基于RBF神经网络的湿蒸汽干度软测量模型  

RBF Neural Network Based Soft Measurement Model for Dryness of Wet Steam

作　　者：李亚芬[1] 王会芹[1] 

机构地区：[1]大连理工大学自动化系,大连116024

出　　处：《自动化仪表》2003年第9期9-12,共4页Process Automation Instrumentation

摘　　要：针对汽驱采油中湿蒸汽干度测量精度过低的问题 ,采用RBF神经网络来建立湿蒸汽干度软测量模型 ,采用最小正交二乘法确定网络隐层节点数以及训练网络输出数值 ,并在实际运行中采用在线校正环节。所建立模型具有良好的逼近精度 。In order to resolve the low accuracy problem of dryness measurement for wet steam in gas driving oil extraction,the soft measurement model is established based on RBF neural network.By using least orthogonal square method the node number of implicit layer of network and output value of training network are determined.In practical operation,online correction element is adopted.The model offers excellent accuracy of proximity.The effectiveness of this method is approved in Liaohe Oil field.

关 键 词：汽驱采油 湿蒸汽 干度 测量精度 软测量 最小正交二乘法 RBF神经网络 

分 类 号：TE357[石油与天然气工程—油气田开发工程] TP274[自动化与计算机技术—检测技术与自动化装置]