基于优化BP神经网络的中子法检测煤中碳含量  

Detection of Carbon Content in Coal with Neutron Method Based on an Optimized BP Neural Network

作　　者：桑海峰[1] 王福利[1] 刘林茂[2] 

机构地区：[1]东北大学信息科学与工程学院教育部辽宁省流程工业综合自动化重点实验室,辽宁沈阳110004 [2]东北师范大学物理系,吉林长春130024

出　　处：《计量学报》2006年第1期73-76,共4页Acta Metrologica Sinica

基　　金：国家973计划(2002CB312200)

摘　　要：介绍了中子法快速检测煤中碳含量的方法。基于遗传算法(GA)优化的BP神经网络建立了煤中碳含量的检测模型,并结合电厂锅炉燃烧用煤的实测数据进行模型的验证研究。结果表明,该方法对煤中碳含量的检测精度达到了0.5%。The detection method of carbon content in coal with neutron method is introduced. The detection model of carbon content in coal based on BP neural network, which is optimized by genetic algorithms, is put forward. A study for verifying the model is made by comparison with the practical measured data of coal in power plant. The results show that the detection precision is 0.5 % with this model.

关 键 词：计量学 碳含量 遗传算法 BP神经网络 中子法 特征γ射线 

分 类 号：TB99[一般工业技术—计量学]