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Modeling of magnetic cooling power of manganite-based materials using computational intelligence approach

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Abstract

Magnetic refrigeration (MR) technology has been identified as a potential replacement for the gas compression system of refrigeration due to its environmental friendliness and high level of efficiency. This technology utilizes manganite-based materials as magnetic refrigerants due to the dependence of magnetic properties as well as relative cooling power (RCP) of this class of materials on the concentration and nature of the dopants. Quantifying the effect of dopants on RCP of manganite-based materials requires a long experimental procedures and techniques that are costly and time-consuming. In order to circumvent these challenges, we propose a model, based on support vector regression (SVR), which quickly estimates the RCP of doped manganite-based materials with high level of accuracy using crystal lattice constants as descriptors. The accuracy and ease with which the proposed SVR-based model estimates RCP of doped manganite-based materials is very promising and effective in designing MR system of desired RCP.

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Authors and Affiliations

  1. Physics Department, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia

    Taoreed O. Owolabi & Luqman E. Oloore

  2. Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria

    Taoreed O. Owolabi

  3. Physics and Engineering Physics Department, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria

    Luqman E. Oloore

  4. Electrical Engineering Department, University of Edinburgh, Edinburgh, UK

    Kabiru O. Akande

  5. Computer Science Department, University of Dammam, Dammam, Kingdom of Saudi Arabia

    Sunday O. Olatunji

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  1. Taoreed O. Owolabi
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  2. Luqman E. Oloore
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  3. Kabiru O. Akande
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  4. Sunday O. Olatunji
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Correspondence to Luqman E. Oloore.

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Owolabi, T.O., Oloore, L.E., Akande, K.O. et al. Modeling of magnetic cooling power of manganite-based materials using computational intelligence approach. Neural Comput & Applic 31 (Suppl 2), 1291–1298 (2019). https://doi.org/10.1007/s00521-017-3054-0

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  • DOI: https://doi.org/10.1007/s00521-017-3054-0

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