Research On The Prediction Of Remaining Life Of Buried Corrosion Pipeline Based On Improved Intelligent Optimization Algorithm

With the increase of the service time of the buried oil and gas pipelines in my country,the pipeline failure caused by external corrosion of the pipeline has caused major losses such as human property.In order to ensure the safe operation of buried pipelines,the corrosion rate and remaining life of pipelines in the pipeline system should be focused on research.Therefore,the use of scientific and reasonable methods to predict the corrosion of the buried pipeline is of great significance for the safe operation and risk control of my country’s oil and gas pipelines.On the basis of the current research of oil and gas pipelines,first study the influencing factors of buried oil and gas pipelines.The corrosion factors of the pipeline have been sorted out and the influencing factor indicator system is constructed.Combined with the detection data of soil burial experiments,the use of entropy power gray correlation analysis method is used to eliminate the influencing factors that affect the weaker pipeline corrosion,and retain the nine strong related factors with the values of gray correlations greater than 0.9,simplify the corrosion rate rate,and simplify the corrosion rate Enter the sample.Secondly,the corrosion rate prediction of the buried corrosion pipeline is performed.The butterfly optimization algorithm and nuclear limit learning machine model were introduced.On this basis,three improved strategies are proposed to optimize the optimization algorithm that is prone to fall into the optimal part.And use multi-strategic improved butterfly optimization algorithms to find the parameters of the nuclear limit learning model,analyze the effectiveness of the improvement of the effectiveness of the test function and time complexity analysis,and finally build the IBOA-KELM corrosion rate prediction model.Combined with the buried experimental data to verify the accuracy of the predictive model of the corrosion rate to locate the corrosion verification pipe section.Finally,the remaining life forecast of buried corrosion pipelines.The multi-strategic improved sparrow search algorithm combines with the minimum daily support vector machine algorithm to build the MSSA-LSSVM model to predict the depth of corrosion of the weak pipe segment.The two-stage corrosion depth combined with the power function model determines the corrosion trend of the weak pipe segment.According to the Asme-B31G surplus intensity evaluation standard,the maximum allowed corrosion depth determination criteria can be achieved,and the surplus life forecast of the buried corrosion pipeline is achieved.Eventually determine the corrosion pipe section below the design life,and put forward operation and maintenance suggestions.Through the actual case,the model of this study has a high predictive accuracy and strong generalization performance of the corrosion of the buried corrosion pipeline and the prediction of the remaining life.Pipeline safety operation,scientific management and risk prevention provides a reference basis.