1. Predicting hydrogen adsorption and desorption rates in cylindrical metal hydride beds: Empirical correlations and machine learning
- Author
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Chun-Sheng Wang and Joshua Brinkerhoff
- Subjects
Materials science ,Renewable Energy, Sustainability and the Environment ,Hydride ,business.industry ,Energy Engineering and Power Technology ,02 engineering and technology ,010402 general chemistry ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,Machine learning ,computer.software_genre ,Thermal diffusivity ,01 natural sciences ,Storage efficiency ,Isothermal process ,0104 chemical sciences ,Hydrogen storage ,Fuel Technology ,Adsorption ,Thermal conductivity ,Desorption ,Artificial intelligence ,0210 nano-technology ,business ,computer - Abstract
A major limitation in chemisorptive hydrogen storage in metal hydrides is the long time required for the adsorption reaction during charging. This study investigates how the shape and material of the reaction chamber influences the adsorption and desorption rates. Numerical simulations of hydrogen storage in a cylindrical reaction chamber filled with LaNi5 hydride are conducted for a range of chamber thermal conductivities and aspect ratios. The results show that adsorption and desorption processes are limited by thermal diffusion in the hydride bed and storage chamber. A storage efficiency is proposed based on an ideal isothermal process and used to evaluate the impact of chamber thermal conductivity and aspect ratio on the adsorption and desorption rates. Empirical correlations are proposed for predicting the adsorption and desorption efficiency of cylindrical LaNi5 hydride beds. Finally, a machine-learning based data model for predicting storage efficiency in metal hydride chambers is presented. Comparison against the empirical correlations highlights that the machine learning-based data model can predict the storage efficiency more accurately.
- Published
- 2021
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