Digital Twin: A Unified Framework for Education, Research, and Critical Infrastructure Development

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Digital Twin: A Unified Framework for Education, Research, and Critical Infrastructure Development

Author: Sebastiano Martignano, research strategy advisor -CRF Italy

Abstract

The integration of Digital Twins into education, scientific research, and industrial innovation is rapidly emerging as a key paradigm to address the challenges of ecological transition, systemic innovation, and infrastructure resilience. This paper proposes a unified, multi-layered framework that incorporates dynamic modeling, process control, environmental impacts, and socio-economic outcomes. The framework supports both advanced training (through virtual and phygital labs) and applications to complex territorial scenarios under exogenous stressors, offering decision-making tools for long-term planning.

Introduction

The evolution of Digital Twins from simple digital replicas to multi-domain predictive and decision-making tools marks a methodological turning point. Modern applications enable process simulation, real-time control, environmental analysis, and long-term sustainability and resilience assessment at the territorial scale. Circular Research Foundation proposes a unified approach that integrates education, modeling, and territorial governance through an open, modular digital infrastructure.

Framework Architecture

The conceptual architecture is structured across four hierarchical and interconnected layers:

Each layer inherits dynamics and states from the preceding one and enriches the system with new indicators and decision logics. The transition from process to plant model includes the integration of control systems (e.g., PID, MPC, discrete logic). The environmental model adds sustainability metrics, while the socio-economic level allows scenario evaluations, aggregated impacts, investment returns, and resilience strategies.

 Integration with Digital Tools and Operational Prototyping

The effectiveness and scalability of the framework are enhanced by its ability to incorporate modeling environments, digital design tools, and computational modeling languages. The combination of parametric design platforms, interactive environments, and phygital systems enables the creation of working prototypes that can be deployed in physical-digital labs for training and applied research. These prototypes can then be seamlessly transferred to robust operational platforms through processes of architectural adaptation, ensuring consistency between ideation and implementation phases. This approach fosters alignment among models, digital infrastructures, and real-world applications.

Hybrid Models for Robustness and Strategic Decision-Making

A core component of the proposed framework is the use of hybrid models, combining physics-based components (first-principle models such as conservation equations of mass, energy, and momentum) with data-driven components derived from historical or real-time data. This integration ensures coherence in representing the underlying phenomena while benefiting from the adaptive and predictive power of statistical or machine learning models. Hybrid models are particularly valuable in complex scenarios where data-only models are insufficient due to semantic closure, lack of transparency, and limited generalizability. In critical decision-making contexts—such as environmental resource management, infrastructure security, or territorial planning—interpretability and traceability are essential. Hybrid models ensure robustness and explainability, providing a reliable foundation for decisions that impact public well-being and socio-ecological balance over the long term.

Digital twins for Training and education

Applications and Educational Integration Digital Twins are proving especially valuable in education due to their ability to support flexible, personalized, and experiential learning. As knowledge doubles over increasingly shorter timespans, educational models must adapt. Digital Twins address this challenge by enabling continuous content updates, simulating systems in varied conditions, and fostering peer learning and self-responsibility. The framework supports:

  • Virtual Labs: Interactive simulations to introduce dynamic modeling and control systems;
  • Phygital Labs: Integration of digital models with physical hardware to support testing and experimentation;
  • Upskilling/Reskilling Programs: Modular and customizable training for professionals, technicians, and system designers;
  • Problem-Based Learning: Real-case simulations that involve territorial dynamics and multi-domain interactions.

An advanced example is provided by the Copenhagen School of Marine Engineering and Technology Management, which has integrated Digital Twin-based modules in its curricula, especially in the fields of industrial and naval automation. Through dynamic virtual models, students can explore complex operational scenarios, program automated systems, and validate them virtually before deploying physical devices. This approach:

  • Increases the number of machine and system configurations students can explore;
  • Reduces the time and cost associated with traditional hands-on learning;
  • Enhances collaboration with industry during internships and thesis projects;
  • Strengthens students’ skills in design, simulation, and commissioning.

 Digital Twins for Regenerative Agriculture and Bioeconomy Districts

Digital Twin technologies are increasingly being explored as enablers of regenerative agriculture and circular bioeconomy districts. By creating virtual replicas of agro-ecological systems, including soil health dynamics, crop performance, biodiversity indicators, water flows, and nutrient cycles, Digital Twins support decision-making aligned with the principles of regeneration, resilience, and ecosystem restoration. These systems integrate sensor data, satellite imagery, and agronomic models to provide tools for adaptive management. At district scale, they simulate resource flows (biomass, energy, water), support logistics, and enhance coordination among stakeholders. The integration of ecological and socio-technical data supports regenerative planning aligned with SDGs and the EU Green Deal.

Digital Twins in Biotech and Bioprocessing 4.0

In biotechnology and bioprocessing, Digital Twins model, simulate, and optimize complex biological systems. A Digital Twin typically represents the full production chain—from fermentation to purification—and integrates real-time sensor data, control parameters, and historical data. Applications include:

  • Process Optimization;
  • Batch-to-Batch Consistency;
  • Scale-Up Support;
  • Continuous Bioprocessing. Digital Twins support Bioprocessing 4.0 by embedding IoT, AI/ML, and predictive analytics, enabling yield improvement, downtime reduction, and faster time-to-market.

Conclusions

This paper has outlined a unified Digital Twin framework capable of integrating education, research, and territorial system innovation through a multi-level architecture. The framework leverages hybrid modeling strategies, combining first-principles and data-driven methods, to provide robustness, traceability, and explanatory power in complex decision-making contexts. Digital Twins foster experiential learning and continuous professional development via Virtual and Phygital Labs, empowering learners and professionals to engage with complex systems and deliver high-quality solutions. Their application in regenerative agriculture and bioprocessing exemplifies their versatility as cognitive infrastructures for innovation and sustainable development.

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