Next Article in Journal
Effect of Strains and V-Shaped Pit Structures on the Performance of GaN-Based Light-Emitting Diodes
Next Article in Special Issue
Comparative Study on the Activity of GaF3 and Ga2O3 Nanoparticle-Doped CsF-AlF3 Flux for Brazing 6061 Al/Q235 Steel Joints
Previous Article in Journal
Response Improvement of Liquid Crystal-Loaded NRD Waveguide Type Terahertz Variable Phase Shifter
Previous Article in Special Issue
Inducing the Effect of a Ga2O3 Nano-Particle on the CsF-RbF-AlF3 Flux for Brazing Aluminum to Carbon Steels
Article

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

1
Department of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, P.O. Box 1803, Hafr Al Batin 31991, Saudi Arabia
2
Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko 342111, Ondo State, Nigeria
*
Author to whom correspondence should be addressed.
Crystals 2020, 10(4), 310; https://doi.org/10.3390/cryst10040310
Received: 1 April 2020 / Revised: 12 April 2020 / Accepted: 14 April 2020 / Published: 16 April 2020
(This article belongs to the Special Issue Intermetallic Compound)
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. View Full-Text
Keywords: magnetocaloric effect; support vector regression; extreme learning machine; maximum magnetic entropy change; gravitational search algorithm magnetocaloric effect; support vector regression; extreme learning machine; maximum magnetic entropy change; gravitational search algorithm
Show Figures

Figure 1

MDPI and ACS Style

Shamsah, S.M.I.; Owolabi, T.O. Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression. Crystals 2020, 10, 310. https://doi.org/10.3390/cryst10040310

AMA Style

Shamsah SMI, Owolabi TO. Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression. Crystals. 2020; 10(4):310. https://doi.org/10.3390/cryst10040310

Chicago/Turabian Style

Shamsah, Sami M.I., and Taoreed O. Owolabi 2020. "Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression" Crystals 10, no. 4: 310. https://doi.org/10.3390/cryst10040310

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop