多相流管线中严重段塞流流型的在线识别

On-Line Flow Regime Identification of Severe Slugging in Multiphase Liquid and Gas Pipelines

基于压力信号的分析,本文对模拟海洋平台输油管道的集输-立管系统中严重段塞流进行了研究,划分了流型,并对系统中各种流型下压力信号的特征参数进行了提取。通过对这些特征参数的分析,获得了流型的判别规则,并利用人工智能最小二乘支持向量机技术对流型进行了在线识别。通过在线实验表明,使用本文的特征参数提取方法和智能识别技术,能够很好地判断管线中严重段塞流的发生,管中流型的平均识别率达到了96.4%。

An improved technique is presented for the flow regime identification in pipelines-rise sys- tem simulating offshore oil pipelines. The technique is based on signal analysis to classify flow pattern and to extract key feature parameters from pressure fluctuation at riser base in different flow regime. Through the analysis of these characteristic parameters, the flow regulation is obtained and the flow regime is successfully- distinguished and identified using least square support vector machine artificial intelligence technology. On-line experimental results demonstrate that the proposed technique can recognize severe slugging in pipelines and has reached better recognition accuracy which is about 96.4%.