Active Clean Energy Chemistry

Synergistic integration and efficient operation of thermochemical energy storage-assisted magnesium hydrides-based hydrogen storage systems

Summary

Original abstract (not yet simplified)

Currently, global energy consumption is growing rapidly, but over-reliance on fossil energy poses significant risks to the global ecosystem. It is urgent to establish a clean, safe and sustainable energy supply system. The large-scale development of green hydrogen energy is a key solution to address the challenges of greenhouse gas emissions and global climate change. However, its large-scale utilization requires...

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Currently, global energy consumption is growing rapidly, but over-reliance on fossil energy poses significant risks to the global ecosystem. It is urgent to establish a clean, safe and sustainable energy supply system. The large-scale development of green hydrogen energy is a key solution to address the challenges of greenhouse gas emissions and global climate change. However, its large-scale utilization requires the development of safe, reliable and convenient methods for hydrogen storage and transportation. Among the many hydrogen storage technologies, solid-state hydrogen storage technology based on magnesium hydrides (MgH2) stands out with its significant advantages, such as high hydrogen storage density (7.6wt%) and high operational safety. However, their high energy consumption in the dehydrogenation processes makes them less economical in practical scenarios. Therefore, this project will focus on synergistic integration and efficient operation of thermochemical energy storage-assisted MgH2-based solid hydrogen storage (TCES-MgH2) system to enhance energy efficiency in the hydrogen supply chain and reduce hydrogen storage costs. This project will combine mechanism analysis, numerical simulation and experimental research to select the most suitable TCES material to assist magnesium hydrides in absorbing and releasing hydrogen. It will use long short-term memory neural networks, heuristic algorithms, and model predictive control to optimize heat transfer structures and control strategies of the hydrogen storage system, achieving rapid heat transfer and efficient hydrogen desorption/absorption. Additionally, it will assessment the large-scall deployment potential of the TCES-MgH2 system by machine learning. The outcomes of this research will provide pivotal technologies for fostering a renewable energy society, driving energy structure transformation, and facilitating widespread adoption of hydrogen energy.

Related Research

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Efficient compact modular thermal energy storage system
Designing Novel High Capacity Multicomponent Hydrides for Near-Ambient Solid State Hydrogen Stores
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