Data-augmented deep learning for hazard assessment of hydrogen accumulation in confined spaces: Multitask prediction and sensitivity analysis

Hydrogen’s high energy efficiency and environmental cleanliness position it as a key solution for sustainable energy systems. However, its high diffusivity and flammability pose significant safety risks in confined spaces, where leaks may lead to hazardous accumulations of gas. This study presents a comprehensive framework that integrates advanced data augmentation and multitask learning to address these challenges, bridging gaps in conventional risk assessment methods. By leveraging augmented data, which shows a 92.3 % increase in diversity, along with a multitask model, the framework achieves exceptional predictive performance, with $R^2$ values reaching 0.999 and F1-scores exceeding 0.99. It also demonstrates resilience to noise, significantly surpassing conventional methods while reducing computational demands. Key findings highlight the critical influence of factors such as orifice dimensions, building area, and operating pressure on hydrogen accumulation risks, suggesting actionable strategies for safer hydrogen facility design and management. The proposed data-driven and interpretable framework offers a transformative approach to enhancing safety and reliability in hydrogen energy systems, tackling critical challenges in clean energy deployment.