A novel bulk density-based recognition method for kitchen and dry waste: A case study in Beijing, China. (1st August 2020)

• Record Type: Journal Article
• Authors: Li, Zhonglei; Wang, Qingwei; Zhang, Tao; Wang, Hongtao; Chen, Tan
• Is Part Of: Waste management. Volume 114(2020)
• Page Start: 89
• Page End: 95
• Publication Date: 2020-08-01
• Subjects: Household solid waste -- Bulk density -- Waste source separation -- Recognition -- Regression analysis; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2020.07.005
• Languages: English
• ISSNs: 0956-053X

Abstract

Graphical abstract: Highlights: A novel bulk density-based recognition method for kitchen and dry waste. Wastes physical properties were analyzed by ANOVA and linear regressions. Dry, mixed, and kitchen waste are discriminated as: <115, 115–211 and > 211 kg/m 3 . Source-separated community was observed to determine the feasibility method. Abstract: Identification technology of household kitchen and dry solid waste has played a major part in improving the accuracy of residents' separation by intelligent outdoor trashcan, which is an effective integral solid waste management tool for growing household solid waste (HSW). Our study aims to present a novel and simple recognition method for kitchen and dry waste based on bulk density. In three communities in Beijing, 270 bagged waste samples were collected, and their moisture content, separation accuracy, and bulk density, characterized. Then a bulk density index was developed to straightforwardly express residents' waste source separation accuracy by linear regression analysis above physical properties. In the 3 Beijing communities, we demonstrated a clear distinction in the bulk density index, for dry, mixed, and kitchen waste of <115, 115–211, >211 kg/m 3, respectively. Our results provide a theoretical basis for the establishment of an intelligent waste supervision system, which is of great significance for waste management in developing countries like China.