TY  - CONF
A1  - Junias, Josua Kondja
A1  - Holtappels, Kai
A1  - Liebner, Christian
A1  - Thewis, Max
A1  - Askar, Enis
A1  - Shaanika, Erasmus
T1  - Hydrogen Mixtures Flammability Limits Prediction using Machine Learning Models
T2  - Proceedings of 11th International Conference on Hydrogen Safety (ICHS) 2025
N2  - 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.
T2  - 11th International Conference on Hydrogen Safety (ICHS) 2025
CY  - Seoul, South Korea
DA  - 22.09.2025
KW  - Machine Learning
KW  - Flammability Limits
PY  - 2025
SN  - 979 -12 -243 -0274 - 2
SP  - 1389
EP  - 1401
AN  - OPUS4-64624
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -