An application of the artificial neural network to optimise the energy performances of a magnetic refrigerator

This paper treats a control technique designed to maximise the working of a rotary permanent magnet magnetic refrigerator (RPMMR). The method, named ANNTEO, is based on the use of the artificial neural networks (ANNs), which have demonstrated to predict well the energy performances of an actual RPMMR obtaining a good agreement with the experimental tests. The ANN gives the possibility to carry out a working map, and then, applying an optimisation process, it is possible to catch the optimal working point regarding the number of revolution of the magnets per minute and the volumetric flow rate of the regenerating fluid. In particular, the optimisation can be processed with the aim to maximise the COP (energy saving) or the cooling capacity (time-saving). As a proof of the concept, this paper reports an example of an application of the ANNTEO. Also, new perspectives on the use of the ANNs in the magnetic refrigeration field are proposed.