Periodic Reporting for period 1 - AUTO-TWIN (Data-driven method based on a process mining approach for Automated Digital Twin generation, operations, and maintenance in circular value chains)
Período documentado: 2022-12-01 hasta 2024-05-31
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 still stand concerning 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 unleash their full potential for
actual zero-waste Circular Economy and reduced dependency on raw materials.
AUTO-TWIN vision is addressing four specific objectives, each one solving a well-defined research challenge, resulting in measurable exploitable results:
- OBJECTIVE 1 - Automated digital twin generation, operations, and maintenance in circular value chains
- OBJECTIVE 2 - Trustworthy high resolution track & trace of products and processes among different actors in circular value chains
- OBJECTIVE 3- Reduce skills and knowledge gaps for all involved actors through augmented intelligence
- OBJECTIVE 4- Augmented intelligence algorithms for decision making at Green Gateways.
production, but significant challenges still stand concerning 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 unleash their full potential for
actual zero-waste Circular Economy and reduced dependency on raw materials.
AUTO-TWIN vision is addressing four specific objectives, each one solving a well-defined research challenge, resulting in measurable exploitable results:
- OBJECTIVE 1 - Automated digital twin generation, operations, and maintenance in circular value chains
- OBJECTIVE 2 - Trustworthy high resolution track & trace of products and processes among different actors in circular value chains
- OBJECTIVE 3- Reduce skills and knowledge gaps for all involved actors through augmented intelligence
- OBJECTIVE 4- Augmented intelligence algorithms for decision making at Green Gateways.
OBJECTIVE 1 - Automated digital twin generation, operations, and maintenance in circular value chains.
In order to reach this objective, two research questions (No. 1 and No. 2) were identified and addressed in the reporting period as follows:
Research Question 1: How to automatically discover, from data, finite state models of process equipment and item flows.
This research question represents the keystone of AUTO-TWIN and its related concepts. The activities in the reporting period were carried out along four dimensions:
· Definition and development of the work-flow from data to digital twin.
· Definition and development of data structure and related domain knowledge.
· Definition and development of the circular value chain features to represent in digital twin models.
· Definition and development of the models and algorithms in the components crossed by the data work-flow.
Research Question 2: How to build and maintain, with scarce resources, simulation model of the whole value chain.
Validation and calibration are the other steps that are needed to maintain the digital twin in a fully automated way. The models, and their related methods and algorithms, of these steps components in the work-flow were defined and implemented in a first version.
OBJECTIVE 2 - Trustworthy high resolution track & trace of products and processes among different actors in circular value chains.
In order to reach this objective, two research questions (No. 3 and No. 4) were identified and addressed in the reporting period as follows:
Research Question 3: How to track & trace items along the value chain in high resolution and in an incorruptible way.
The activities in the reporting period were carried out along four dimensions:
· Definition of the architecture specification.
· Connection with industry legacy systems using AUTO-TWIN solutions.
· Trustworthiness and data incorruptibility.
· Twin passport.
Research Question 4: How to store, share, and use data enabling data sovereignty for all different involved actors.
The activities in the reporting period focused on the safe storage and retrieval of data and information among actors and are ongoing at month M18.
OBJECTIVE 3- Reduce skills and knowledge gaps for all involved actors through augmented intelligence.
In order to reach this objective, two research questions (No. 5 and No. 6) were identified and addressed in the reporting period as follows:
Research Question 5: How to assess competencies and skills and enhanced it with focused training.
The activities in the reporting period focused on the design and development of component Worker and Production System Profiler.
Research Question 6: How (AI-empowered) digital tools can interact with users in a transparent, trustworthy, and understandable way.
The activities in the reporting period were carried out along two dimensions:
· Augmenting user understandability in data analysis and knowledge.
· Human interaction with AUTO-TWIN solutions.
OBJECTIVE 4- Augmented intelligence algorithms for decision making at Green Gateways.
In order to reach this objective, two research questions (No. 7 and No. 8) were identified and addressed in the reporting period as follows:
Research Question 7: How to Augment Actors Intelligence capabilities.
