Hydrogen Mixtures Flammability Limits Prediction using Machine Learning Models

  • 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,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.zeige mehrzeige weniger

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Autor*innen:Josua Kondja Junias, Kai HoltappelsORCiD, Christian Liebner, Max Thewis, Enis AskarORCiD, Erasmus Shaanika
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Proceedings of 11th International Conference on Hydrogen Safety (ICHS) 2025
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:2 Prozess- und Anlagensicherheit
2 Prozess- und Anlagensicherheit / 2.1 Sicherheit von Energieträgern
Herausgeber (Institution):International Association for Hydrogen Safety (HySafe)
Aufsatznummer:ID121, QRA
Erste Seite:1389
Letzte Seite:1401
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik
Freie Schlagwörter:Flammability Limits; Machine Learning
Themenfelder/Aktivitätsfelder der BAM:Energie
Energie / Wasserstoff
Veranstaltung:11th International Conference on Hydrogen Safety (ICHS) 2025
Veranstaltungsort:Seoul, South Korea
Beginndatum der Veranstaltung:22.09.2025
Enddatum der Veranstaltung:26.09.2025
ISBN:979 -12 -243 -0274 - 2
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:10.11.2025
Referierte Publikation:Nein
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