埋地燃气管道防腐性评估与破损点识别

发布时间：2018-01-03 04:45

本文关键词：埋地燃气管道防腐性评估与破损点识别　出处：《首都经济贸易大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着燃气的广泛普及,我国燃气管网已经进入了快速发展的阶段。但是因为埋地燃气管道长时间处在一个比较复杂的外部环境条件中,而且随着管道在役时间的不断增加,埋地燃气管道的外防腐层经常会发生腐蚀老化、破损的情况,最终会引起管道自身的腐蚀穿孔,进而发生燃气泄漏事故,不仅威胁着人民的生命和财产安全,还会带来严重的社会危害。因此,对埋地燃气管道的防腐层进行评估,并准确定位缺陷点,是保障埋地燃气管道安全运行的有效手段。本文阐述了埋地燃气管道发生腐蚀的形式和腐蚀检测技术的相关理论。在调研和参与北京市燃气集团对埋地燃气管道防腐状况检测和评估的基础上,结合专家意见,文献资料,确定了以防腐层种类、运行年数、管段埋深、土壤电阻率、直流杂散电流、防腐层绝缘电阻6个参数为埋地燃气管道防腐性的评估指标,运用RBF神经网络对北京市埋地燃气管道防腐性进行了综合评估,并验证了准确性。结果表明,RBF神经网络对埋地燃气管道腐蚀性评估的精度要优于北京市燃气集团主观地综合评价。同时,运用SVM支持向量机对埋地燃气管道的防腐层破损情况进行定位,准确判断出了样本管道的破损情况,为燃气集团管理埋地燃气管道,及时发现缺陷点问题,进行修补和改造,加强埋地燃气管道的日常管理提供了一定的参考价值。
[Abstract]:With the wide popularity of gas, gas network in China has entered a stage of rapid development. But because of buried gas pipeline for a long time in a relatively complex external environment conditions, and with the increase in service time of the pipeline, anticorrosion layer of buried gas pipeline corrosion often occurs in aging, damaged, eventually will cause the pipeline corrosion perforation of itself, and the occurrence of gas leakage accident, not only threatens the safety of people's lives and property, but also brings serious harm to the society. Therefore, evaluate the anticorrosion layer of buried gas pipeline, and accurate positioning of defects, is the effective means to ensure the safe operation of underground gas pipeline. This paper expounds the related the theory of form and corrosion detection technology of corrosion of buried gas pipeline. In the research and to participate in the Beijing Gas Group on the corrosion status of buried gas pipeline inspection The basic measurement and assessment, combined with expert opinion, literature, to determine the type of coating, the number of years of operation, deep buried pipe, soil resistivity, stray current, insulation resistance of anti-corrosion layer 6 parameters for the evaluation of buried gas pipeline corrosion protection, the use of RBF neural network is evaluated Beijing City buried gas pipeline corrosion, and verify the accuracy. The results show that the RBF neural network to comprehensive evaluation of Beijing gas group is better than the subjective corrosion of buried gas pipeline assessment accuracy. At the same time, the use of SVM to support the anticorrosion of underground gas pipeline damage layer vector machine positioning, accurately determine the damage situation the sample for buried gas pipeline pipeline, gas group management, timely detection of defects, repair and renovation, provides a certain reference value to strengthen the daily management of buried gas pipeline.

【学位授予单位】：首都经济贸易大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TU996.7

【参考文献】