Although technically challenging, effective, safe, and economical transport is crucial for enabling a widespread rollout of hydrogen technologies. A promising option to transport large amounts of hydrogen lies in employing retrofitted natural gas pipelines. Nevertheless, H2-rich environments tend to degrade pipeline steels, reducing their load-bearing capability and accelerating crack propagation. Regular inspection and maintenance activities can preserve the pipelines’ integrity and guarantee safe operations. The risk-based inspection (RBI) approach is based on estimating the risk for each component item. It focuses most inspection activities on high-risk components to reduce costs while maximizing the plant’s safety and availability. However, the RBI standards do not consider hydrogen-induced degradations and cannot be adopted for industrial equipment operating in H2 environments. This study proposes a novel ad-hoc methodology for the risk-based inspection planning of hydrogen handling equipment. A machine-learning model to predict the fatigue crack growth in gaseous hydrogen environments is developed and integrated with the conventional RBI approach. The proposed methodology is validated on three pipelines transporting hydrogen and natural gas in different concentrations. The results show how similar operating conditions can determine different degradation rates depending on the environment and highlight how hydrogen-enhanced fatigue can reduce the pipelines’ lifetime.

中文翻译：

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机器学习辅助的基于风险的氢技术检查策略


尽管技术上具有挑战性，但有效、安全和经济的运输对于实现氢技术的广泛推广至关重要。运输大量氢气的一个有前途的选择是采用改造的天然气管道。然而，富含 H2 的环境往往会使管道钢降解，降低其承载能力并加速裂纹扩展。定期检查和维护活动可以保持管道的完整性并保证安全运行。基于风险的检查 （RBI） 方法基于估计每个组件项目的风险。它将大多数检查活动集中在高风险组件上，以降低成本，同时最大限度地提高工厂的安全性和可用性。然而，RBI 标准不考虑氢诱导降解，不能用于在 H2 环境中运行的工业设备。本研究为氢气处理设备的基于风险的检查计划提出了一种新的临时方法。开发了一种机器学习模型来预测气态氢环境中的疲劳裂纹增长，并将其与传统的 RBI 方法集成。所提出的方法在三条输送不同浓度的氢气和天然气的管道上进行了验证。结果显示了相似的操作条件如何根据环境确定不同的降解率，并突出了氢增强疲劳如何缩短管道的使用寿命。