{"title":"利用极端学习机和混合智能方法为 RE2TM2Y 三元金属间稀土基磁致化合物建立磁制冷应用的相对制冷功率模型","authors":"Sami M. Ibn Shamsah","doi":"10.1016/j.ijrefrig.2024.08.010","DOIUrl":null,"url":null,"abstract":"<div><div>Ternary intermetallic rare-earth-based magnetocaloric compounds (RE<sub>2</sub>TM<sub>2</sub>Y, where RE = Gd, Tb, Dy, Ho, Er, Tm, TM= Ni, Cu, Co and <em>Y</em> = Sn, In, Cd, Ga, Al) have attracted attention lately as magnetic refrigerants in addressing major concerns of the conventional system of refrigeration. Assessment of the amount of heat transferred between cold and hot reservoirs at varying applied magnetic fields through relative cooling power (RCP) determination is costly and experimentally intensive which calls for predictive computational techniques with characteristic precision. In this contribution, intelligent-based predictive models are developed through sine activation function-based extreme learning machine (SELM) and genetically optimized support vector regression (GSVR) with Gaussian (GU) and polynomial (PY) kernel functions for data mapping and transformation using applied magnetic field and ionic radii of the constituent elements as descriptors. The GU-GSVR model exhibits superior performance compared to both the PY-GSVR and SELM models when validated using a testing set of ternary intermetallic rare-earth-based magnetocaloric compounds with improvement of 10.55% and 2.28%, respectively using correlation coefficient (CC) as assessment parameter. During model validation, the developed GU-GSVR also showcases enhanced performance across additional performance metrics, including root mean square error (RMSE) and mean absolute error (MAE). The impact of externally applied magnetic field on the RCP of different ternary intermetallic rare-earth-based magnetocaloric compounds was examined by utilizing the developed GU-GSVR model. The characteristic precision and accuracy of the developed computational intelligent models would enable adequate as well as comprehensive investigation of ternary intermetallic rare-earth-based magnetocaloric compounds for a clean system of refrigeration.</div></div>","PeriodicalId":14274,"journal":{"name":"International Journal of Refrigeration-revue Internationale Du Froid","volume":"168 ","pages":"Pages 122-134"},"PeriodicalIF":3.5000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Relative cooling power modeling of RE2TM2Y ternary intermetallic rare-earth-based magnetocaloric compounds for magnetic refrigeration application using extreme learning machine and hybrid intelligent method\",\"authors\":\"Sami M. Ibn Shamsah\",\"doi\":\"10.1016/j.ijrefrig.2024.08.010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Ternary intermetallic rare-earth-based magnetocaloric compounds (RE<sub>2</sub>TM<sub>2</sub>Y, where RE = Gd, Tb, Dy, Ho, Er, Tm, TM= Ni, Cu, Co and <em>Y</em> = Sn, In, Cd, Ga, Al) have attracted attention lately as magnetic refrigerants in addressing major concerns of the conventional system of refrigeration. Assessment of the amount of heat transferred between cold and hot reservoirs at varying applied magnetic fields through relative cooling power (RCP) determination is costly and experimentally intensive which calls for predictive computational techniques with characteristic precision. In this contribution, intelligent-based predictive models are developed through sine activation function-based extreme learning machine (SELM) and genetically optimized support vector regression (GSVR) with Gaussian (GU) and polynomial (PY) kernel functions for data mapping and transformation using applied magnetic field and ionic radii of the constituent elements as descriptors. The GU-GSVR model exhibits superior performance compared to both the PY-GSVR and SELM models when validated using a testing set of ternary intermetallic rare-earth-based magnetocaloric compounds with improvement of 10.55% and 2.28%, respectively using correlation coefficient (CC) as assessment parameter. During model validation, the developed GU-GSVR also showcases enhanced performance across additional performance metrics, including root mean square error (RMSE) and mean absolute error (MAE). The impact of externally applied magnetic field on the RCP of different ternary intermetallic rare-earth-based magnetocaloric compounds was examined by utilizing the developed GU-GSVR model. The characteristic precision and accuracy of the developed computational intelligent models would enable adequate as well as comprehensive investigation of ternary intermetallic rare-earth-based magnetocaloric compounds for a clean system of refrigeration.</div></div>\",\"PeriodicalId\":14274,\"journal\":{\"name\":\"International Journal of Refrigeration-revue Internationale Du Froid\",\"volume\":\"168 \",\"pages\":\"Pages 122-134\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Refrigeration-revue Internationale Du Froid\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0140700724002858\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Refrigeration-revue Internationale Du Froid","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140700724002858","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Relative cooling power modeling of RE2TM2Y ternary intermetallic rare-earth-based magnetocaloric compounds for magnetic refrigeration application using extreme learning machine and hybrid intelligent method
Ternary intermetallic rare-earth-based magnetocaloric compounds (RE2TM2Y, where RE = Gd, Tb, Dy, Ho, Er, Tm, TM= Ni, Cu, Co and Y = Sn, In, Cd, Ga, Al) have attracted attention lately as magnetic refrigerants in addressing major concerns of the conventional system of refrigeration. Assessment of the amount of heat transferred between cold and hot reservoirs at varying applied magnetic fields through relative cooling power (RCP) determination is costly and experimentally intensive which calls for predictive computational techniques with characteristic precision. In this contribution, intelligent-based predictive models are developed through sine activation function-based extreme learning machine (SELM) and genetically optimized support vector regression (GSVR) with Gaussian (GU) and polynomial (PY) kernel functions for data mapping and transformation using applied magnetic field and ionic radii of the constituent elements as descriptors. The GU-GSVR model exhibits superior performance compared to both the PY-GSVR and SELM models when validated using a testing set of ternary intermetallic rare-earth-based magnetocaloric compounds with improvement of 10.55% and 2.28%, respectively using correlation coefficient (CC) as assessment parameter. During model validation, the developed GU-GSVR also showcases enhanced performance across additional performance metrics, including root mean square error (RMSE) and mean absolute error (MAE). The impact of externally applied magnetic field on the RCP of different ternary intermetallic rare-earth-based magnetocaloric compounds was examined by utilizing the developed GU-GSVR model. The characteristic precision and accuracy of the developed computational intelligent models would enable adequate as well as comprehensive investigation of ternary intermetallic rare-earth-based magnetocaloric compounds for a clean system of refrigeration.
期刊介绍:
The International Journal of Refrigeration is published for the International Institute of Refrigeration (IIR) by Elsevier. It is essential reading for all those wishing to keep abreast of research and industrial news in refrigeration, air conditioning and associated fields. This is particularly important in these times of rapid introduction of alternative refrigerants and the emergence of new technology. The journal has published special issues on alternative refrigerants and novel topics in the field of boiling, condensation, heat pumps, food refrigeration, carbon dioxide, ammonia, hydrocarbons, magnetic refrigeration at room temperature, sorptive cooling, phase change materials and slurries, ejector technology, compressors, and solar cooling.
As well as original research papers the International Journal of Refrigeration also includes review articles, papers presented at IIR conferences, short reports and letters describing preliminary results and experimental details, and letters to the Editor on recent areas of discussion and controversy. Other features include forthcoming events, conference reports and book reviews.
Papers are published in either English or French with the IIR news section in both languages.