Open Access
| Issue |
EPJ Web Conf.
Volume 371, 2026
9th International Congress on Thermal Sciences (AMT’2026)
|
|
|---|---|---|
| Article Number | 04001 | |
| Number of page(s) | 17 | |
| Section | Smart Systems, Digital Twins and AI in Thermal Sciences | |
| DOI | https://doi.org/10.1051/epjconf/202637104001 | |
| Published online | 22 May 2026 | |
- K. Prawiranto, J. Carmeliet, and T. Defraeye, Physics-Based Digital Twin Identifies Trade-Offs Between Drying Time, Fruit Quality, and Energy Use for Solar Drying. Front. Sustain. Food Syst. 4, 606845 (2021). doi: https://doi.org/10.3389/fsufs.2020.606845. [Google Scholar]
- E. E. M. Abdurrahman and G. Ferrari, Digital Twin applications in the food industry: a review. Front. Sustain. Food Syst. 9, 1538375 (2025). doi: https://doi.org/10.3389/fsufs.2025.1538375 [Google Scholar]
- A. Juckers, P. Knerr, F. Harms, and J. Strube, Digital Twin Enabled Process Development, Optimization and Control in Lyophilization for Enhanced Biopharmaceutical Production. Processes 12, 211 (2024). https://doi.org/10.3390/pr12010211 [Google Scholar]
- G. Wang, D. Gao, W. Pedrycz, and L. Fellow, Solving Multi-Objective Fuzzy Jobshop Scheduling Problem by a Hybrid Adaptive Differential Evolution Algorithm. IEEE Trans. Ind. Inform. (2022). https://doi.org/10.1109/TII.2022.3165636 [Google Scholar]
- A. Soussi et al., Integrating Digital Twins and MPC for Sustainable Greenhouse Management in Smart Agriculture. IEEE Trans. AgriFood Electron. (2025). https://doi.org/10.1109/TAFE.2025.3572808 [Google Scholar]
- M. Kannapinn, D. Dorer, M. Schäfer, and O. Weeger, Digital twins for autonomous thermal food processing: A model predictive control study with reduced-order models of augmented neural ordinary differential equation type. J. Food Eng. 410, 112918 (2026). https://doi.org/10.1016/j.jfoodeng.2025.112918 [Google Scholar]
- M. Kannapinn, M. Khang, and M. Sch, Physics-based digital twins for autonomous thermal food processing: Efficient, non-intrusive reduced-order modeling. Innov. Food Sci. Emerg. Technol. 81, 103143 (2022). https://doi.org/10.1016/j.ifset.2022.103143 [Google Scholar]
- Y. Sardahi and J. Q. Sun, Many-Objective Optimal Design of Sliding Mode Controls. J. Dyn. Syst. Meas. Control 139, 011021 (2017).doi: https://doi.org/10.1115/1.4034421 [Google Scholar]
- W. Purcell and T. Neubauer, Digital Twins in Agriculture: A State-of-the-art review. Smart Agric. Technol. 3, 100094 (2023). https://doi.org/10.1016/j.atech.2022.100094 [Google Scholar]
- F. Munoz, E. N. Sanchez, Y. Xia, and S. Deng, Régulation neuronale inverse optimale en temps réel de la température et l,humidité intérieures dans un système de conditionnement d,air à détente directe. Int. J. Refrig. 79, 196-206 (2017). https://doi.org/10.1016/j.ijrefrig.2017.04.011 [Google Scholar]
- M. S. Dihan et al., Digital twin: Data exploration, architecture, implementation and future. Heliyon 10, e26503 (2024). https://doi.org/10.1016/j.heliyon.2024.e26503 [Google Scholar]
- R. Haber, J. A. Rossiter, and K. Zabet, An alternative for PID control: Predictive Functional Control-A tutorial. Proc. Am. Control Conf., 6935-6940 (2016). https://doi.org/10.1109/ACC.2016.7526765. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

