Open Access

ARTICLE

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

Qi Zhuang^1,*, Dong Liu², Zhuo Chen³

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

* Corresponding Author: Qi Zhuang. Email:

Energy Engineering 2024, 121(3), 821-834. https://doi.org/10.32604/ee.2023.044054

Received 19 July 2023; Accepted 13 October 2023; Issue published 27 February 2024

Abstract

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.

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