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Data-driven method based on a process mining approach for Automated Digital Twin generation, operations, and maintenance in circular value chains

Project description

More accessible digital twins for the circular economy transition

The transition to a circular economy is even more beneficial than lowering emissions or reducing damage to the environment, providing an expected EUR 4.1 trillion global growth by 2030. Digital twin technology is considered an essential part of achieving this transition, offering immense benefits in optimising business and production processes. The EU-funded AUTO-TWIN project is introducing a new method for creating digital twins. It aims to address the limitations of current system engineering models via a breakthrough method for automated process-aware discovery towards autonomous digital twins generation.


The transition towards a Circular Economy is estimated to represent a $4.5 trillion global growth opportunity by 2030. Digital Twins availability is largely recognized as an accelerator and an enabler of Circular Economy in business and in production, but significant challenges are still standing in relation to its development within the present technological framework, the needed skill sets, and the implementation costs. AUTO-TWIN addresses the technological shortcoming and economic liability of the current system-engineering model by 1) introducing a breakthrough method for automated process-aware discovery towards autonomous Digital Twins generation, to support trustworthy business processes in circular economies; 2) adopting an (International Data Space) IDS-based common data space, to promote and facilitate the secure and seamless exchange of manufacturing/product/business data within value-networks in a circular-economy ecosystem; 3) integrating novel hardware technologies into the digital thread, to create smart Green Gateways, empowering companies to perform data and digital twin enabled green decisions, and to unleash their full potential for actual zero-waste Circular Economy and reduced dependency from raw materials.


Funding Scheme



Net EU contribution
€ 749 375,00
Piazza leonardo da vinci 32
20133 Milano

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Nord-Ovest Lombardia Milano
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00

Participants (10)

Partners (2)