A Novel Prediction Method for the Remaining Life of Corrosion Damage in Oil and Gas Pipeline Systems - Jian Cui; Jie Wang; Lei Dong (IEEE Access)
During long-term operation, oil and gas pipelines are often subjected to corrosion caused by complex environmental factors, which can lead to serious safety incidents. Therefore, accurately assessing the extent of corrosion damage and predicting the remaining life of pipelines has become a critical issue in the safety management of oil and gas pipelines. This paper aims to propose a high-precision corrosion rate prediction model and, based on this model, provide an accurate evaluation of the pipeline’s remaining life to enhance the operational safety and reliability of oil and gas pipelines. First, the MFO-LSSVM model is employed to precisely predict the corrosion rate of oil and gas pipelines. This model combines the Moth-Flame Optimization (MFO) algorithm and Least Squares Support Vector Machine (LSSVM), which significantly improves the prediction accuracy under complex environmental conditions. Building on this, a combination of Long Short-Term Memory (LSTM) and Deep Neural Networks (DNN) is used to predict the remaining life of the pipelines, capturing the time-dependent and nonlinear characteristics of the corrosion rate. Experimental results demonstrate that the MFO-LSSVM model achieves outstanding accuracy in corrosion rate prediction, with a root mean square error (RMSE) of 0.0023 and a mean absolute error (MAE) of 0.17%. For the remaining life prediction, the LSTM-DNN model shows a mean square error (MSE) of 0.0004 and a coefficient of determination (R2) of 0.9999, significantly outperforming other individual models and closely aligning with the actual values. This multi-stage prediction framework effectively enhances the model’s robustness and accuracy in complex environments. The innovation of this study lies in the development of a multi-stage prediction framework combining the MFO-LSSVM and LSTM-DNN models. The MFO-LSSVM model provides highly accurate corrosion rate predictions, while the LSTM-DNN model captures the time-evolution and nonlinear effects, leading to improved precision in corrosion damage assessment and remaining life prediction. This approach effectively overcomes the limitations of traditional models in complex environments and offers both theoretical support and practical value for the safety management of oil and gas pipelines.