Osama Khan, Mohd Parvez, Pratibha Kumari, Zeinebou Yahya, Aiyeshah Alhodaib, Ashok Kumar Yadav, Anoop Kumar Shukla
{"title":"利用优先聚类法优化月桂酸基相变材料的热性能","authors":"Osama Khan, Mohd Parvez, Pratibha Kumari, Zeinebou Yahya, Aiyeshah Alhodaib, Ashok Kumar Yadav, Anoop Kumar Shukla","doi":"10.1002/est2.70026","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This study investigates the thermal properties of lauric acid (LA) as a phase change material (PCM) using the <i>K</i>-Means clustering method to analyze the melting characteristics. This study focuses on the optimization of PCMs using a hybrid methodology of analytic hierarchy process (AHP) and <i>K</i>-Means clustering. LA, enhanced with zinc oxide (ZnO) nanoparticles, was evaluated for its thermal performance. LA's suitability as a PCM is evaluated based on initial temperature, heating rate, final temperature, and time to melt. AHP was employed to determine the weightage for three critical outcomes: latent heat, melting point, and thermal conductivity. The weightages assigned were 59%, 31%, and 11%, respectively, reflecting the relative importance of each outcome in assessing the efficiency of LA as a PCM. Furthermore, <i>K</i>-Means clustering was then applied to categorize the data based on these weighted outcomes. AHP was utilized to determine the weightage of input parameters, assigning 27% to initial temperature, 15% to heating rate, and 22% to final temperature, underscoring their significance in the analysis. The optimal input conditions identified were an initial temperature of 24.8°C, a ieating rate of 5.6°C/min, a final temperature of 81.4°C, and a time to melt of 10.6 min. These conditions resulted in optimal outcomes of 208 J/g for latent heat, a melting point of 80.9°C, and a thermal conductivity of 0.21 W/m·K. This hybrid approach provides a robust framework for optimizing PCM performance, facilitating enhanced thermal energy storage and release in practical applications.</p>\n </div>","PeriodicalId":11765,"journal":{"name":"Energy Storage","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of Thermal Performance in Lauric Acid-Based Phase Change Materials Using a Priority Clustering Approach\",\"authors\":\"Osama Khan, Mohd Parvez, Pratibha Kumari, Zeinebou Yahya, Aiyeshah Alhodaib, Ashok Kumar Yadav, Anoop Kumar Shukla\",\"doi\":\"10.1002/est2.70026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>This study investigates the thermal properties of lauric acid (LA) as a phase change material (PCM) using the <i>K</i>-Means clustering method to analyze the melting characteristics. This study focuses on the optimization of PCMs using a hybrid methodology of analytic hierarchy process (AHP) and <i>K</i>-Means clustering. LA, enhanced with zinc oxide (ZnO) nanoparticles, was evaluated for its thermal performance. LA's suitability as a PCM is evaluated based on initial temperature, heating rate, final temperature, and time to melt. AHP was employed to determine the weightage for three critical outcomes: latent heat, melting point, and thermal conductivity. The weightages assigned were 59%, 31%, and 11%, respectively, reflecting the relative importance of each outcome in assessing the efficiency of LA as a PCM. Furthermore, <i>K</i>-Means clustering was then applied to categorize the data based on these weighted outcomes. AHP was utilized to determine the weightage of input parameters, assigning 27% to initial temperature, 15% to heating rate, and 22% to final temperature, underscoring their significance in the analysis. The optimal input conditions identified were an initial temperature of 24.8°C, a ieating rate of 5.6°C/min, a final temperature of 81.4°C, and a time to melt of 10.6 min. These conditions resulted in optimal outcomes of 208 J/g for latent heat, a melting point of 80.9°C, and a thermal conductivity of 0.21 W/m·K. This hybrid approach provides a robust framework for optimizing PCM performance, facilitating enhanced thermal energy storage and release in practical applications.</p>\\n </div>\",\"PeriodicalId\":11765,\"journal\":{\"name\":\"Energy Storage\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Storage\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/est2.70026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Storage","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/est2.70026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization of Thermal Performance in Lauric Acid-Based Phase Change Materials Using a Priority Clustering Approach
This study investigates the thermal properties of lauric acid (LA) as a phase change material (PCM) using the K-Means clustering method to analyze the melting characteristics. This study focuses on the optimization of PCMs using a hybrid methodology of analytic hierarchy process (AHP) and K-Means clustering. LA, enhanced with zinc oxide (ZnO) nanoparticles, was evaluated for its thermal performance. LA's suitability as a PCM is evaluated based on initial temperature, heating rate, final temperature, and time to melt. AHP was employed to determine the weightage for three critical outcomes: latent heat, melting point, and thermal conductivity. The weightages assigned were 59%, 31%, and 11%, respectively, reflecting the relative importance of each outcome in assessing the efficiency of LA as a PCM. Furthermore, K-Means clustering was then applied to categorize the data based on these weighted outcomes. AHP was utilized to determine the weightage of input parameters, assigning 27% to initial temperature, 15% to heating rate, and 22% to final temperature, underscoring their significance in the analysis. The optimal input conditions identified were an initial temperature of 24.8°C, a ieating rate of 5.6°C/min, a final temperature of 81.4°C, and a time to melt of 10.6 min. These conditions resulted in optimal outcomes of 208 J/g for latent heat, a melting point of 80.9°C, and a thermal conductivity of 0.21 W/m·K. This hybrid approach provides a robust framework for optimizing PCM performance, facilitating enhanced thermal energy storage and release in practical applications.