Analyzing Hydrogen-Related Undesired Events: A Systematic Database for Safety Assessment

Hydrogen has the potential to channel a large amount of renewable energy from the production sites to the end users. Nevertheless, safety aspects represent the major bottleneck for its widespread utilization. The knowledge of past hydrogen-related undesired events is fundamental to avoid the occurrence of similar accidents in the future. Databases such as HIAD 2.0 and H2 Tools are dedicated to those accidents, but the scarcity of structured and quantitative information makes it difficult to apply advanced data-driven analyses based on Machine Learning (ML). In this paper, undesired events related to the hydrogen value chain were selected from the HIAD 2.0 and MHIDAS databases. These records were collected in a structured repository tool, namely Hydrogen-related Incident Reports and Analyses (HIRA). The definition of its features is based on a critical comparison of the primary reporting systems, and an analysis of the literature regarding H2 safety. Subsequently, text mining tools were used to analyze the event descriptions in natural language, extract relevant information and data, and sort them in the database. Finally, the new database was analyzed through Business Intelligence (BI) and ML classification tools. Data-driven analyses could help identifying valuable information about H2-related undesired events, promoting a safety culture, and improving accident management in the emerging hydrogen industry.