Incremental Deployment of Digital Twin for the Production of Biogas Purifiers

The thesis focuses on the design and deployment of a digital twin (DT) for the biogas production system, in collaboration between the DISP laboratory and the company Prodeval. The DT will need to model the biogas manufacturing process, while ensuring digital continuity and real-time monitoring of system performance, even in an extended production context involving subcontractors and customers.

The DT will serve as a decision-making tool at various levels (workshop, factory, company) by leveraging accumulated domain knowledge to improve decision-making. It will be developed in parallel with the construction of a new factory and must be capable of interacting and synchronizing with the physical system being created. This means the DT will need to be updated as the physical system evolves, but it should also be able to influence the physical system by detecting malfunctions and proposing improvements.

The main challenges of this research are:
1. How to build, test, and validate a DT for a physical system that is still under development?
2. How to make heterogeneous models and data interoperable across different decision-making levels?
3. How to evaluate and synchronize the DT with the physical system?
4. How to ensure the dynamic reconfiguration of the DT in response to misalignments during the physical system's development?

The PhD student will need to develop new methods and algorithms for the DT, based on model-based systems engineering (MBSE) and data-driven systems engineering (DDSE), as well as agile development methods. A reconfigurable architecture will need to be developed to adapt to changes in the physical system.