Abstract
The thermal response of a magnetic solid to an applied magnetic field constitutes magnetocaloric effect. The maximum magnetic entropy change (MMEC) is one of the quantitative parameters characterizing this effect, while the magnetic solids exhibiting magnetocaloric effect have great potential in magnetic refrigeration technology as they offer a green solution to the known pollutant-based refrigerants. In order to determine the MMEC of doped manganite and the influence of dopants on the magnetocaloric effect of doped manganite compounds, this work developed a grid search (GS)-based extreme learning machine (ELM) and hybrid gravitational search algorithm (GSA)-based support vector regression (SVR) for estimating the MMEC of doped manganite compounds using ionic radii and crystal lattice parameters as descriptors. Based on the root-mean-square error (RMSE), the developed GSA-SVR-radii model performs better than the existing genetic algorithm (GA)-SVR-ionic model in the literature by 27.09%, while the developed GSA-SVR-crystal model performs better than the existing GA-SVR-lattice model in the literature by 38.34%. Similarly, the developed ELM-GS-crystal model performs better than the existing GA-SVR-ionic model with a performance enhancement of 14.39% and 20.65% using the mean absolute error (MAE) and RMSE, respectively, as performance measuring parameters. The developed models also perform better than the existing models using correlation coefficient as the performance measuring parameter when validated with experimentally measured MMEC. The superior performance of the present models coupled with easy accessibility of the descriptors definitely will facilitate the synthesis of doped manganite compounds with a high magnetocaloric effect without experimental stress.
Highlights
Magnetocaloric compounds are of technological and scientific interest mainly because of their significance in magnetic refrigeration, which has shown high potential in replacing the conventional compression–expansion cycle of a gas cooling system [1,2,3,4]
Magnetocaloric effect can be observed in many compounds, while the application of these compounds in magnetic refrigeration technology is hindered by the appearance of the magnetocaloric effect at relatively high magnetic fields and/or at transition temperatures differing greatly from room temperature
The dependence of the convergence of the developed gravitational search algorithm (GSA)-support vector regression (SVR)-radii and GSA-SVR-crystal models to the initial number of agents exploring and exploiting the search space are presented in Figures 1 and 2, respectively
Summary
Magnetocaloric compounds are of technological and scientific interest mainly because of their significance in magnetic refrigeration, which has shown high potential in replacing the conventional compression–expansion cycle of a gas cooling system [1,2,3,4]. Manganite-based compounds combine many unique features that make them fit well into magnetic refrigeration technology [9] They demonstrate a high magnetocaloric effect at low applied magnetic fields, relatively cheap elemental compositions, high stability (especially in some corrosive environments) and physical parameters that can be tuned through doping mechanisms [5,10,11]. With the stochastic latent layer weights, the ELM still maintains universal approximation strength in acquiring the relationship between descriptors and targets These unique capacities of the ELM algorithm are explored in the present work for modeling the MMEC of doped manganite compounds using lattice parameters and ionic radii as descriptors.
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