Prediction of hydrogen leakage location and intensity in hydrogen refueling stations based on deep learning

Abstract

The common risk of high-pressure hydrogen leakage seriously affects the safety of hydrogen refueling stations (HRS). In this study, we have developed a hybrid model (WD-KNN-CNN) based on wavelet denoising (WD) and deep learning to predict the hydrogen leakage location and intensity in HRS. The raw data were calculated using validated Computational Fluid Dynamics (CFD), and the WD-processed raw data were utilized as input data for the hybrid model. The K-nearest Neighbors (KNN) is employed to identify the leak location, while a two-dimensional Convolutional Neural Network (CNN) is utilized to extract temporal and spatial distribution features from the concentration sequence for intensity prediction. Bayesian optimization is applied to determine the optimal hyperparameter combination for the model. The results demonstrate that the model achieves a prediction accuracy of 99.14% for leak location and 97.42% for intensity level. Furthermore, through the reduction of sensor quantity, we tested the model's performance and found that utilizing 14 sensors as input resulted in 91.3% accuracy for leak location and 90.8% for intensity level prediction. This hybrid model provides a method for safety monitoring in HRS and offers guidance for personnel response during leak incidents.