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Evaluation Of Corrosion Resistance Of Buried Gas Pipeline And Identify With Break Points

Posted on:2018-09-16Degree:MasterType:Thesis
Country:ChinaCandidate:Z C MaFull Text:PDF
GTID:2322330515996857Subject:Safety engineering
Abstract/Summary:PDF Full Text Request
With the widespread popularity of gas,China's gas pipeline network has entered a period of rapid development.However,because buried gas pipelines are in a relatively complex external environmental conditions for a long time,and as the pipeline in service time continues to increase,buried gas pipeline external corrosion often corrosion damage situation,will eventually lead to pipeline itself of the corrosion perforation,and then the occurrence of gas spills,threatening people's lives and property damage.Therefore,it is an effective means to ensure the safe operation of buried gas pipeline by evaluating the coating of buried gas pipeline and accurately locating the defect point.This paper describes the corrosive form of buried gas pipeline corrosion and corrosion detection technology related theory.Based on the investigation and evaluation of the anti-corrosion condition of the buried gas pipeline in Beijing City,this paper,based on the expert opinions and literatures,identified the types of corrosion,the number of years of operation,the depth of the pipe,soil resistivity,DC stray current,The six parameters of the insulation resistance of the coating are the evaluation indexes of the corrosion resistance of the buried gas pipeline.The RBF neural network is used to evaluate the corrosion resistance of the buried gas pipeline in Beijing,and the accuracy is verified.At the same time,the SVM support vector machine is used to locate the damage of the buried gas pipeline,and the damage of the sample pipeline is judged accurately.The gas pipeline is managed by the gas group,and the defect point is found,To strengthen the daily management of buried gas pipeline provides a certain reference value...
Keywords/Search Tags:Buried gas pipeline anti-corrosion, RBF neural network, SVM support vector machine, Antiseptic assessment, Damage point identification
PDF Full Text Request
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