Ensemble-based support vector regression with gravitational search algorithm optimization for estimating magnetic relative cooling power of manganite refrigerant in magnetic refrigeration application

Owolabi, Taoreed O.; Akande, Kabiru O.; Olatunji, Sunday O.; Aldhafferi, Nahier; Alqahtani, Abdullah

Abstract

Magnetic refrigeration technology (MRT) is considered an energy-efficient and environmental-friendly system of refrigeration that has a considerable potential of replacing the classical gas-compression expansion method of refrigeration. Inclusion of manganite-based material (MBM) in MRT as a magnetic refrigerant has attracted significant attention recently due to cost effectiveness of the refrigerant as well as better resistance to oxidation and corrosion as compared to the commonly used metal gadolinium refrigerant. Relative cooling power (RCP) is one of the most important parameters to be considered while assessing the usefulness of MBM. Its value can be altered through doping with external materials and accurate estimation of the dopant influence is required to achieve the right amount of RCP. This present research work proposes support vector regression (SVR) ensemble models with gravitational search algorithm (GSA) hyper-parameters optimization, for estimating RCP of MBM and to determine the influence of dopants on RCP using ionic radii and dopant concentrations as descriptors. GSA-SVR ensemble model (GSE) is developed by employing the outputs of five different SVR models as descriptors while GSA-SVR ensemble model with averaging (GSEA) uses the average of the five different SVR models as its descriptor. The novel ensemble models outperform other SVR models, specifically; GSE performs better than GSA-SVR model and the conventional SVR model with performance improvement of 269.14% and 283.61%, respectively on the basis of root mean square error (RMSE). Furthermore, GSEA outperforms GSE, GSA-SVR model and conventional SVR with performance improvement of 27.51%, 370.70%, and 389.14%, respectively on the basis of RMSE. The developed GSE and GSEA also perform better than the existing RCP model in the literature with performance improvement of 11.53% and 42.21%, respectively. The results of this research work will not only serve to circumvent the experimental challenges of RCP measurement without loss of experimental precision but also further promotes environment-friendly system of refrigeration.

Citation

Owolabi, T. O., Akande, K. O., Olatunji, S. O., Aldhafferi, N., & Alqahtani, A. (2019). Ensemble-based support vector regression with gravitational search algorithm optimization for estimating magnetic relative cooling power of manganite refrigerant in magnetic refrigeration application. Journal of Superconductivity and Novel Magnetism, 32(7), 2107-2118. https://doi.org/10.1007/s10948-018-4930-2

• Journal Article Type: Article
• Acceptance Date: Oct 29, 2018
• Online Publication Date: Nov 20, 2018
• Publication Date: 2019-07
• Deposit Date: May 21, 2021
• Journal: Journal of Superconductivity and Novel Magnetism
• Print ISSN: 1557-1939
• Electronic ISSN: 1557-1947
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 7
• Pages: 2107-2118
• DOI: https://doi.org/10.1007/s10948-018-4930-2
• Keywords: Electronic, Optical and Magnetic Materials; Condensed Matter Physics
• Public URL: https://uwe-repository.worktribe.com/output/5289143
• Additional Information: Received: 24 September 2018; Accepted: 29 October 2018; First Online: 20 November 2018; : ; : The authors declare that there is no conflict of interest as this manuscript represents original research work.