The activity in the reporting period focused on empowering Green Gateways with AI. Component C14 EcoSync: Green gateways Feedback Loop was designed and implemented in its first version to close the loop from data to physical system and providing at the same time useful insights to the user. This result was described in Deliverable D4.1 Green gateway AIs and explainable AIs for digital twin No. 1
Research Question 8: How to make production,logistics and pricing decisions based on real time process data.
The activity in the reporting period were carried out along two dimensions:
· Definition of Green Gateway concept.
· Decision making for Green Gateways.
In order to reach this objective, two research questions (No. 1 and No. 2) were identified and addressed in the reporting period as follows:
Research Question 1: How to automatically discover, from data, finite state models of process equipment and item flows.
This research question represents the keystone of AUTO-TWIN and its related concepts. The activities in the reporting period were carried out along four dimensions:
· Definition and development of the work-flow from data to digital twin.
· Definition and development of data structure and related domain knowledge.
· Definition and development of the circular value chain features to represent in digital twin models.
· Definition and development of the models and algorithms in the components crossed by the data work-flow.
Research Question 2: How to build and maintain, with scarce resources, simulation model of the whole value chain.
Validation and calibration are the other steps that are needed to maintain the digital twin in a fully automated way. The models, and their related methods and algorithms, of these steps components in the work-flow were defined and implemented in a first version.
OBJECTIVE 2 - Trustworthy high resolution track & trace of products and processes among different actors in circular value chains.
In order to reach this objective, two research questions (No. 3 and No. 4) were identified and addressed in the reporting period as follows:
Research Question 3: How to track & trace items along the value chain in high resolution and in an incorruptible way.
The activities in the reporting period were carried out along four dimensions:
· Definition of the architecture specification.
· Connection with industry legacy systems using AUTO-TWIN solutions.
· Trustworthiness and data incorruptibility.
· Twin passport.
Research Question 4: How to store, share, and use data enabling data sovereignty for all different involved actors.
The activities in the reporting period focused on the safe storage and retrieval of data and information among actors and are ongoing at month M18.
OBJECTIVE 3- Reduce skills and knowledge gaps for all involved actors through augmented intelligence.
In order to reach this objective, two research questions (No. 5 and No. 6) were identified and addressed in the reporting period as follows:
Research Question 5: How to assess competencies and skills and enhanced it with focused training.
The activities in the reporting period focused on the design and development of component Worker and Production System Profiler.
Research Question 6: How (AI-empowered) digital tools can interact with users in a transparent, trustworthy, and understandable way.
The activities in the reporting period were carried out along two dimensions:
· Augmenting user understandability in data analysis and knowledge.
· Human interaction with AUTO-TWIN solutions.
OBJECTIVE 4- Augmented intelligence algorithms for decision making at Green Gateways.
In order to reach this objective, two research questions (No. 7 and No. 8) were identified and addressed in the reporting period as follows:
Research Question 7: How to Augment Actors Intelligence capabilities.
The activity in the reporting period focused on empowering Green Gateways with AI. Component C14 EcoSync: Green gateways Feedback Loop was designed and implemented in its first version to close the loop from data to physical system and providing at the same time useful insights to the user. This result was described in Deliverable D4.1 Green gateway AIs and explainable AIs for digital twin No. 1
Research Question 8: How to make production,logistics and pricing decisions based on real time process data.
The activity in the reporting period were carried out along two dimensions:
· Definition of Green Gateway concept.
· Decision making for Green Gateways.
The project in the period from month M1 to month M18 has reached the first intermediate results:
- the ICT infrastructure has been designed with advanced features and most of it has been developed in a software architecture
- the automated workflow from data to the generation of discrete event situation models representing circular value chains, or part of them, as been implemented in its first release
- the concept of Green Gateways has been defined.
Further research will:
- consolidate and finalize the above initial results
- develop internal services for the automated maintenance of digital twins
- develop and implement decision-making tools enhanced by digital twins for Green Gateways.
- the ICT infrastructure has been designed with advanced features and most of it has been developed in a software architecture
- the automated workflow from data to the generation of discrete event situation models representing circular value chains, or part of them, as been implemented in its first release
- the concept of Green Gateways has been defined.
Further research will:
- consolidate and finalize the above initial results
- develop internal services for the automated maintenance of digital twins
- develop and implement decision-making tools enhanced by digital twins for Green Gateways.