Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods

Abstract

Spinel ferrite exhibits large magnetocaloric effect that promotes its candidature for solid state magnetic refrigeration technology. This method of cooling is promising and can conveniently replace the gas-compression method of refrigeration due to its energy saving capacity and ecological cleanliness. Among the available magnetocaloric materials, spinel ferrite metal oxide enjoys low production cost, non-toxicity, ease of preparation and non-oxidative. However, effective utilization of this magnetocaloric material for cooling technology requires determination of its magnetic cooling efficiency which is subjected to time consuming, intensive and laborious experimental procedures. This work employs the applied magnetic field, ionic radii and the concentrations of spinel ferrite constituents to develop a genetically hybridized support vector regression model (G-SVR) and extreme learning machine (ELM) intelligent model for determining magnetic cooling efficiency of spinel ferrite compounds. The developed G-SVR model outperforms ELM model for the testing set of spinel ferrite samples with percentage improvement of 60.57%, 19.69% and 64.73% using root mean square error, correlation coefficient and mean absolute error respectively, as performance measuring parameters. Influence of metal ions such as zinc, nickel and cadmium on magnetic cooling efficiency of various kinds of spinel ferrite compounds was investigated using the developed models. The presented intelligent approaches for spinel ferrite magnetic cooling efficiency determination in this work, with characteristic lower cost, quickness, effectiveness and precision would definitely facilitate and strengthen practical implementation of magnetic refrigeration technology for energy conservation and environmental friendliness.