Abstract
The transition towards green energy has intensified the focus on hydrogen as a clean energy, with hydrogen refueling stations (HRSs) becoming increasingly prevalent. Despite the potential benefits, the inherent risks associated with high-pressure hydrogen storage pose significant safety challenges. Effective emergency response planning is critical to mitigate the consequences of any potential accidents at HRSs. This study developed a deep learning-based model to predict the demand for emergency resources following an accident at an HRS, thereby enhancing the efficiency of emergency responses. A HRS case library was constructed encompassing a variety of accident scenarios, including characteristics, potential casualties, and corresponding emergency resource demands. By employing deep learning algorithms, correlations between accident consequences and emergency resource demands were established, enabling rapid predictions post-accident. The study evaluated various neural network models and found the Random Forest model to be the most accurate for predicting casualties and resource demands. To facilitate user accessibility, a graphical user interface was developed allowing non-technical emergency responders to input accident parameters and receive immediate predictions on casualties and emergency resource demands. The integration of deep learning with emergency response planning presents a significant advancement in risk mitigation strategies for HRSs.
Published Version
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