## 2 Supplementary Literature and Related Work  
Eddy current separators (ECSs) have become indispensable in industrial recycling because they can separate non-ferrous metals, such as aluminum and copper, from waste streams containing plastic, wood, and glass [4]. A critical component of this system is the conveyor belt, which not only facilitates the movement of materials, but also significantly influences the quality and precision of the separation process. Any deviation or misalignment in the belt can alter the trajectory of materials, reducing separation efficiency and damaging equipment. Prior research has addressed this problem using various sensor-based approaches. For instance, studies have implemented displacement sensors [49], LEM ATO-B10 current sensors [50], optical and acoustic sensors [51], and distributed fiber optic sensors (DOFS) [52] to detect belt deviations. While effective to some degree, these sensor systems are often limited by their single-functionality, sensitivity to environmental noise (dust, vibration, and heat), high maintenance demands, and inability to integrate with multi-fault detection tasks, like identifying sharp objects or overheating. Most notably, they lack corrective mechanisms for real-time belt alignment. More advanced methods using machine vision have emerged to overcome these limitations. A study by [PERSON] et al. [53] applied OpenCV for misalignment detection. However, the proposed correction method was incompatible with BECS systems due to their unique belt structure, which involves four non-tensionable rollers. Similarly, studies by [PERSON] [54] and [PERSON] [51] designed correction mechanisms tailored for mining systems, which are mechanically and functionally different from BECS conveyor setups. Hence, their applicability remains restricted. Deep learning-based misalignment detection has also been explored. For example, [PERSON] et al. [55] and [PERSON] et al. [44] implemented YOLO-based and ARIMA-LSTM models for fault diagnosis. Although they reported high accuracy (up to 99.45%), these systems depend on GPU resources, are expensive to deploy in real-time industrial environments, and offer detection only--not active correction. In contrast, the proposed study introduces a lightweight, real-time misalignment detection and correction module using a Raspberry Pi camera and stepper motors. This system provides dual functionality, minimal calibration, and can adapt to multiple conveyor-related anomalies. Moreover, it operates efficiently without GPU support, making it highly suitable for embedded industrial environments. Fire detection in BECS systems has been another concern, as overheating due to trapped ferromagnetic materials poses a significant risk. Traditional smoke and gas-based detection systems [36; 39; 40] are prone to false alarms in dusty environments, undermining safety protocols. Distributed Temperature Sensing (DTS) [41] offers high accuracy but is cost-prohibitive for small BECS setups and lacks visual confirmation of fire risks. More recently, thermal cameras have been proposed as an effective alternative [42; 43]. They provide pixel-level temperature maps that facilitate early detection of heat anomalies. The present study integrates thermal imaging with IoT-based Telegram notifications, enabling remote alerts and reducing reliance on human presence. Unlike conventional systems [37], this method supports visual confirmation, early intervention, and real-time monitoring tailored for BECS environments. Sharp foreign objects, such as iron scraps, can damage conveyor belts, disrupt separation, and cause injury. Traditional object detection systems relying on fiber-optic [37], infrared [40], or electromagnetic sensors [39] often lack adaptability, precision, or remote alert features. Deep learning models such as lightweight YOLOv4 have shown promise, achieving 93.73% accuracy at 70.1 FPS in foreign object detection [56]. However, they require high-power GPUs and are usually designed for large-scale systems.