Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning

Ahsan Ali, Muhammad Adnan Khan and Hoimyung Choi ()
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Ahsan Ali: Department of Mechanical Engineering, Gachon University, Seongnam 13120, Korea
Muhammad Adnan Khan: Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University Lahore Campus, Lahore 54000, Pakistan
Hoimyung Choi: Department of Mechanical Engineering, Gachon University, Seongnam 13120, Korea

Mathematics, 2022, vol. 10, issue 20, 1-14

Abstract: The hydrogen stored in liquid organic hydrogen carriers (LOHCs) has an advantage of safe and convenient hydrogen storage system. Dibenzyltoluene (DBT), due to its low flammability, liquid nature and high hydrogen storage capacity, is an efficient LOHC system. It is imperative to indicate the optimal reaction conditions to achieve the theoretical hydrogen storage density. Hence, a Hydrogen Storage Prediction System empowered with Weighted Federated Machine Learning (HSPS-WFML) is proposed in this study. The dataset were divided into three classes, i.e., low, medium and high, and the performance of the proposed HSPS-WFML was investigated. The accuracy of the medium class is higher (99.90%) than other classes. The accuracy of the low and high class is 96.50% and 96.40%, respectively. Moreover, the overall accuracy and miss rate of the proposed HSPS-WFML are 96.40% and 3.60%, respectively. Our proposed model is compared with existing studies related to hydrogen storage prediction, and its accuracy is found in agreement with these studies. Therefore, the proposed HSPS-WFML is an efficient model for hydrogen storage prediction.

Keywords: dibenzyltoluene; federated learning; hydrogen storage prediction; and HSPS-WFML (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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