Hydrogen autoignition under varying methane blends: Experimental analysis and prediction model development

Shuhong Li, Shiyao Peng, Zhenyi Liu*, Yao Zhao, Mingzhi Li, Pengliang Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigates the effects of methane volume fractions on hydrogen autoignition. Experiments are conducted on an autoignition platform with a cylindrical release tube, focusing on the impact of methane volume blending ratios (0 %, 10 %, 20 %, and 30 %) on critical autoignition pressure (Pcr), shock wave velocity, and flame morphology. Results show that methane significantly suppresses hydrogen autoignition. Under pure hydrogen conditions, the critical autoignition pressure (Pcr) is 4.44 MPa, and the average shock wave velocity is 1185.5 m/s. When 10 % methane is blended, the critical autoignition pressure (Pcr) markedly increases to 8.63 MPa (approximately twice that of pure hydrogen), while the shock wave velocity slightly increases to 1230.4 m/s. However, at 20 % and 30 % methane, autoignition is not observed even at pressures over 17 MPa. Theoretical model for shock-induced ignition is employed in combination with three commonly used reaction kinetics mechanisms: GRI 3.0, FFCM-1, and Aramco 2.0. The results indicate that the theoretical model exhibits significant deviations and is not well suited for predicting the autoignition behavior of methane-hydrogen mixtures. A GRNN model is developed by integrating experimental and literature data, achieving 72.55 % accuracy at low methane blending ratios, outperforming conventional models. This GRNN model provides a new approach for predicting autoignition criteria in methane-hydrogen mixtures, offering insights for safe discharge and storage tank design in industrial processes.

Original languageEnglish
Article number107245
JournalProcess Safety and Environmental Protection
Volume199
DOIs
Publication statusPublished - Jul 2025
Externally publishedYes

Keywords

  • Autoignition
  • GRNN prediction model
  • Methane-hydrogen mixture
  • Shock-induced ignition

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