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Black powder source identification in a gas pipeline network based on a One-D model

机译:基于一维模型的燃气管网黑粉源识别

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Black Powder (BP) is a phenomenon experienced worldwide by transmission gas pipeline operators with internally uncoated lines. It can cause serious problems in pipeline operation and instruments, and contaminate customer supply. It is regenerative and forms inside natural gas pipelines due to corrosion of the internal walls of the pipeline and chemical reactions. The aim of the present study is to develop a novel algorithm for BP source identification within gas pipelines network based on a 1-D static model. The proposed identification method is based on the well-known Particle Swarm Optimization (PSO) algorithm, which is able to identify and quantify BP source at different junctions simultaneously. Extensive simulation results are given to illustrate the effectiveness of the proposed optimization algorithm for BP identification.
机译:黑粉(BP)是全球范围内使用内部未涂层管线的输气管道运营商所经历的一种现象。这可能会在管道运行和仪器中造成严重问题,并污染客户供应。它是可再生的,由于管道内壁的腐蚀和化学反应而在天然气管道内部形成。本研究的目的是开发一种基于一维静态模型的天然气管道网络内BP源识别的新算法。所提出的识别方法基于众所周知的粒子群优化(PSO)算法,该算法能够同时识别和量化不同交界处的BP源。大量的仿真结果说明了所提出的优化算法对BP辨识的有效性。

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