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Generic Multi-Layered Digital-Twin-Framework-Enabled Asset Lifecycle Management for the Sustainable Mining Industry

Author

Listed:
  • Nabil El Bazi

    (Laboratory of Industrial Engineering (LGIIS), Faculty of Science and Technology, University Sultan Moulay Slimane (USMS), Beni Mellal 23000, Morocco
    Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco)

  • Mustapha Mabrouki

    (Laboratory of Industrial Engineering (LGIIS), Faculty of Science and Technology, University Sultan Moulay Slimane (USMS), Beni Mellal 23000, Morocco)

  • Oussama Laayati

    (Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco)

  • Nada Ouhabi

    (Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco)

  • Hicham El Hadraoui

    (Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco)

  • Fatima-Ezzahra Hammouch

    (Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco)

  • Ahmed Chebak

    (Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco)

Abstract

In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies.

Suggested Citation

  • Nabil El Bazi & Mustapha Mabrouki & Oussama Laayati & Nada Ouhabi & Hicham El Hadraoui & Fatima-Ezzahra Hammouch & Ahmed Chebak, 2023. "Generic Multi-Layered Digital-Twin-Framework-Enabled Asset Lifecycle Management for the Sustainable Mining Industry," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3470-:d:1067767
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    References listed on IDEAS

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    1. Oussama Laayati & Hicham El Hadraoui & Adila El Magharaoui & Nabil El-Bazi & Mostafa Bouzi & Ahmed Chebak & Josep M. Guerrero, 2022. "An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems," Energies, MDPI, vol. 15(19), pages 1-28, October.
    2. Kamil Židek & Ján Piteľ & Milan Adámek & Peter Lazorík & Alexander Hošovský, 2020. "Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    3. Ahmed Saad & Samy Faddel & Osama Mohammed, 2020. "IoT-Based Digital Twin for Energy Cyber-Physical Systems: Design and Implementation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    4. Adila El Maghraoui & Younes Ledmaoui & Oussama Laayati & Hicham El Hadraoui & Ahmed Chebak, 2022. "Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine," Energies, MDPI, vol. 15(13), pages 1-22, June.
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    Cited by:

    1. Ana Perišić & Ines Perišić & Branko Perišić, 2023. "Simulation-Based Engineering of Heterogeneous Collaborative Systems—A Novel Conceptual Framework," Sustainability, MDPI, vol. 15(11), pages 1-24, May.
    2. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    3. Guoliang Shi & Zhansheng Liu & Dengzhou Xian & Rongtian Zhang, 2023. "Intelligent Assessment Method of Structural Reliability Driven by Carrying Capacity Sustainable Target: Taking Bearing Capacity as Criterion," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
    4. Małgorzata Jasiulewicz-Kaczmarek & Katarzyna Antosz & Chao Zhang & Vitalii Ivanov, 2023. "Industry 4.0 Technologies for Sustainable Asset Life Cycle Management," Sustainability, MDPI, vol. 15(7), pages 1-7, March.

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