## 3 Methods
### Enhancing Conveyor Belt Monitoring with IoT-Based Alert Systems  
In the recycling industry and separation processes, particularly in BECS systems, the conveyor belt plays a critical role in material transportation and improving separation accuracy. However, challenges, such as belt misalignment, abnormal temperature increases, and unwanted objects like ferromagnetic materials, can disrupt system performance, cause production downtime, or even lead to fire hazards [77]. Implementing an IoT-based alert system enables conveyor belt monitoring, automatic issue detection, and reduced response time. This system leverages smart sensors, computer vision, image processing, and communication technologies to analyze collected data and send immediate alerts upon detecting anomalies. One of the most effective ways to monitor conveyor belt conditions is by integrating smart sensors and industrial cameras into an IoT network, which processes and analyses operational data [78; 79]. In our project, combining various technologies has enabled more precise monitoring. Thermal cameras have been employed to identify hot spots on the conveyor belt, which may result from friction, belt misalignment, or ferromagnetic material accumulation. Additionally, a line scan camera has been used to examine the shape and type of materials transported on the conveyor, allowing for the detection of sharp objects that could damage the belt. Alongside these hardware components, Raspberry Pi and IoT modules [80] handle edge computing for local data processing and send immediate alerts via communication channels when anomalies are detected. The system integrates OpenCV and lightweight machine-learning models [81; 82; 83] for anomaly detection. For instance, belt misalignment is detected by analyzing the movement pattern of a reference white line on the belt. Similarly, thermal data captured by cameras are processed to analyze temperature fluctuations, triggering alerts when abnormal hot spots are detected, which could indicate excessive friction or the presence of unwanted materials. The system can also identify unauthorized objects, such as iron fragments or angular debris, which could cause belt abrasion or physical damage. These capabilities transform the system from a simple alert mechanism into an advanced analytical and preventive tool, optimizing operations and reducing unexpected failures. Instead of relying on traditional monitoring methods, which require manual inspection and lead to delayed problem detection, the IoT-based alert system sends instantaneous notifications upon detecting anomalies. This system includes two levels of alerts. Local alerts activate an on-site alarm buzzer and warning lights, allowing operators to respond immediately. In addition, remote notifications are sent via a Telegram bot to technicians, providing thermal images and temperature analysis of the conveyor belt. Suppose the temperature exceeds a critical threshold (e.g., 40 \({}^{\circ}\)C). In that case, the system sends a text alert and initiates an automatic call to ensure the responsible personnel receives the warning. This approach significantly enhances reaction speed, minimizes equipment damage risks, and prevents sudden production halts. A key challenge in implementing intelligent industrial systems is dealing with hardware limitations and harsh working environments (e.g., dust, environmental noise, and high energy consumption). In our project, multiple strategies were adopted to optimize system performance and increase stability. One of these strategies is hybrid processing (edge computing + cloud computing), where local processing is performed on the Raspberry Pi and only essential data are sent to high-performance computing systems (such as a GPU-equipped laptop). Additionally, power management techniques have been applied to adjust the frame rate of cameras and regulate data transmission based on emergency levels. For instance, if the conveyor belt temperature remains safe, high-load processing is disabled, thus saving computational resources.