Optimizing geographic locations for electric vehicle battery recycling preprocessing facilities in California
Optimizing geographic locations for electric vehicle battery recycling preprocessing facilities in California
复制标题
优化加州电动汽车电池回收预处理设施的地理位置
DOI：
10.1039/d3su00319a
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Hatzell, Marta C.
中科院分区：
文献类型：
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作者：
Haynes, Megan W.;González, Rodrigo Cáceres;Hatzell, Marta C.
报废的锂离子电池 (LIB) 会带来多种安全风险。具体而言，锂离子电池有可能在运输过程中自燃，在焚烧过程中释放有毒化合物，并可能将污染物渗入垃圾填埋场。废锂离子电池被归类为危险废物，也受到许多政策的约束，并需要由经过认证的人员和公司进行处置。与其他废物相比，这些要求导致运输成本和体积增加。改善锂离子电池回收的努力主要集中在降低成本，使回收在经济上有利可图。最重要的是改进回收技术；然而，运输成本极大地影响了锂离子电池回收的总成本。在这里，我们提供了一个选择无监督机器学习聚类启发式的程序，以确定加利福尼亚州锂离子电池回收预处理设施的最佳位置。确定的分散设施位置最大限度地减少了最终使用部门设施和潜在二次使用地点之间废旧电动汽车电池的运输距离和运输成本。
Spent lithium-ion batteries (LIBs) at end of life pose several safety risks. Specifically, LIBs have the potential to self-ignite during transport, release toxic compounds during incineration, and can leach contaminants into landfills. Spent LIBs, which are classified as hazardous waste, are also subject to numerous policies and require disposal by certified personnel and companies. These requirements result in an increase in transport costs and volume compared to other waste. Efforts to improve LIB recycling focus primarily on reducing costs to make recycling economically profitable. The greatest emphasis is placed on improving recycling technologies; however, transport costs significantly impact the total cost of LIB recycling. Here, we provide a procedure for choosing an unsupervised machine learning clustering heuristic to identify optimal locations for LIB recycling preprocessing facilities in California. The identified decentralized facility locations minimize the transportation distance and the cost of shipping spent electric vehicle batteries between end-use sector facilities and potential second-use locations.