Hydrogen Mixtures Flammability Limits Prediction Using Machine Learning Models
References
- Abdelkhalik, A., Askar, E., Markus, D., Stolz, T., Brandes, E. and Zakel, S. (2021) ‘Explosion regions of 1,3-dioxolane/nitrous oxide and 1,3-dioxolane/air with different inert gases – Experimental data and numerical modelling’, Journal of Loss Prevention in the Process Industries, 71, p.
104496 . Available at: 10.1016/j.jlp.2021.104496 - Ale, B.B. (1998) Flammability limits of gaseous fuels and their mixtures in air at elevated temperatures. Doctoral thesis, University of Calgary.
- Antonini, A.S., Tanzola, J., Asiain, L., Ferracutti, G.R., Castro, S.M., Bjerg, E.A. and Ganuza, M.L. (2024) ‘Machine learning model interpretability using SHAP values: Application to Igneous Rock Classification task’, Applied Computing and Geosciences, 23, p.
100178 . Available at: 10.1016/j.acags.2024.100178 - Arnaldos, J., Casal, J. and Planas-Cuchi, E. (2001) ‘Prediction of flammability limits at reduced pressures’, Chemical Engineering Science, 56(12), 3829–3843. Available at: 10.1016/S0009-2509(01)00090-2
- Bade, S.S. (1993)
‘Systematic approach to calculations of flammability limits of fuel-diluent mixtures in air’ , Thesis, The Iniversity of Calgary. Calgary, Alberta - CHEMSAFE. (2019) CHEMSAFE—Database for Recommended Safety Characteristics, BAM, PTB, and DECHEMA. Available at: http://www.dechema.de/en/chemsafe.htm
- Dwyer, J., Hansel, J.G. and Philip, T. (2003) ‘Temperature influence on the flammability limits of heat treating atmospheres. Proceedings of the 22nd heating treating society conference and the 2nd international surface engineering congress’, Indianapolis.
- Erdogan, K. (2013) ‘Spline interpolation techniques’, Journal of Technical Science and Technologies, 2(1), pp. 47–52. Available at: 10.31578/jtst.v2i1.56
- Gasse, A.S. (1992) Experimentelle Bestimmung und Simulation von Explosionsgrenzen, undersucht an wasserstoffhaltigen Brenngasgemischen. Dissertation, Uni-GH Paderborn, Germany.
- Goodwin, D.G., Moffat, H.K., Schoegl, I., Speth, R.L. and Weber, B.W. (2018). ‘Cantera: An object-oriented software toolkit for chemical kinetics, thermodynamics, and transport processes’, Zenodo.
- Hattwig, M. and Steen, H. (2008) Handbook of explosion prevention and protection. Chichester, UK: John Wiley & Sons.
- Henriksen, M. and Bjerketvedt, D. (2025) ‘Explosive characteristics of hydrogen-air mixtures diluted with an inert gas’, Hydrogen Safety, 2(1), pp, 68–75. Available at: 10.58895/hysafe.26
- Jiao, Z., Yuan, S., Zhang, Z. and Wang, Q. (2020) ‘Machine learning prediction of hydrocarbon mixture lower flammability limits using quantitative structure-property relationship models’, Process Safety Progress, 39(2), p.
