Supervised Machine Learning-Based Prediction of Hydrogen Storage Classes Utilizing Dibenzyltoluene as an Organic Carrier
DC Field | Value | Language |
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dc.contributor.author | Ali, Ahsan | - |
dc.contributor.author | Khan, Muhammad Adnan | - |
dc.contributor.author | Choi, Hoimyung | - |
dc.date.accessioned | 2024-05-05T06:30:20Z | - |
dc.date.available | 2024-05-05T06:30:20Z | - |
dc.date.issued | 2024-03 | - |
dc.identifier.issn | 1420-3049 | - |
dc.identifier.issn | 1420-3049 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91110 | - |
dc.description.abstract | Dibenzyltoluene (H0-DBT), a Liquid Organic Hydrogen Carrier (LOHC), presents an attractive solution for hydrogen storage due to its enhanced safety and ability to store hydrogen in a concentrated liquid form. The utilization of machine learning proves essential for accurately predicting hydrogen storage classes in H0-DBT across diverse experimental conditions. This study focuses on the classification of hydrogen storage data into three classes, low-class, medium-class and high-class, based on the hydrogen storage capacity values. We introduce Hydrogen Storage Prediction with the Support Vector Machine (HSP-SVM) model to predict the hydrogen storage classes accurately. The performance of the proposed HSP-SVM model was investigated using various techniques, which included 5-Fold Cross Validation (5-FCV), Resubstitution Validation (RV), and Holdout Validation (HV). The accuracy of the HV approach for the low, medium, and high class was 98.5%, 97%, and 98.5%, respectively. The overall accuracy of HV approach reached 97% with a miss clarification rate of 3%, whereas 5-FCV and RV possessed an overall accuracy of 93.9% with a miss clarification rate of 6.1%. The results reveal that the HV approach is optimal for predicting the hydrogen storage classes accurately. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Supervised Machine Learning-Based Prediction of Hydrogen Storage Classes Utilizing Dibenzyltoluene as an Organic Carrier | - |
dc.type | Article | - |
dc.identifier.wosid | 001192903800001 | - |
dc.identifier.doi | 10.3390/molecules29061280 | - |
dc.identifier.bibliographicCitation | MOLECULES, v.29, no.6 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85189179860 | - |
dc.citation.title | MOLECULES | - |
dc.citation.volume | 29 | - |
dc.citation.number | 6 | - |
dc.type.docType | Article | - |
dc.publisher.location | Switzerland | - |
dc.subject.keywordAuthor | 5-Fold Cross Validation | - |
dc.subject.keywordAuthor | Holdout Validation | - |
dc.subject.keywordAuthor | HSP-SVM | - |
dc.subject.keywordAuthor | Resubstitution Validation | - |
dc.subject.keywordAuthor | Support Vector Machine | - |
dc.subject.keywordPlus | DEHYDROGENATION PERFORMANCE | - |
dc.subject.keywordPlus | METAL-HYDRIDES | - |
dc.subject.keywordPlus | N-ETHYLINDOLE | - |
dc.subject.keywordPlus | KINETICS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | ENERGY | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalWebOfScienceCategory | Biochemistry & Molecular Biology | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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