64624 2025 eng 1389 1401 conferenceobject International Association for Hydrogen Safety (HySafe) 0 -- -- -- Flammability characteristics of hydrogen mixtures have been extensively investigated at different initial conditions(temperature and pressure). Based on the available experimental datasets, empirical and semi-empirical models are commonly used to calculate flammability limits in dependance to initial conditions and mixture composition to reduce the experimental effort. However, unevenly distributed empirical data and the complex non-linear relationship characteristics of these data present significant challenges to empirical flammability limits prediction methods under various mixture initial conditions. Moreover, the empirical models and semi-empirical models only cover some influencing parameters, respectively. To address these issues, the present study adapts a machine learning (ML) approach for improving the hydrogen-air/oxygen-inert gas mixture flammability limits prediction at different conditions with a holistic approach. A Multi-Layer Perceptron (MLP) model was trained, validated, and tested using key input features such as flammability state, initial mixture temperature, equivalence ratio, inert gas concentration, adiabatic flame temperature, and Lewis numbers. Data augmentation techniques were conducted on experimental datasets to improve the predictive capability of the model. The models’ performance was compared with empirical flammability limit prediction methods. The goal is to deliver fast, reliable, and more accurate predictions across different scenarios with a single prediction model. Most importantly, the machine learning approach offers a cost-effective and robust alternative to existing empirical flammability limit prediction methods, thus also reducing the experimental effort for explosion limits determination. Proceedings of 11th International Conference on Hydrogen Safety (ICHS) 2025 979 -12 -243 -0274 - 2 11th International Conference on Hydrogen Safety (ICHS) 2025 Seoul, South Korea 22.09.2025 26.09.2025 publish false true Josua Kondja Junias Kai Holtappels Christian Liebner Max Thewis Enis Askar Erasmus Shaanika eng uncontrolled Machine Learning eng uncontrolled Flammability Limits Angewandte Physik 2 Prozess- und Anlagensicherheit 2.1 Sicherheit von Energieträgern Energie Datei im Netzwerk der BAM verfügbar ("Closed Access") Graue Literatur Wasserstoff