This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
Twin-AI: Intelligent Barrier Eddy Current Separator with Digital Twin and AI Integration
by
Shohreh Kia
Shohreh Kia 1,*
,
Johannes B. Mayer
Johannes B. Mayer 1
,
Erik Westphal
Erik Westphal 2
and
Benjamin Leiding
1,*
1
Institute for Software and Systems Engineering, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany
2
ESD Elektro Systemtechnik Dargun GmbH, 17159 Dargun, Germany
*
Authors to whom correspondence should be addressed.
Sensors 2025, 25(15), 4731; https://doi.org/10.3390/s25154731 (registering DOI)
Submission received: 30 June 2025
/
Revised: 25 July 2025
/
Accepted: 25 July 2025
/
Published: 31 July 2025
Abstract
The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments.
Share and Cite
MDPI and ACS Style
Kia, S.; Mayer, J.B.; Westphal, E.; Leiding, B.
Twin-AI: Intelligent Barrier Eddy Current Separator with Digital Twin and AI Integration. Sensors 2025, 25, 4731.
https://doi.org/10.3390/s25154731
AMA Style
Kia S, Mayer JB, Westphal E, Leiding B.
Twin-AI: Intelligent Barrier Eddy Current Separator with Digital Twin and AI Integration. Sensors. 2025; 25(15):4731.
https://doi.org/10.3390/s25154731
Chicago/Turabian Style
Kia, Shohreh, Johannes B. Mayer, Erik Westphal, and Benjamin Leiding.
2025. "Twin-AI: Intelligent Barrier Eddy Current Separator with Digital Twin and AI Integration" Sensors 25, no. 15: 4731.
https://doi.org/10.3390/s25154731
APA Style
Kia, S., Mayer, J. B., Westphal, E., & Leiding, B.
(2025). Twin-AI: Intelligent Barrier Eddy Current Separator with Digital Twin and AI Integration. Sensors, 25(15), 4731.
https://doi.org/10.3390/s25154731
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article Access Statistics
For more information on the journal statistics, click
here.
Multiple requests from the same IP address are counted as one view.