用神经网络法预测鼓泡塔内的气含率  

Prediction of Gas Holdup in Bubble Columns Using Artificial Neural Network

作　　者：罗湘华[1] 吴元欣[1] 李定或[1] 

机构地区：[1]武汉化工学院化工系,湖北武汉430073

出　　处：《化学工程》2003年第3期41-44,49,共5页Chemical Engineering(China)

基　　金：国家自然科学基金资助 ( 2 0 0 76 0 36 )

摘　　要：在 2 0 0 0多组公开发表的鼓泡塔实验数据组成的数据库基础上 ,提出了一个预测鼓泡塔内气含率的关联式 ,介绍了一种新的神经网络回归方法。将该方法与受力分析结合起来得到 4个对鼓泡塔内气含率影响较大的无因次准数 。On the basis of a large data bank consisting of more than 2000 experimental results published for bubble columns, a state of the art correlation for the prediction of gas holdup was proposed. A new method of neural network regression was introduced,and it was applied by combined with force analysis to identify four most expressive dimensionless groups. Assessment of the correlation was demonstrated.

关 键 词：神经网络法 预测 鼓泡塔 气含率 关联式 

分 类 号：TQ021[化学工程]