In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms.

Industry 4.0 is evolving through technological advancements, leveraging information technology to enhance industry with digitalisation and intelligent activities. Whereas Industry 5.0 is the Age of Augmentation, striving to concentrate on human-centricity, sustainability, and resilience of the intelligent factories and synergetic industry. The crucial enhancer for the improvements accomplished by digital transformation is the notion of ‘digital triplet D3’, which is an augmentation of the digital twin with artificial intelligence, human ingenuity, and experience. digital triplet D3 encompasses intelligent activities based on human awareness and the convergence among cyberspace, physical space, and humans, in which Implementing useful reference hierarchy is a crucial part of instigating Industry 5.0 into a reality. This paper depicts a digital triplet which discloses the potency of retrofitting a conventional drilling machine. This hierarchy included the perceptive level for complex decision-making by deploying machine learning based on human ingenuity and creativity, the concatenated level for controlling the physical system’s behaviour predictions and emulation, the observing level is the iterative observation of the actual behaviour of the physical system using real-time data, and the duplicating level visualises and emulates virtual features through physical tasks. The accomplishment demonstrated the viability of the hierarchy in imitating the real-time functionality of the physical system in cyberspace, an immaculate performance of this paradigm. The digital triplet’s complexity was diminished through the interaction among facile digital twins, intelligent activities, and human awareness. The performance parameters of the digital triplet D3 paradigm for retrofitting were eventually confirmed through appraising, anomaly analysis, and real-time monitoring.

Despite the extensive use of ejectors in the process industry, it is complex to predict suction and motive fluids mixture characteristics, especially with multiphase flows, even if, in most cases, mixture pressure control is necessary to satisfy process requirements or to avoid performance problems. The realization of an ejector model can allow the operators to overcome these difficulties to have real-time control of the system performance. In this context, this work proposes a framework for developing a Digital Twin of an ejector installed in an experimental plant able to predict the future state of an item and the impact of negative scenarios and faults diagnosis. ANNs have been identified as the most used tool for simulating the multiphase flow ejector. Nevertheless, the complexity in defining their structure and the computational effort to train and use them are not suitable for realizing standalone applications onboard the ejector. The proposed paper shows how Swarm Intelligence algorithms require a low computational complexity and overperform prediction error and computational effort. Specifically, the Grey Wolf optimizer proves to be the best one among those analyzed.

The automation of grading, handling and packaging of soft and deformable food products in the industry necessitates precision in manipulation, adaptive gripping, and real-time monitoring to ensure uniform quality and prevent damage. These aspects are the objectives of the European project AGILEHAND. This project aims at developing advanced technologies as a strategic instrument to improve flexibility, agility and reconfigurability of production and logistic systems of the manufacturing companies. In this context, this paper discusses the development of a data-driven framework for automated generation of simulation models for the realization of the digital twins in food factories. The core of this framework involves discrete event simulation: by real system data it is possible to streamline the reconfiguration of production and logistic systems. During this process, it is also possible to promptly identify design or process sequence problems at an early stage through cross-domain simulation. Firstly, this framework supports the project manager in scheduling production by optimising resources and minimising the order of Estimated Time of Arrival. Then, during the system monitoring phase, it simulates new scenario to find the proper corrective action when a significant deviation from the plan occurs. In the end, the accurate capture of dynamics arising in the physical layer allows an effective evaluation of their negative effects on the system's overall operational state. This approach supports informed decision-making in planning and controlling logistics, production, and associated activities, providing a comprehensive framework for enhanced operational efficiency.

The Production Logistics system is generally a large-scale complex system with various operational phases and management levels that must integrate. In the specific context of soft and deformable food products, the core of AGILEHAND European project, this complexity increases further due to challenges related to the handling and movement of such items. Efficient coordination of production and logistics phases becomes crucial to ensure product quality, prevent losses, and optimize the entire process. In this article, focus will be placed on a data-driven framework for the automated generation of simulation models, serving as the foundation for digital twins in intelligent factories within the previously mentioned sector. The proposed framework represents a multi-layered data-driven system designed for real-time/near-real-time simulation, planning and synchronization of production and logistics systems during line reconfiguration. The digital model forms the basis for a digital twin with simulation and optimization capabilities, designed to facilitate decision-making at various management levels in the production and logistics process and control activities such as changes, maintenance, quality and safety. Exploiting information provided by the Enterprise Traceability system, the digital twin aims to establish a real-time/near-real-time information flow. This flow enables accurate capturing of dynamics occurring in the physical layer and effective assessment of their negative effects on the overall operational state of the system in the digital layer. In this context, the use of the digital twin is intended to simplify and expedite the reconfiguration of production and logistics systems. This is achieved through the early detection of system design or process sequence through cross-sectional simulation.

The food manufacturing sector increasingly requires advanced technologies for production management and process optimization. In particular, the development of Digital Twins (DT) enhances synchronization between logistics and production by integrating innovative technologies such as Big Data, IoT, and Artificial Intelligence. These technologies enable process optimization, reducing wait times and improving resource utilization. In the food sector, this translates in minimizing food waste and ensuring product freshness. This study focuses on optimizing production management and human resource allocation in collaboration with an Italian company specializing in small fruit processing as part of the AGILEHAND European project. The optimization approach includes improving forecasting techniques through AI-based algorithms, optimizing workforce allocation and streamlining production workflows to improve adaptability to fluctuating demand. The DT serves as a decision-making tool by analysing both real-time and historical data, simulating various operational scenarios, and determining optimal production configurations using a non-real-time simulation module. The results demonstrate the system accuracy in meeting demand deadlines while optimizing workforce requirements, leading to more efficient resource management. By leveraging DT technology, the system enables proactive adjustments in production schedules and workforce distribution.