In this paper, a Phase Change Material (PCM) has been incorporated within the cabinet of a refrigerator, attached to the bare tube evaporator placed below the racks, with the aim of analysing the variation of temperatures and compressor operation, among others. The effect of different control settings (hysteresis) on the performance of such a system, equipped with tap water as PCM, is investigated. Furthermore, the impact of the variation of the ambient temperature and the product mass is highlighted. Two novel parameters are introduced: the first one to estimate the utilisation of the PCM during the cyclic operations of the refrigerator, and the second one is used to evaluate the fluctuation of the product temperature within the cabinet. Experimental results show that the introduction of the PCM has led to a noticeable reduction of the temperature gradient within the cabinet and the fluctuations of product temperature ensuring better conservation conditions, and to extend the OFF time of the compressor. The tests also show that with PCM, hysteresis has lower impact on the fluctuation of the product temperature, ONtime ratio and energy consumption of the refrigerator than without it. On the contrary, the product temperature distribution is more affected by the hysteresis with PCM than without, leading to a better uniformity with higher hysteresis.

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Phase Change Materials (PCMs) can absorb and release a high amount of energy, and therefore, can be used for shifting electricity demand in some household appliances. Indeed, the use of PCMs in a cabinet refrigerator leads to increase its OFF time and flexibility, and moves the energy consumption to other periods for a reduction of the peak energy demand. The higher flexibility of the refrigerator with PCM can be exploited to reduce its running cost considering that many countries adopted Time-of-Use (TOU) tariffs, in which the electricity cost is based on the time of energy use over a day and a week. Hence, this work proposes an algorithm based on Simulated Annealing (SA) method to identify the optimal working scheduling of a refrigerator with PCM in order to reduce its running costs. Experimental results have been used as inputs for the algorithm, and nine 2-TOU and three 3-TOU electricity tariffs from different European countries have been selected to test the methodology. The possibility to achieve running cost savings for various case studies has been proven. Furthermore, higher the difference between the peak and off-peak electricity cost, greater the economic benefits reached by the proposed method.

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The thermodynamic characterisation of magnetocaloric materials is an essential task when evaluating the performance of a cooling process based on the magnetocaloric effect and its application in a magnetic refrigeration cycle. Several methods for the characterisation of magnetocaloric materials and their thermodynamic properties are available in the literature. These can be generally divided into theoretical and experimental methods. The experimental methods can be further divided into direct and indirect methods. In this paper, a new procedure based on an artificial neural network to predict the thermodynamic properties of magnetocaloric materials is reported. The results show that the procedure provides highly accurate predictions of both the isothermal entropy and the adiabatic temperature change for two different groups of magnetocaloric materials that were used to validate the procedure. In comparison with the commonly used techniques, such as the mean field theory or the interpolation of experimental data, this procedure provides highly accurate, time-effective predictions with the input of a small amount of experimental data. Furthermore, this procedure opens up the possibility to speed up the characterisation of new magnetocaloric materials by reducing the time required for experiments.