基于双能γ射线的煤质灰分软测量技术研究  

Study on soft-sensing of coal ash content based on dual-energy γ-ray

作　　者：程栋[1] 温和[1] 滕召胜[1] 黎福海[1] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《仪器仪表学报》2014年第10期2263-2270,共8页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(61370014;51377049)资助项目

摘　　要：为提高煤质灰分测量精度,提出了基于双能γ射线的煤质灰分智能软测量方法,该方法以137Cs和241Am作为中能和低能的γ射线源,并以探测器检测到的γ计数作为辅助变量,利用混沌算法优化的函数链神经网络实现灰分软测量辨识建模,最后对煤质灰分进行软测量预测和验证。研究结果表明:混沌算法优化的函数链神经网络预测方法的预测精度高,具有较强的泛化能力;基于混沌算法优化函数链神经网络的灰分智能软测量值与实测值的平均误差为0.7%,最大误差为0.9%,煤质灰分测量准确度高。In order to enhance the measurement accuracy of coal ash content, An intelligent soft-sensing method with dual-energy γ-ray based on functional link neural network is proposed. The proposed method takes 137Cs and 241Am as the sources of medium and low ener- gy γ-rays, and the results of the γ-countering as auxiliary variables. The coal ash content is measured and verified after accomplishing the modeling of soft-sensing applying the functional link neural network optimized with Chaos optimization algorithm. The study results show that the functional link neural network forecasting method based on Chaos optimization algorithm has higher accuracy and stronger generalization capability than other forecasting methods. The intelligent soft-sensing based on optimized functional link neural network with the Chaos algorithm has good measurement accuracy, and the maximum error and average error between the soft-sensing value and real value are 0.9% and 0.7% , respectively.

关 键 词：煤灰分 软测量 函数链神经网络 混沌优化 双能量γ射线 

分 类 号：TH83[机械工程—仪器科学与技术]