Abstract

Chemical leakage accidents may lead to significant hazards such as fire, explosions and the release of toxic gases. And the estimation of real-time accident hazards is of critical importance for emergency response. In this study, a method was proposed to predict the gas flow in real-time based on the simulated database. Based on datasets generated by numerical computations, a machine-learning algorithm was trained and used to then be validated by data from real-world scenarios. Compared with the full-scale experimental data, for the scenarios in which the relative error of the simulation was within the range of 0%–25%, the relative error of the developed model prediction was 9.8%. When the error of the simulation was within the range of 0%–25%, the machine learning model trained by simulated data can predict the gas leakage in real world with high accuracy.

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