摘要
基于电容层析成像(ECT)和人工神经网络的软测量方法,实现了两相流流型识别。以油气两相流为例,建立了两相流流型识别的软测量模型。从ECT传感器的输出中提取特征参数作为软测量模型的辅助变量,两相流流型为主导变量,构建二级自组织竞争神经网络,进而实现对两相流流型的在线判别。仿真结果表明,该方法判别精度高、判别速度快。
Taking the oil-gas two-phase flow as an example, this paper investigated the identification of flow regimes using soft-sensing technique, which is based on Electrical capacitance tomography （ECT） and artificial neural network. An online soft-sensing model was built. The character parameters extracted from ECT sensor outputs were the second variable. The primary variable was flow regime of oil-gas two-phase flow. Then, a two- layer self-organizing competitive neural network was built. Simulation results showed that the proposed method has good identification precision and fast identification speed, which means it is an effective tool in two-phase flow pattern online identification.
出处
《电力科学与工程》
2007年第3期6-8,16,共4页
Electric Power Science and Engineering
基金
国家自然科学基金资助项目(60301008
50337020)
天津自然科学基金资助项目(013614411.)
关键词
电容层析成像
软测量技术
流型识别
自组织竞争神经网络
electrical capacitance tomography
soft-sensing technique
flow regime identification
self-organizing competitive neural network
