Walk-Through Corrosion Assessment of Slurry Pipeline Using Machine Learning

Abstract

The study of pipeline corrosion is crucial to prevent economic losses, environmental degradation, and worker safety. In this study, several machine learning methods such as recursive feature elimination (RFE), principal component analysis (PCA), gradient boosting method (GBM), support vector machine (SVM), random forest (RF), K-nearest neighbors (KNN), and multilayer perceptron (MLP) were used to estimate the thickness loss of a slurry pipeline subjected to erosion corrosion. These different machine learning models were applied to the raw data (the set of variables), to the variables selected by RFE, and to the variables selected by PCA (principal components), and a comparative analysis was carried out to find out the influence of the selection and transformation of the data on the performance of the models. The results show that the models perform better on the variables selected by RFE and that the best models are RF, SVM, and GBM with an average RMSE of 0.017. By modifying the hyperparameters, the SVM model becomes the best model with an RMSE of 0.011 and an R-squared of 0.83.