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Corrosion Rate Prediction And Residual Life Of Buried Oil And Gas Pipelines

Posted on:2020-08-08Degree:MasterType:Thesis
Country:ChinaCandidate:W H WangFull Text:PDF
GTID:2381330626451706Subject:Information management and information systems
Abstract/Summary:PDF Full Text Request
Oil and natural gas have an extremely important position in China's energy strategy.However,with the increase of pipeline construction in China and the long service life of old pipelines,pipeline failures occur frequently,causing major property losses and casualties,and oil and gas pipeline safety issues.Caused widespread concern.The buried buried pipeline buried in the ground,the concealing of corrosion failure and the expensive maintenance cost determine that the pipeline safety construction not only requires hardware technology innovation,but also requires fundamental changes in theoretical knowledge and management mode.Therefore,it is particularly important to study the corrosion failure of buried pipelines and predict the health of pipelines in advance.Based on the analysis of the current common detection techniques of buried pipelines and the advantages and disadvantages of corrosion prediction methods,combined with multi-source data analysis and data mining methods,this paper mainly carried out the following three aspects:(1)Analysis of corrosion factors of buried oil and gas pipelines,combined with accident tree theory,analyzes the causes of corrosion failure of buried oil and gas pipelines,and uses the rough set theory to select the characteristics of buried pipeline corrosion factors.The main factors affecting the corrosion of the pipeline are extracted and the importance is sorted.(2)Prediction of corrosion rate of buried oil and gas pipelines,improving the inertia weight and learning factor of traditional particle swarm optimization algorithm,optimizing the parameters of generalized regression neural network,constructing prediction model of buried pipeline corrosion rate,and other cluster intelligent algorithms Compared with the comparison,the improved particle swarm optimization algorithm has better ability to optimize and converge.Compared with BP model and SVM model,the prediction accuracy of the model is higher.(3)Prediction of residual life of buried oil and gas pipelines,and the calculation method of maximum allowable corrosion depth of different pipeline sections of buried pipelines,establishing prediction model of buried pipeline corrosion depth under different time and different environments,combined with corrosion development of buried pipelines Predict the corrosion residual life of the buried weak pipe section.The practical application shows that the model is consistent with the actual situation and can better reflect the corrosion development trend of the buried pipeline.Due to the limitations of the conditions,this study still has defects.For instance,the buried film test does not consider the active protective effect of the anti-corrosion layer.Although the internal corrosion of the pipeline has little effect,it will weaken the prediction accuracy of the model and comprehensively consider the influence of internal and external corrosion of the buried pipeline.It is also the focus of future research.
Keywords/Search Tags:Buried oil and gas pipelines, Particle swarm optimization, Generalized regression neural networks, Corrosion rate prediction, Residual life research
PDF Full Text Request
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