Metabolic network optimization for surface treatment waste based on the fusion of administrative data and web textual data

Abstract

Surface treatment waste (HW17) contains a large amount of toxic and corrosive components, which may be harmful to the public health if improper disposal. To understand its metabolism is a prerequisite for the management optimization. In this study, the metabolic network for HW17 in Chengdu city (Sichuan Province, Southwestern China) is constructed by fusion of administrative data and web textual data to identify the associated issues through an investigation of the waste flow and direction. On such basis, a dual-objective mixed-integer linear programming model, with three scenarios (cost minimization, risk minimization, both the cost and risk minimization), is established to optimize the metabolic network for enhancing the management efficiency. The results show that the total HW17 in 2019 is 32,846.14 tons in Chengdu city. Of this, 0.4% of HW17 does not have downstream metabolic paths, and only 23.51% of it is completely disposed. About 78.08% of the generated HW17 is transported outside the metropolitan area of Chengdu city for disposal, highlighted by an open-loop feature. After optimization, the network cost reduces by 3.91% among the three scenarios, and risk decreases by 87.85%. In view of these results, this study further discusses the capacities of the recycling centers and disposal centers, as well as the limitations and uncertainty. Managerial implications are proposed, to lay a foundation for the improvement of waste management sustainability.