基于双能γ射线的煤炭灰分测量模型及其应用  

Coal Ash Measurement Model and Its Application Based on Dual-energy γ-ray

作　　者：程栋[1] 滕召胜[1] 黎福海[1] 代扬[1] 

机构地区：[1]湖南大学电气与信息工程学院,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2014年第5期99-105,共7页Journal of Hunan University:Natural Sciences

基　　金：国家科技支撑计划项目(2012BAJ24B00)

摘　　要：针对传统双能γ射线测量法检测误差较大以及灰分成分对检测精度影响大的问题,建立了基于模糊神经网络的双能γ射线的新型煤炭灰分测量模型,并应用该模型对煤炭灰分进行了在线检测试验,实例分析了两种双能γ射线测量方法在煤炭灰分检测中的应用情况.试验结果表明:相比于传统双能γ射线测量法3%的平均相对误差,本文提出的基于模糊神经网络的双能γ射线的灰分测量法的相对误差小于1%,且其测量结果不受灰分组成成分的影响.同时,利用X-射线荧光光谱分析法(XRF)分析了灰分的化学组分与其含量的关系,研究了灰分化学组分对双能γ透射法检测结果的影响.结果表明:煤炭中Fe,Ca,Mg和S元素的含量会影响双能γ射线透射法的检测精度,其成分含量波动越大,检测结果误差也越大.In order to decrease the measurement error of the traditional dual-energy γ-ray method and reduce the impact of the ash component on detection accuracy in ash determination, a new coal ash measurement model based on fuzzy neural network and the dual-energy γ-ray was established, and then the model was applied on online testing of coal ash content. The application of the traditional and new coal ash measurement model was analyzed by online testing, and the measurement results of both were comparatively analyzed. The results show that the relative error of ash content using the new method was less than 1%, while the average relative error of the traditional measurement was about 3 %, and the measurement accuracy of the new method is not affected by the ash component. Meanwhile, the relationship between the chemical composition of coal ash and ash content was analyzed by using X-ray fluorescence spectrometry （XRF）, and the results indicate that the content of Fe, Ca, Mg and S elements in coal can affect the detection accuracy of traditional dual-energy γ-ray dialysis, the result in greater measurement error. greater fluctuations of their ingredient content result in greater measurement error.

关 键 词：煤 灰分 双能γ射线 模糊神经网络 化学组分 

分 类 号：TD94[矿业工程—选矿]