Hydrogen pipelines (HPL) are one of the hydrogen transportation systems for realizing a hydrogen society. Hydrogen leakage from HPL is a challenge because hydrogen has a wide flammable range and low minimum ignition energy. Thus, hydrogen leakage from the HPL must be rapidly detected, and appropriate actions should be taken. Leakage detection is important for safe operation of HPL. The basic leakage detection method for HPL involves monitoring the pressure and flow rate values of the sensors. However, in some cases, it is difficult to distinguish between non-leakage and leakage conditions using this method. In this study, we focus on a leakage detection method using machine learning (ML) based on the relationship between pressure and flow rate data. There are two challenges in applying the ML- based leak detection method to an HPL. First, there are insufficient operational data for ML during the process- design stage. Secondly, it is difficult to obtain the pressure and flow rate behaviors during hydrogen leakage because leakage does not occur frequently. Consequently, this study employed an unsupervised ML method based on data simulated using a physical model of the HPL. First, a physical model of the HPL (HPL model) was constructed, and an ML model for leak detection was constructed based on the data simulated by the HPL model. The leak detection capability of the ML model was verified by comparing the anomaly scores of the non-leakage and leakage conditions. From the results, the ML model can distinguish between non-leakage and leakage behaviors and identify leakage points under certain conditions. This method can contribute to the optimization of the sensors required for leak detection during the process design stage.
hydrogen pipeline, leak detection, machine learning
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