Abstract
In this paper a digital twin model of a drying
machine is presented to accurately predict its temperature and
humidity based on experimental data. The prediction facilitates
energy-efficiency of the industrial dryer IQ6 for disinfecting
surgical instruments, e.g., forceps. The model is achieved by
integrating a simplified thermodynamic model and a Data-
Driven Physics-Informed Neural Networks (DD-PINNs) into
reducing temperature and relative humidity prediction errors to
10.11ºC and 8.48%, respectively. Our contribution to the state-
of-the-art is to provide and open-source a digital twin model of
an industrial dryer. The shared model and data can be extended
and reused for other energy-efficient drying applications.
machine is presented to accurately predict its temperature and
humidity based on experimental data. The prediction facilitates
energy-efficiency of the industrial dryer IQ6 for disinfecting
surgical instruments, e.g., forceps. The model is achieved by
integrating a simplified thermodynamic model and a Data-
Driven Physics-Informed Neural Networks (DD-PINNs) into
reducing temperature and relative humidity prediction errors to
10.11ºC and 8.48%, respectively. Our contribution to the state-
of-the-art is to provide and open-source a digital twin model of
an industrial dryer. The shared model and data can be extended
and reused for other energy-efficient drying applications.
| Original language | English |
|---|---|
| Publication date | 2025 |
| Publication status | Accepted/In press - 2025 |
| Event | IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots - Duration: 14. Apr 2025 → 18. Apr 2025 https://www.simpar2025.org/ |
Conference
| Conference | IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots |
|---|---|
| Period | 14/04/2025 → 18/04/2025 |
| Internet address |