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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 01 | Jan 2026

p-ISSN: 2395-0072

www.irjet.net

A REVIEW PAPER ON e - WASTE SEGREGATION AND MANAGEMENT Jyotsna Gawai1 ,Vaibhav Pawar2,Bhavika Bele3,Shantanu Bodele4,Pratik Kulte5 1Head f Dept , Dept of Electronics and Telecommunication, KDK College of Engineering, Maharashtra, India 2345UG student, Dept of Electronics and Telecommunication, KDK College of Engineering, Maharashtra, India

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Abstract - The high rate of progress in electronic

disposal approaches may result in the pollution of the soil, water, and air, and efficient management of e-waste has become a worldwide concern.

technologies and the subsequent shortening of the product life cycles has led to an enormous rise in the electronic waste (e-waste), which causes a serious threat to the environment and human health. The proper e-waste management and segregation are thus very important in facilitating sustainable recovery of resources and proper disposal of the toxic elements. This review paper discusses the current ewaste management and segregation technologies comprising manual, semi-automated and fully automated systems with special consideration to how it works, its efficiencies, as well as its limitations. The review finds the major flaws in the existing methods to include high reliance on hand labor, low accuracy of segregation, safety, and poor flexibility to various types of e-waste. This paper will create an intelligent and automated solution to such challenges where a robotic hand-based segregation system is proposed. The proposed system incorporates the use of servo motors, sensors, and Raspberry Pi camera module to facilitate realtime identification and classification of electronic waste. The e-waste is classified into electronic components like printed circuit boards, sensors, processors, glass parts, hazardous metals, and non-metallic products by using advanced and improved deep learning algorithms or YOLOv8 and the use of TensorFlow-based models. The proposed solution is evaluated to increase the accuracy of segregation, operational safety, and decrease human interaction by integrating robotics with computer vision and machine learning. The present review creates a powerful framework in the future studies of intelligent ewaste management systems and emphasizes the potential of AI-based robotics in the attainment of sustainable and effective e-waste segregation.

Different e-waste management and segregation technologies have been created over the years starting with simple manual dismantling, and up to modern advanced technologies. Although manual processes are still common in most parts because of low startup expenses, they are labour-intensive, time conscious, and subject the workers to toxic substances. When processing efficiency has been enhanced due to automated and sensor-based systems, it is most of the time that these systems have a very high cost of implementation, restricted material recognition, and lacked flexibility in handling heterogeneous e-waste streams. These shortcomings bring about the necessity of smart, dynamic, and economical alternatives. In this regard, the combination of robotics, computer vision and machine learning is an avenue to be followed in order to develop e-waste segregation. The present review paper critically evaluates the current technology to establish the weaknesses and suggest a new robotic handbased e-waste sorting system. The system utilizes the deep learning algorithms including the YOLOv8 and TensorFlow to get precision in finding and classifying the electronic components with the assistance of Raspberry Pi camera module. The proposed solution will allow e-waste management to be more efficient, safer, and sustainable by facilitating an accurate sorting of materials such as printed circuit boards, sensors, processors, glass, hazardous metals, and non-metals.

2. LITERATURE REVIEW

Key Words: e-Waste, Electronic Waste Management, Recycling, Sustainability, Urban Mining, EPR, Circular Economy

a) Abdullah et al. (2025) conducted a comprehensive bibliometric analysis on global e-waste management research from 2019–2025 using the Scopus database. The study identified major contributing countries, institutions, and emerging research themes such as metal recovery, policy frameworks, and AI-based solutions. However, the work remains descriptive in nature and does not evaluate the real-world effectiveness of identified technologies or policies. Thus, empirical validation of these trends is still lacking.

1. INTRODUCTION The electronics industry has completely altered the modern life, yet on the other hand it has created one of the fastest growing wastes in the world which is called the electronic waste, or e-waste. E-waste is the discarded electrical and electronic equipment that contains a complex set of valuable materials including metals and reusable parts in addition to hazardous materials that are very dangerous to the environment and human health when not handled appropriately. Poor management and

Impact Factor value: 8.315

b) Ruparel et al. (2024) proposed an AI-based smart bin system using deep learning models (VGG16 and ResNet50) for automatic classification of wet, dry, and electronic

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