Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities

Abstract

Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.

Recycling of Electrical Cables—Current Challenges and Future Prospects

“…The authors suggest that such a solution, implemented at the collection, segregation, and recycling levels, relieves human workers and can significantly reduce waste management costs [105]. Similar solutions were proposed by Mohammed et al [106]. The authors developed an automated waste sorting and classification system based on artificial neural networks supported by nuclear fusion techniques.…”

“…The authors developed an automated waste sorting and classification system based on artificial neural networks supported by nuclear fusion techniques. The project, which utilizes various digital models and machine learning, is effective and achieves high accuracy (approximately 91%) [106]. The last example of intelligent algorithm utilization is one developed and described by Ziouzios et al [107].…”

Multi-class Waste Classification Using Convolutional Neural Network

“…The fourth Industrial Revolution has introduced waste sorting methods utilizing machine learning based on image data, replacing manual sorting methods. Several machine learning methods for waste classification include Bayesian networks [9], Artificial Neural Networks (ANN) [10], K-Nearest Neighbor, random forest, and Gaussian naive bayes [11].…”

An Assessment of Drivers and Barriers to Implementation of Circular Economy in the End-of-Life Vehicle Recycling Sector in India

“…Implementing the CE in India's ELV recycling sector requires a practical framework to meet the CE's goals and enhance the CE initiatives. With the application of information and communication technologies, an innovative framework needs to be developed to enhance sustainability and facilitate the implementation of a CE [86,87]. That framework appropriately encompasses the development of concepts and key terminology, setting targets, identifying indicators to assess enhancements toward the goals, designing a proper methodology, data collection and analysis, enhancing materials recovery, and protecting the environment.…”