化学组分及单组分熔融温度与煤灰熔融温度的相关性分析  

The Correlativity on Shan Chemistry Component Melting Temperature and Coal Ash Melting Temperature

作　　者：樊泉桂[1] 潘攀[1] 

机构地区：[1]华北电力大学,河北保定071003

出　　处：《锅炉技术》2007年第6期10-13,19,共5页Boiler Technology

摘　　要：提出考虑煤灰各成分熔融温度与煤灰化学成分含量的统计分析判别方法及神经网络判别方法。对170多种中国煤质采用2种方法的计算结果与实验数值的比较结果表明,使用统计分析能将煤灰熔融温度的计算命准率提高到80%以上,使用神经网络方法的命准率达100%,且均不需要将煤质分类判别。提高判别方法命准率的核心机理是:煤灰的结渣特性主要取决于煤灰中各化学成分的百分比含量,同时也取决于各物质的熔点温度。这一机理与很多电站锅炉通过掺烧高灰熔点煤降低结渣程度的实际运行事实和试验结果相吻合,提高了各种煤质的结渣特性判别的通用性和命准率。神经网络方法具有智能优化的特点,比统计分析方法更具有普适性和高命准率。Consider the ash melting temperature and composition of the ash content of the chemical composition on the method of statistical analysis and neural network. Uses 2 methods to 170 kinds of Chinese coal, the computed results and the empirical datum comparison result indicated, statistical analysis can fuse the coal ash slagging characteristic the temperature the distinction life rate enhancement to 80% above, the neural network method reaches 100%, both does not need the coal classification distinction. Discriminate method to improve the lives prospective core rate mechanism is： ash slagging characteristics depend mainly on the percentage of ash content in the chemical composition, but also depends on the melting point temperature of the material. This mechanism tallies the practical facts and the test results in a lot of power station boilers burn the coal doped with high degree of ash melting point lower slagging, which enhance the distinguishing characteristics of various coal slagging the overall rate and life potential. Neural network is intelligent optimization, more universal access and high rate of life than statistical analysis methods.

关 键 词：煤灰化学组分 单组分熔融温度 煤灰熔融温度 相关分析 统计分析 神经网络 

分 类 号：TQ533.2[化学工程—煤化学工程]