Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Abstract

External corrosion poses a significant threat to the integrity and lifespan of buried pipelines. Accurate prediction of corrosion rates is important for the safe and efficient transportation of oil and natural gas. However, limited data availability often impacts the performance of conventional predictive models. This study proposes a novel composite modeling approach integrating kernel principal component analysis (KPCA), particle swarm optimization (PSO), and extreme learning machine (ELM). The key innovation lies in using KPCA for reducing the dimensionality of complex input data combined with PSO for optimizing the parameters of the ELM network. The model was rigorously trained on 12 different datasets and comprehensively evaluated using metrics such as the coefficient of determination (R2), standard deviation (SD), mean relative error (MRE), and root mean square error (RMSE). The results show that KPCA effectively extracted four primary components, accounting for 91.33% of the data variability. The KPCA-PSO-ELM composite model outperformed independent models with a higher accuracy, achieving an R2 of 99.59% and an RMSE of only 0.0029%. The model comprehensively considered various indicators under the conditions of limited data. The model significantly improved the prediction accuracy and provides a guarantee for the safety of oil and gas transport.