4.7 Article

Mass transfer performance inside Ca-based thermochemical energy storage materials under different operating conditions

Journal

RENEWABLE ENERGY
Volume 205, Issue -, Pages 340-348

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2023.01.091

Keywords

Thermochemical energy storage; Porous media; Lattice Boltzmann method; Machine learning

Ask authors/readers for more resources

This study investigates the synergetic effect of complex pore structures and different operating conditions on the micro-flow diffusion mass transfer performance inside CaO materials. It is found that the effective gas diffusion coefficient increases with increasing porosity, and decreases with an increase in fractal dimension. A prediction model based on machine learning is proposed to better understand this synergetic effect, which shows better performance than the traditional Maxwell model. This model may provide theoretical guidance for the design of Ca-based materials with high performance, and it could also be used in reactor design or system thermodynamic investigations.
CaCO3/CaO is a promising thermochemical energy storage material to achieve the continuous and stable operation of renewable energy, due to its unique merits such as high energy storage density and long storage time. However, it makes stringent demands on mass transfer to obtain a high energy discharging performance. This study investigated the synergetic effect of complex pore structures and different operating conditions on the micro-flow diffusion mass transfer performance inside CaO materials. We found that the effective gas diffusion coefficient increased with increasing porosity, and decreased with an increase in fractal dimension. In addition, under different operating conditions, the difference in the effective gas diffusion coefficient inside CaO materials with high porosity and low fractal dimensions was much larger than that inside CaO materials with low porosity and high fractal dimensions. This indicated that the synergetic effect should be considered for mass transfer performance inside CaO materials with high porosity and low fractal dimensions. To better understand this synergetic effect, a prediction model was proposed based on machine learning, where its average error was around 12%, and the root means square error was around 0.04138, which was better than that of the traditional Maxwell model. This proposed model may provide theoretical guidance for the design of Ca-based materials with high performance, and it could also be used in reactor design or system thermodynamic investigations.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available