## 5 Conclusions and Future Work  
This study introduced an intelligent modular system designed to enhance the conveyor belt's performance, safety, and automation within a barrier eddy current separator (BECS) machine. The project addressed several research questions through the development and evaluation of four core components:  
* A shape recognition module using YOLOv11n-seg to classify round and sharp-edged particles, thus improving material separation accuracy and reducing mechanical wear on the belt. * A conveyor misalignment detection and correction mechanism that was developed and tested on a scaled-down prototype. * A thermal monitoring and fire prevention unit based on an MLX90640 thermal camera. Figure 25: Full BECS conveyor integration: left—line-scan camera for material detection, center—thermal camera and lighting for heat monitoring, and right—Raspberry Pi with sensors and an alert system. * An IoT-based alert system for real-time notification and remote intervention. These modules form a unified, innovative system that significantly improves operational reliability and workplace safety in high-risk environments. The findings demonstrate that visual recognition based on particle shape can be critical in reducing belt damage while enhancing separation quality. Though tested on a prototype due to hardware access restrictions, the misalignment correction system showed reliable real-time performance under simulated industrial conditions. Thermal monitoring using low-cost hardware successfully detected abnormal temperature rises, proving the practicality of integrating such systems into ECS environments for early fire hazard prevention. Additionally, the IoT-driven alert mechanism substantially reduced technician response time to safety threats, validating its potential for broader industrial applications. Despite these achievements, several limitations emerged during the implementation phase. Due to equipment-sharing constraints and laboratory policies, direct testing on the full-scale BECS conveyor was impossible. Furthermore, limitations in available funding and infrastructure prevented the use of high-torque motors necessary for full-scale belt alignment correction. While highly effective on a GPU-powered laptop, the particle recognition model posed challenges for real-time deployment on the resource-constrained devices typically found in production lines. Future work will focus on resolving these limitations. Key directions include applying model compression techniques, such as pruning and knowledge distillation, to enable YOLOv11n-seg deployment on edge devices like Raspberry Pi or Jetson Nano. The misalignment correction module will be upgraded with industrial-grade motors and tested on actual ECS hardware. On the safety side, more advanced smoke detection systems and wider field-of-view cameras will be integrated to improve fire risk mitigation. Finally, the system will be extended to support PLC integration, enabling automated machinery shutdown in hazardous conditions and further enhancing the system's practicality and industrial applicability. Conceptualization, S.K. ; methodology, S.K. ; software, S.K. ; data curation, S.K. ; formal analysis, S.K. ; visualization, S.K. ; writing--original draft preparation, S.K. ; supervision, B.L. ; project administration, B.L. ; writing--review and editing, S.K. All authors have read and agreed to the published version of the manuscript. This research was funded by The Federal Ministry of Education and Research (BMBF), Germany. The SoRec project has received funding from the \"Digital GreenTech 2\" research and innovation program under grant agreement 02 WDG1695A. We acknowledge the financial support from the Open Access Publishing Fund of Clausthal University of Technology. Not applicable. This study did not involve humans or animals, and it only utilized machine systems and non-personal data. Not applicable. This study did not involve human participants.