Machine learning-based corrosion prediction in supercritical carbon dioxide transport pipelines: Model evaluation and experimental validation

The repurposing of existing oil and gas pipelines for carbon dioxide (CO 2 ) transportation in carbon capture, utilization, and storage (CCUS) systems is gaining momentum. In these systems, CO 2 is transported in a supercritical state (s-CO 2 ), often containing aggressive impurities such as water (H 2 O), oxygen (O 2 ), and acidic gases, which significantly increases corrosion risk, necessitating robust corrosion management strategies. Traditional corrosion evaluation and prediction methods are often time-consuming and costly, making machine learning (ML) a promising alternative for predicting complex corrosion scenarios. This study evaluates the potential of four ML classifiers, including random forest (RF), gradient boosting classifier (GBC), support vector machine (SVM), and K-nearest neighbors (KNN), for corrosion severity prediction in pipeline steels exposed to s-CO 2 environments with varying impurity compositions. The models were trained on data comprising of temperature, pressure, exposure duration, and impurity type/concentration. Experimental validation was conducted to assess model reliability. Data preprocessing and domain-specific knowledge were identified as critical to improving model performance. Among the classifiers, RF exhibited the highest predicted accuracy (81.0%) and an F1 score of 0.798. Utilizing the most important 80% of features further improved the RF model's accuracy (85.7%) and F1 score (0.837). Feature importance analysis identified the interaction between H 2 O and sulfur dioxide (SO 2 ), as well as SO 2 content alone, as the most critical parameters underscoring the need for further corrosion assessments in s-CO 2 systems containing SO 2 . This study highlights the promise of ML, particularly the RF classifier, for efficient and reliable corrosion prediction in CCUS pipeline applications, supported by experimental validation.