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Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance

Author

Listed:
  • Fabio Di Carlo

    (Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, 60131 Ancona, Italy)

  • Giovanni Mazzuto

    (Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, 60131 Ancona, Italy)

  • Maurizio Bevilacqua

    (Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, 60131 Ancona, Italy)

  • Filippo Emanuele Ciarapica

    (Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, 60131 Ancona, Italy)

Abstract

The transformation from traditional industry to Industry 4.0 can bring many benefits in various spheres, from efficiency to safety. However, this transition involves adopting technologically advanced machinery with a high level of digitization and communication. The costs and time to replace obsolete machines could be unsustainable for many companies while retrofitting the old machinery. To make them ready to the Industry 4.0 context, they may represent an alternative to the replacement. Even if there are many studies related to retrofitting applied to machinery, there are very few studies related to the literature process industry sector. In this work, we propose a case study of a two-phase mixing plant that needed to be enhanced in the safety and maintainability conditions with reasonable times and costs. In this regard, the Digital Twin techniques and Deep Learning algorithms will be tested to predict and detect future faults, not only already visible and existing malfunctions. This approach strength is that, with limited investments and reasonable times, it allows the transformation of an old plant into a smart plant capable of communicating quickly with operators to increase its safety and maintainability.

Suggested Citation

  • Fabio Di Carlo & Giovanni Mazzuto & Maurizio Bevilacqua & Filippo Emanuele Ciarapica, 2021. "Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:646-:d:478663
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    References listed on IDEAS

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    1. Maurizio Bevilacqua & Filippo Emanuele Ciarapica & Giulio Marcucci & Giovanni Mazzuto, 2020. "Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study," International Journal of Production Research, Taylor & Francis Journals, vol. 58(20), pages 6370-6398, October.
    2. Marcello Fera & Raffaele Abbate & Mario Caterino & Pasquale Manco & Roberto Macchiaroli & Marta Rinaldi, 2020. "Economic and Environmental Sustainability for Aircrafts Service Life," Sustainability, MDPI, vol. 12(23), pages 1-17, December.
    3. Maurizio Bevilacqua & Eleonora Bottani & Filippo Emanuele Ciarapica & Francesco Costantino & Luciano Di Donato & Alessandra Ferraro & Giovanni Mazzuto & Andrea Monteriù & Giorgia Nardini & Marco Orten, 2020. "Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants," Sustainability, MDPI, vol. 12(3), pages 1-17, February.
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    Cited by:

    1. Nwaila, Glen T. & Frimmel, Hartwig E. & Zhang, Steven E. & Bourdeau, Julie E. & Tolmay, Leon C.K. & Durrheim, Raymond J. & Ghorbani, Yousef, 2022. "The minerals industry in the era of digital transition: An energy-efficient and environmentally conscious approach," Resources Policy, Elsevier, vol. 78(C).

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