Modeling thermo-physical properties of hydrogen utilizing machine learning schemes: Viscosity, density, diffusivity, and thermal conductivity.

Hydrogen is crucial in the forthcoming low-carbon energy systems offering the potential to reduce CO 2 emissions and its versatility to be utilized across diverse energy sectors. Precise assessment of hydrogen thermo-physical characteristics is vital for the effective design and execution of numerous processes encompassing hydrogen production, transportation, storage, and utilization. In this paper, various distinct machine learning (ML) approaches, namely extreme gradient boosting (XGBoost), Random Forest (RF), Adaptive boosting (AdaBoost), and Light gradient boosting machine (LightGBM) were implemented to estimate thermo-physical properties, namely thermal conductivity, density, viscosity, and diffusivity in water based on pressure and temperature variables. For this purpose, comprehensive experimental data points covering a broad range of pressures and temperatures were acquired from the literature. The results demonstrated a strong agreement between the predictions generated by all proposed techniques and the experimental data. Furthermore, XGBoost yielded a root mean square error (RMSE) of 0.0085, 2.1548, 0.3343, and 1.5308 for the estimation of thermal conductivity, density, viscosity, and diffusivity, respectively, demonstrating superior accuracy in predicting all outcomes. In the sensitivity evaluation, it was observed that temperature, with absolute relevancy factor 0.88, 0.54, 0.246, and 0.92 had the most significant influence on the hydrogen thermo-physical properties in the order mentioned above. Furthermore, the trend analysis of the model indicated that all thermo-physical properties of hydrogen experience positive effects from temperature and pressure, except for density, which decreases as temperature increases. The leverage technique confirmed the statistical validity of both the experimental dataset used for modeling and the model's development. This study's results provide the efficient design and secure operation of hydrogen storage, refueling stations, transportation infrastructure, and production facilities. [Display omitted] • Hydrogen holds great importance in the forthcoming low-carbon energy. • XGBoost, RF, AdaBoost, and LightGBM are utilized for modeling the thermo-physical properties of hydrogen. • XGBoost demonstrates its superior accuracy in predicting thermal conductivity, density, viscosity, and diffusivity. • Temperature has the most significant influence on the hydrogen thermo-physical properties. [ABSTRACT FROM AUTHOR]