Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel  

作　　者：Qi Zhuang Dong Liu Zhuo Chen 

机构地区：[1]PetroChina Changqing Oilfield Company,The Second Gas Production Plant,Xi’an,710000,China [2]PetroChina Changqing Oilfield Company,Safety and Environmental Supervision Department Co.,Ltd.,Xi’an,710000,China [3]Sinopec Northwest Oilfield Company,The Second Oil Production Plant Co.,Ltd.,Urumqi,830016,China

出　　处：《Energy Engineering》2024年第3期821-834,共14页能源工程（英文）

摘　　要：Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

关 键 词：Oil and gas pipeline corrosion defect failure pressure prediction sparrow search algorithm BP neural network logistic chaotic map 

分 类 号：TE973[石油与天然气工程—石油机械设备] TP39[自动化与计算机技术—计算机应用技术]