Many enabling technologies of Industry 4.0 (Internet of Things 'IoT', Cloud systems, Big Data Analytics) contribute to the creation of what is the Digital Twin or virtual twin of a physical process, that is a mathematical model capable of describing the process, product or service in a precise way in order to carry out analyses and apply strategies. Digital Twin models integrate artificial intelligence, machine learning and analytics software with the data collected from the production plants to create digital simulation models that update when the parameters of the production processes or the working conditions change. This is a self-learning mechanism, which makes use of data collected from various sources (sensors that transmit operating conditions; experts, such as engineers with deep knowledge of the industrial domain; other similar machines or fleets of similar machines) and integrates also historical data relating to the past use of the machine. Starting from the virtual twin vision, simulation plays a key role within the Industry 4.0 transformation. Creating a virtual prototype has become necessary and strategic to raise the safety levels of the operators engaged in the maintenance phases, but above all the integration of the digital model with the IoT has become particularly effective, as the advent of software platforms offers the possibility of integrating real-time data with all the digital information that a company owns on a given process, ensuring the realization of the Digital Twin. In this context, this work aims at developing optimized solutions for application in a beverage pasteurization system using the Digital Twin approach, capable of creating a virtual modelling of the process and preventing high-risk events for operators.

A digital twin model of a pasteurization system for food beverages: Tools and architecture / Bottani, E.; Vignali, G.; Tancredi, G.. - ELETTRONICO. - 1(2020), pp. 1-8. ((Intervento presentato al convegno 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020 tenutosi a Cardiff, United Kingdom (virtual conference) nel 2020 [10.1109/ICE/ITMC49519.2020.9198625].

A digital twin model of a pasteurization system for food beverages: Tools and architecture

Bottani E.;Vignali G.
;
Tancredi G.
2020

Abstract

Many enabling technologies of Industry 4.0 (Internet of Things 'IoT', Cloud systems, Big Data Analytics) contribute to the creation of what is the Digital Twin or virtual twin of a physical process, that is a mathematical model capable of describing the process, product or service in a precise way in order to carry out analyses and apply strategies. Digital Twin models integrate artificial intelligence, machine learning and analytics software with the data collected from the production plants to create digital simulation models that update when the parameters of the production processes or the working conditions change. This is a self-learning mechanism, which makes use of data collected from various sources (sensors that transmit operating conditions; experts, such as engineers with deep knowledge of the industrial domain; other similar machines or fleets of similar machines) and integrates also historical data relating to the past use of the machine. Starting from the virtual twin vision, simulation plays a key role within the Industry 4.0 transformation. Creating a virtual prototype has become necessary and strategic to raise the safety levels of the operators engaged in the maintenance phases, but above all the integration of the digital model with the IoT has become particularly effective, as the advent of software platforms offers the possibility of integrating real-time data with all the digital information that a company owns on a given process, ensuring the realization of the Digital Twin. In this context, this work aims at developing optimized solutions for application in a beverage pasteurization system using the Digital Twin approach, capable of creating a virtual modelling of the process and preventing high-risk events for operators.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2886311
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