Application of artificial intelligence to enhance collection of E-waste: A potential solution for household WEEE collection and segregation in India

Abstract

Our society has undergone a massive technological revolution over the past decade and electronic appliances have now become ubiquitous. The increase in production of electronic products and the growing inherent need to own the latest technology available has led to a significant increase in the amount of E-waste produced each year. India generated 3.2 million tonnes of E-waste in 2020, with metropolitan cities like Mumbai, Delhi and Bangalore leading the list. Proper management and recycling of E-wastes are critical for the sustainability of any modern city today. While industrial and commercial collection of E-wastes has been in the spotlight, solutions for collection of E-wastes from individual households are limited. This article proposes the implementation of a mobile robot that identifies common electronic wastes based on transfer learning and serves as an attachment to existing municipality garbage trucks. The robot moves around, identifies electronic wastes and performs segregation of the identified material via its arm-based lift and storage mechanism. A convolutional neural network–based identification system has been employed for categorising the E-wastes and yields 96% accuracy. This is a first of its kind attempt, especially in India, to collect and segregate E-wastes from homes and individuals. The system will relieve unskilled labour from the hazardous process while providing a 20% decrease in costs over a 5-year period. The application of this article aims to provide a viable mobile solution for E-waste collection from households with minimal human intervention.

Economic and Environmental Sustainability of Biogenic Synthesis of Indium-Graphitic Material from Electronic Waste

“…Larger-scale operations have the potential to provide more opportunities for the implementation of cost-effective and mechanized processes such as artificial intelligence (AI) and machine learningassisted e-waste separations, as well as automated phytoextraction systems, thereby reducing the reliance on laborintensive methods. 17,18 For the more optimized BG2 scenario, the cost per gram GM synthesis is estimated at 0. The categories for environmental impacts include energy, operation, capital, and direct emissions.…”

“…For the BG1 scenarios, the capital and operation of the process is a critical driver per gram GM synthesis cost, whereas the contribution from the labor cost is much lower potentially because of the small scale of operation. Larger-scale operations have the potential to provide more opportunities for the implementation of cost-effective and mechanized processes such as artificial intelligence (AI) and machine learning-assisted e-waste separations, as well as automated phytoextraction systems, thereby reducing the reliance on labor-intensive methods. , …”

Artificial intelligence for waste management in smart cities: a review

“…Shreyas Madhav et al ( 2022 ) have developed a convolutional neural network-based recognition system for e-waste classification. The system can classify e-waste into eight categories with 96% accuracy, potentially leading to a 20% cost reduction within five years if implemented as a replacement for manual classification.…”

Modelling the Barriers to Circular Economy Practices in the Indian State of Tamil Nadu in Managing E-Wastes to Achieve Green Environment

“…These numbers are expected to increase in the coming years, and presently, India is ranked fifth in the e-waste generation. Many developed nations are viewing India as an e-waste dump yard for reasons such as cheap labour [7]. Imported e-waste and domestic e-waste makes e-waste management a critical problem for India.…”