Construction of a full-chain model of municipal solid waste sorting based on digital technology and case studies

Abstract

[Objective] In the context of mandatory municipal solid waste (MSW) classification, traditional waste sorting practices face challenges in terms of participation rates and accuracy. In view of the increasing waste generation and resource recycling demand, the management and disposal level of MSW need to be further improved. This study aimed to thoroughly explore how digital technology paths enhance and transform waste sorting models, promoting the sustainable development of resource recycling and environmental protection. [Methods] This research conducted empirical case studies on the different application scenarios and practical cases of digital technology at various stages of domestic waste management, including generation, transfer, collection, and final disposal. It provided an in-depth analysis of different cases by integrating advanced algorithms such as deep learning and genetic algorithms. [Results] The application of digital technology at the front-stage can improve the participation rate and accuracy of MSW disposal and predict future waste production trends, with errors within 21.94%. The application in the middle-stage optimizes the inefficiencies in the traditional model of waste collection and transportation, planning the optimal routes using genetic algorithms to achieve unified objectives of economic cost, carbon emissions, and collection efficiency. The application at the end-stage helps enhance the overall efficiency of waste treatment and disposal, achieving a net benefit increase ranging from 8% to 39.7%. [Conclusion] This paper proposes a new model for waste sorting based on digital technology and centered on full-chain management. By leveraging information and automation technologies, it provides more efficient and precise solutions for waste sorting. Digital technology addresses the information gaps in traditional waste sorting methods, while the full-chain approach coordinates the front-end, mid-end, and back-end stages of waste sorting. This model overcomes the shortcomings of traditional waste sorting methods, enhances the recycling rate of typical urban waste, and achieves sustainable urban waste management.

Keywords

• municipal solid waste|waste sorting|digital technology|deep learning|full-chain