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

Author

Listed:
  • 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
    Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13120, Korea)

  • Hoimyung Choi

    (Department of Mechanical Engineering, Gachon University, Seongnam 13120, Korea)

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.

Suggested Citation

  • Ahsan Ali & Muhammad Adnan Khan & Hoimyung Choi, 2022. "Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning," Mathematics, MDPI, vol. 10(20), pages 1-14, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3846-:d:945103
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    References listed on IDEAS

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    1. Alauddin Ahmed & Saona Seth & Justin Purewal & Antek G. Wong-Foy & Mike Veenstra & Adam J. Matzger & Donald J. Siegel, 2019. "Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
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