Performance analysis of a metal hydride refrigeration system

The varying applications of metal hydride refrigeration systems, such as cold storage and space air conditioning, grant them important advantages over conventional ones. These advantages include being a low-grade heat driven, more environmentally friendly and renewable working fluid with greater compactness and fewer moving parts. However, a metal hydride refrigeration system always operates under unsteady conditions due to the cyclic hydriding and dehydriding processes involved. To analyse and optimise the metal hydride refrigeration system's design and performance, in this paper, a comprehensive transient system model has been developed with a new and revised intrinsic kinetic correlation inclusive of the essential operating controls and applicable process conditions of regeneration, cooling and transitions in between. In addition, the correlative model on the characterisation process of pressure, concentration and temperature (PCT) profiles for the metal hydride alloys employed in the system has been developed and is introduced briefly in this paper. It is integrated in the system model and ensures the accurate prediction of maximum capacities for the metal hydride isothermal desorption and absorption processes. The developed transient system model has been validated through comparison with experimental results from literature on the medium-temperature cooling process of a metal hydride refrigeration system. The model simulation is conducted for a specially designed low-temperature metal hydride refrigeration system at different operating conditions and controls. In quantity, when the high-grade heat source temperature increases from 90 degrees C to 120 degrees C, the low-grade heat source temperature increases from 20 degrees C to 10 degrees C, the medium-grade heat sink temperature decreases from 30 degrees C to 15 degrees C, and the time period for regeneration or cooling process decreases from 10 min to 4 min, the cooling COP increases by 112.0%, 136.6%, 19.3% and 31.8% respectively. The optimisation strategies for the system operating conditions and controls are therefore recommended based on the detailed performance analyses of the system simulation results.