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

Fabio Di Carlo, Giovanni Mazzuto, Maurizio Bevilacqua and Filippo Emanuele Ciarapica
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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

Sustainability, 2021, vol. 13, issue 2, 1-18

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.

Keywords: retrofitting; Industry 4.0 technologies; digital twin; deep learning; process plant; safety and maintenance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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