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Existing natural gas pipelines can facilitate low-cost, large-scale hydrogen transportation and storage, but hydrogen may entail safety challenges. These challenges stem from hydrogen’s different properties compared to natural gas, such as higher ignition probability, different flame behavior, and potential for hydrogen embrittlement. Although risk assessments for hydrogen pipelines are increasing, the impact of hydrogen on the risk of third-party excavation damage (TPD), the major cause of pipeline incidents in the U.S., has received little attention. This work presents the SHyTERP model for Safe Hydrogen Transportation and Excavation Risk Prevention for Pipelines. The model incorporates causal models, excavation damage and pipeline failure statistics, and validated physical models of hydrogen and natural gas release and jet flame behavior. Through four case studies, the model compares the TPD risks of hydrogen and natural gas pipelines, offering insights and recommendations for the safe implementation of hydrogen in existing pipelines.
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Predicting Natural Gas Pipeline Failures Caused by Natural Forces: An Artificial Intelligence Classification Approach
Pipeline networks are a crucial component of energy infrastructure, and natural force damage is an inevitable and unpredictable cause of pipeline failures. Such incidents can result in catastrophic losses, including harm to operators, communities, and the environment. Understanding the causes and impact of these failures is critical to preventing future incidents. This study investigates artificial intelligence (AI) algorithms to predict natural gas pipeline failures caused by natural forces, using climate change data that are incorporated into pipeline incident data. The AI algorithms were applied to the publicly available Pipeline and Hazardous Material Safety Administration (PHMSA) dataset from 2010 to 2022 for predicting future patterns. After data pre-processing and feature selection, the proposed model achieved a high prediction accuracy of 92.3% for natural gas pipeline damage caused by natural forces. The AI models can help identify high-risk pipelines and prioritize inspection and maintenance activities, leading to cost savings and improved safety. The predictive capabilities of the models can be leveraged by transportation agencies responsible for pipeline management to prevent pipeline damage, reduce environmental damage, and effectively allocate resources. This study highlights the potential of machine learning techniques in predicting pipeline damage caused by natural forces and underscores the need for further research to enhance our understanding of the complex interactions between climate change and pipeline infrastructure monitoring and maintenance.
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- Award ID(s):
- 2119691
- PAR ID:
- 10422258
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 13
- Issue:
- 7
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 4322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app13074322
