学反应,主要包括SiO_2与Al_2O_3结合产生高熔点物质莫来石以及Fe_2O_3、CaO与莫来石反应转化形成铁尖晶石、钙长石等新物质,由此造成了煤灰熔融温度的变化。基于BP神经网络对实验数据建立预测模型,其预测效果优于前人总结的经验公式,平均准确度高于99%。利用热力学软件HSC 5.0分析了CaO、Fe_2O_3对降低煤灰熔融温度的影响,分析表明,CaO对莫来石的转化作用优于Fe_2O_3。
Xiangyang coal with low ash fusion temperature (AF T ) and Jincheng coal with high A F T were used to prepare the blending samples. The influence of Xiangyang coal addition on AFT of Jincheng coal was explored by XRF , SEM , DSC , XRD , and ternary phase diagram analysis. The results show that blending coal can reduce the AFT effectively. The AFT of blending coal is lowered significantly w h e n the adding amount of Xiangyang coal is lower than 24% . Whereas, w h e n the adding amount is between 24 % and 40 % , AFT of the mixed coal has a slight change and the ash flow temperature is below 1 400 益.A series of chemical reactions a m o n g ash composition of mixed coal occur at 1 000-1 200 ℃,mainly including formation of high melting point compound (mullite) from SiO2 with Al2O3,and that of low melting point compounds ( anorthite and hercynite) from the reactions between mullite and CaO or Fe2O3 . The above reactions mainly cause the changes of ash fusion temperature in blending coal. Based on B P neural network, a prediction model of ash fusion temperature was built. It is proved that the prediction average accuracy by B P neural network is higher than 99 % , which is better than that of a previous empirical formula. Furthermore, analysis by thermodynamics software (HSC 5.0) shows that mullite prefers to react with CaO rather than Fe2O3 .
出处
《燃料化学学报》
EI
CAS
CSCD
北大核心
2016年第12期1430-1439,共10页
Journal of Fuel Chemistry and Technology
基金
国家自然科学基金重点项目(21536009)
山东省自然科学基金(ZR2014BM014)
西安市科技计划项目(CXY1511(4))资助~~
关键词
配煤
灰熔融温度
BP神经网络
coal blending
ash fusion temperature
B P neural network