粉体密相气力输送中的管道压降预测  

The prediction of pipe pressure drop in powder dense-phase pneumatic conveying

作　　者：刘小军[1] 龚欣[1] 屈强[1] 于遵宏[1] 谭可荣[2] 

机构地区：[1]华东理工大学洁净煤技术研究所,上海200237 [2]科技部高新司,北京100862

出　　处：《计算机与应用化学》2003年第1期13-15,共3页Computers and Applied Chemistry

基　　金：国家"十五"科技攻关项目(2001BA301B01)

摘　　要：利用人工神经网络技术,建立了BP网络模型,通过网络的学习训练,比较准确地预测了粉体密相气力输送过程中的管道压降,预测准确率在93.3％以上,表明该方法可以作为密相气力输送研究中的一种有效的辅助手段。The technology of artificial neural network is used in this paper.The back-propagation (BP) network model is also established. And the pipe pressure drop in dense-phase pneumatic conveying is predicted well by applying this network model. The result of prediction reaches more than 93.3% , which indicates that BP network can be applied as an efficient auxiliary method in study of dense-phase pneumatic conveying.

关 键 词：神经网络 密相气力输送 压降 

分 类 号：O643[理学—物理化学]