聚氨酯废弃物升级回收系统的逆向供应链网络设计
Reverse Supply Chain Network Design of a Polyurethane Waste Upcycling System
AI总结：
本文提出一个MILP框架，优化聚氨酯废弃物升级回收的逆向供应链网络，以最小化成本并确定设施布局，案例显示当前经济性不敌化石原料，但激励下潜力巨大。
摘要：
本文提出了一个通用的数学规划框架，用于塑料废弃物升级回收供应链基础设施的设计与优化。为此，构建了一个多产品、多层级、多周期的混合整数线性规划（MILP）模型。该模型的目标是在满足从消费后塑料废弃物收集到生产与原生料等效的高价值聚合物这一整个循环供应链中大量约束（从收集配额到原料质量）的前提下，最小化总成本。该框架旨在通过确定各类设施的最优数量、位置和规模，以及在指定时期内供应链网络各节点之间运输的物料量，来支持未来循环供应链的战略规划。该框架的功能已通过一个案例研究进行了测试，该案例针对德国建筑工地产生的硬质聚氨酯泡沫废弃物的升级回收。评估了经济潜力和基础设施需求，结果发现，仅从经济角度来看，当前价值链的状态无法与化石基原料或焚烧竞争。然而，在正确的经济激励下，一旦升级回收技术成熟且经济框架条件稳定，建立此类价值链具有相当大的潜力。
英文摘要：
This paper presents a general mathematical programming framework for the design and optimization of supply chain infrastructures for the upcycling of plastic waste. For this purpose, a multi-product, multi-echelon, multi-period mixed-integer linear programming (MILP) model has been formulated. The objective is to minimize the cost of the entire circular supply chain starting from the collection of post-consumer plastic waste to the production of virgin-equivalent high value polymers, satisfying a large number of constraints from collection quota to the quality of the feedstock. The framework aims to support the strategic planning of future circular supply chains by determining the optimal number, locations and sizes of various types of facilities as well as the amounts of materials to be transported between the nodes of the supply chain network over a specified period. The functionality of the framework has been tested with a case study for the upcycling of rigid polyurethane foam waste coming from construction sites in Germany. The economic potential and infrastructure requirements are evaluated, and it has been found that from a solely economic perspective, the current status of the value chain is not competitive with fossil-based feedstock or incineration. However, with the right economic incentives, there is a considerable potential to establish such value chains, once the upcycling technology is ready and the economic framework conditions have stabilized.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
