Residual strength prediction of hydrogen-blended natural gas pipelines based on incremental knowledge distillation

Utilizing existing natural gas pipelines is an effective way for hydrogen energy transportation. However, hydrogen embrittlement can aggravate the material degradation, which can reduce the residual strength and pose a significant challenge to pipeline reliability. Consequently, in this paper, a residual strength prediction method is proposed for hydrogen-blended natural gas pipelines. Firstly, finite element analysis is utilized to investigate the failure pressure under different defect shapes, varying defect parameters and hydrogen concentrations. Secondly, a new feature space is constructed by integrating physically significant features tied to failure mechanism, and feature selection is conducted based on Pearson correlation coefficient. Then, a knowledge distillation model optimized by multi-objective improved hiking optimization algorithm (MOIHOA) is developed for residual strength prediction, the teacher-student framework is used to improve the computational speed and prediction performance. To overcome the challenge of limited hydrogen pipeline data, incremental learning mechanism is introduced for generalization of hydrogen-blended conditions. Finally, Shapley additive explanations (SHAP) is utilized to enhance the model interpretability. The results indicate that the proposed model can effectively predict the residual strength under hydrogen-blended conditions. This study provides a theoretical basis for reliability evaluation of hydrogen-blended natural gas pipelines.