e12103 . Available at: 10.1002/prs.12103 - Kaur, M., Chauhan, K.K., Aggarwal, T., Bharadwaj, P., Vig, R., Ihianle, I.K., Joshi, G. and Owa, K. (2023)
‘Taguchi based design of sequential convolution neural network for classification of defective fasteners’ , Sentiment analysis and deep learning, Singapore: Springer. Available at: 10.1007/978-981-19-5443-6_39 - Kumar, R.K. (1985) ‘Flammability limits of hydrogen-oxygen-diluent mixtures’, Journal of Fire Sciences, 3(4), pp. 245–262. Available at: 10.1177/073490418500300402
- Lazić, L. (2013) ‘Use of orthogonal arrays and design of experiments via Taguchi methods in software testing’, Recent Advances in Applied and Theoretical Mathematics, 23(2), pp. 256–267. Available at: 10.1007/s11219-014-9234-6
- Li, J., Liang, W. and Han, W. (2024) ‘Predicting the explosion limits of hydrogen-oxygen-diluent mixtures using machine learning approach’, International Journal of Hydrogen Energy, 50, pp. 1306–1313. Available at: 10.1016/j.ijhydene.2023.10.204
- Liaw, H.-J. and Chen, K.-Y. (2016) ‘A model for predicting temperature effect on flammability limits’, Fuel, 178, pp. 179–187. Available at: 10.1016/j.fuel.2016.03.034
- Lubo-Robles, D., Devegowda, D., Jayaram, V., Bedle, H., Marfurt, K.J. and Pranter, M.J. (2020) ‘Machine learning model interpretability using SHAP values: Application to a seismic facies classification task’, SEG International Exposition and Annual Meeting. Available at: 10.1190/segam2020-3428275.1
- Marcílio, W.E. and Eler, D.M. (2020) ‘From explanations to feature selection: assessing SHAP values as feature selection mechanism’, 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). Available at: 10.1109/SIBGRAPI51738.2020.00053
- Mashuga, C.V. and Crowl, D.A. (1999) ‘Flammability zone prediction using calculated adiabatic flame temperatures’, Process Safety Progress, 18(3), pp. 127–134. Available at: 10.1002/prs.680180303
- Mendiburu, A.Z., Carvalho, J.A.Jr. and Ju, Y. (2023) ‘Flammability limits: A comprehensive review of theory, experiments, and estimation methods’, Energy & Fuels, 37(6), pp. 4151–4197. Available at: 10.1021/acs.energyfuels.2c03598
- Molnarne, M., Schendler, T. and Schröder, V. (2008) Safety characteristic data volume 2: Explosion regions of gas mixtures. Bremerhaven: NW-Verlag.
- Munger, T. and Desa, S. (2021) ‘A statistical design of experiments approach to machine learning model selection in engineering applications’, Journal of Computing and Information Science in Engineering, 21(1), p.
011008 . Available at: 10.1115/1.4047915 - Pan, Y., Jiang, J., Wang, R., Cao, H. and Cui, Y. (2009) ‘A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine’, Journal of Hazardous Materials, 168(2), pp. 962–969. Available at: 10.1016/j.jhazmat.2009.02.122
- Qi, C., Yan, X., Wang, Y., Ning, Y., Yu, X., Hou, Y., Lv, X., Ding, J., Shi, E. and Yu, J. (2022) ‘Flammability limits of combustible gases at elevated temperatures and pressures: recent advances and future perspectives’, Energy & Fuels, 36(21), pp. 12896–12916. Available at: 10.1021/acs.energyfuels.2c02567
- Standardisation. (2017) ‘Determination of explosion limits of gases and vapours’, DIN EN Standard 1839.
- Standardization. (2017) ‘Gas cylinders — Gases and gas mixtures — Determination of fire potential and oxidizing ability for the selection of cylinder valve outlets’, ISO Standard 10156.
- Terpstra, M. (2012) Flammability limits of hydrogen-diluent mixtures in air. Thesis, The University of Calgary.
- Yuan, S., Jiao, Z., Quddus, N., Kwon, J.S.,
II . and Mashuga, C.V. (2019) ‘Developing quantitative structure–property relationship models to predict the upper flammability limit using machine learning’, Industrial & Engineering Chemistry Research, 58(8), pp. 3531–3537. Available at: 10.1021/acs.iecr.8b05938
Language: English
Page range: 84 - 96
Submitted on: Feb 6, 2026
Accepted on: Mar 18, 2026
Published on: Apr 9, 2026
Published by: KIT Scientific Publishing
In partnership with: Paradigm Publishing Services
Keywords:
© 2026 Josua Kondja Junias, Erasmus Shaanika, Kai Holtappels, Christian Liebner, Max Thewis, Enis Askar, published by KIT Scientific Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.