# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 1 Introduction and Background
### Sensor-Based Systems to AI: Advancing Waste Sorting  
Traditional automated sorting systems, which predominantly utilize programmable logic controllers (PLCs) linked to sensor networks, face challenges in processing mixed or unorganized waste streams. PLCs, extensively used in the domain of industrial automation, conventionally employ devices such as proximity sensors, limit switches, and push buttons to obtain electrical input from the physical environment. Although these sensors exhibit a high degree of efficiency in detecting the presence and proximity of objects, they demonstrate limited capability to differentiate between different materials such as plastic, metal, and paper. In mixed waste streams, the inability to distinguish between dissimilar materials obstructs the sorting process, thereby complicating the precise segregation of recyclable waste. In addition, the diversity of geometries, dimensions, or constituent materials presented by such waste streams makes conventional sensor-based systems insufficient for current requirements. [PERSON] et al. [6] concluded that conventional sensor-based systems are ineffective in classifying objects with arbitrary shapes and orientations in industrial settings, which requires sophisticated vision systems capable of real-time classification, especially for complex sorting operations. [PERSON] et al. [7] demonstrated the effectiveness of integrating vision systems with PLCs for industrial pick-and-place robots, highlighting improvements in object detection accuracy, real-time processing, and the flexibility of PLC-controlled robotic systems in sorting tasks. One of the critical challenges in modern waste management is efficiently sorting and recycling waste, particularly in urban and industrial settings, where the volume and complexity of waste streams continue to grow. Through their study on the influence of AI in waste sorting, [PERSON] et al. [8] illustrated that integrating AI into waste management systems considerably improves the efficiency of recycling and the purity of the material. The \"ZRR for municipal waste project\" underscores the tangible effects of AI on operational efficiency and safety, thus highlighting its potential to advance a sustainable circular economy. The use of AI and Machine Learning (ML) has shown promise in automating this process and enhancing the accuracy of the sorting, as demonstrated by several recent studies. [PERSON] et al. [9] reviewed recent advances in the integration of ML and AI with spectroscopic methods and machine vision for the classification of plastic waste. Their investigation highlights advances in automated technologies that can achieve up to 99.99% sorting accuracy and processing speeds of 10 tons per hour [9]. Although challenges persist in the classification of complex films and plastics, AI-driven systems have significantly improved accuracy and throughput, indicating a positive trajectory for advancing recycling technologies. [PERSON] et al. [10] investigated an AI-powered sorting robot that utilizes image recognition and advanced grippers to classify containers such as PET bottles and colored glass to tackle Japan's recycling challenges. [PERSON] et al. [11] designed an intelligent waste bin powered by GECM-EfficientNet, achieving above 94% accuracy in waste classification on two datasets. Machine vision systems, particularly those that use deep learning algorithms, can classify objects based on visual data, enabling more accurate real-time sorting [12]. [PERSON] et al. [13] developed a system for the automatic classification of waste materials using computer vision and ML techniques. This system utilizes both Near-Infrared (NIR) and RGB cameras to analyze and identify incoming objects, achieving substantial classification accuracy. Similarly, [PERSON] et al. [14] explored the application of AI in public waste sorting through an automated bin system, the \"Waste Wizard\", deployed in public spaces such as zoos and retail stores.