Khan, Muhammad Adnan, Ali, Ahsan and Choi, Hoimyung (2024) Supervised Machine Learning-Based Prediction of Hydrogen Storage Classes Utilizing Dibenzyltoluene as an Organic Carrier. Molecules, 29 (6). p. 1280. ISSN 1420-3049
29/6/1280 - Published Version
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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.
Affiliation: | Skyline University College |
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SUC Author(s): | Khan, Muhammad Adnan |
All Author(s): | Khan, Muhammad Adnan, Ali, Ahsan and Choi, Hoimyung |
Item Type: | Article |
Uncontrolled Keywords: | 5-Fold Cross Validation; Holdout Validation; HSP-SVM; Resubstitution Validation; Support Vector Machine |
Subjects: | B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 25 Apr 2024 17:37 |
Last Modified: | 25 Apr 2024 17:37 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/869 |
Publisher URL: | https://doi.org/10.3390/molecules29061280 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/19458 |
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