期刊
JOURNAL OF MATERIAL CYCLES AND WASTE MANAGEMENT
卷 23, 期 4, 页码 1563-1575出版社
SPRINGER
DOI: 10.1007/s10163-021-01237-0
关键词
Lifespan distribution; Population balance model; Recycling; Motorcycle; Two-wheeler; ELV
资金
- JSPS KAKENHI [26281056, 20K20013, 19H04329]
- Environment Research and Technology Development Fund [S-16]
- Grants-in-Aid for Scientific Research [19H04329, 20K20013] Funding Source: KAKEN
This study aims to estimate the number and material content of obsolete motorcycles in Vietnam by disassembling a motorcycle, with results indicating a 2.6-fold increase in the number of obsolete motorcycles in 2030 compared to 2010. Feasibility of intermediate treatment facilities for managing obsolete motorcycles in Vietnam is determined through a techno-economic assessment.
The demand for various modes of transportation has significantly increased around the world due to rapid urbanization, the increase in population, and changes in lifestyle. This inevitably is associated with a significant increase in the number of end-of-life vehicles (ELVs). In particular, the number of obsolete motorcycles is rapidly increasing in developing countries. In Vietnam, environmental pollution and the dissipation of resources are the consequences of not having a system to properly treat and recycle obsolete motorcycles. Towards finding a solution to these problems, the focus of this study was to develop the compositional data by disassembling a motorcycle and then estimating the number of obsolete motorcycles and the amount of material contained in them. A population balance model, logistic function, and Weibull distribution were used to estimate this data for the period 2010-2030 in Vietnam. The results indicate that the number of obsolete motorcycles in 2030 will be 2.6 times more than in 2010. Finally, to consider the appropriate management strategy of obsolete motorcycles in Vietnam, the feasibility of intermediate treatment facilities is determined through a techno-economic assessment.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据