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). 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 |
|---|---|
| Title of host publication | Proceedings of IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025 |
| Editors | Ignazio Infantino, Valeria Seidita |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 2025 |
| Article number | 10979177 |
| ISBN (Print) | 979-8-3315-1686-4 |
| ISBN (Electronic) | 979-8-3315-1685-7 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025 - Palermo, Italy Duration: 14. Apr 2025 → 18. Apr 2025 |
Conference
| Conference | 2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025 |
|---|---|
| Country/Territory | Italy |
| City | Palermo |
| Period | 14/04/2025 → 18/04/2025 |
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