Modeling of necking area reduction of carbon steel in hydrogen environment using machine learning approach

Abstract

Low carbon and low alloy steel pipes, prevalent in natural gas transmission systems due to their affordability, weldability, and strength, are confronted with significant challenges such as hydrogen embrittlement (HE) when transitioning towards hydrogen energy systems. This necessitates innovative predictive strategies to overcome these hurdles. Most existing research on HE has focused on a limited range of low carbon and low alloy steels under specific experimental conditions and has been constrained by the limitations of experimental facilities. To expand the research scope, our study incorporated seven machine learning (ML) techniques: Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Artificial Neural Networking (ANN). The aim is to predict the HE in terms of the degradation of mechanical properties, specifically the reduction in area. Drawing from tensile test data obtained from 47 distinct low carbon and low alloy steels under pressurized hydrogen gas conditions, we constructed and evaluated a range of ML models, with the aim of identifying the most efficacious one for our study. Our results indicated that the CatBoost ML model offered the best prediction of the reduction in the area of these steels in a hydrogen environment. The CatBoost model provided a low Mean Absolute Error (MAE) of 7.32, Mean Square Error (MSE) of 83.78, Root Mean Square Error (RMSE) of 9.15, and a coefficient of determination (R2) value of 77.62% for the training data and 72.50% for the testing data. Furthermore, the CatBoost model identified hydrogen gas pressure and the steel's ultimate tensile strength as the most influential parameters, contributing 47.4% and 19.2% respectively to the prediction of HE. This research provides valuable insights into the development of advanced materials and infrastructure for efficient hydrogen gas transportation, supporting the broader shift towards a hydrogen-based energy economy.