.png){kind=icon}
.png){kind=icon}
Return
A critical review on the key issues and optimization of agricultural residue transportation
.png){kind=icon}
OA
AI
S
X
尹
A
DOI：10.1016/j.biombioe.2021.105979.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In recent years, residue-to-energy utilization has become an important energy source that can alleviate the reliability of fossil fuel, and agricultural residue (AR) transportation has great potential for optimization in the AR supply chain. However, various methods presented make their correct implementation difficult for sustainable optimization. Therefore, four key issues of the AR transportation for bioenergy production have been thoroughly reviewed: (i) distribution of AR in cropland, (ii) estimation of transportation distance, (iii) variability of vehicle arrangements, and (iv) (de)centralized transportation patterns. This review has several findings. To begin with, land cover classification method with remote sensing technology is suggested to locate the distribution of AR, thereby being helpful for facilitates decision-making optimization. Second, electronic navigator applications (Google Maps or Baidu Maps) are recommended for transportation distance estimation, and the implementation of manipulating Baidu Maps with R is provided. Third, the vehicle selection bias would bring estimation gaps, so a vehicle database is required to satisfy the local needs. And finally, transportation distance, the requirement of AR pretreatments and brokers' participation can affect the decision of (de)centralized transportation patterns. The findings and technical details discussed are especially useful for researchers and bioenergy project investors who intend to reduce the AR transportation cost in the future, especially for developing counties.
Keywords:
Agricultural residue (AR)
Land cover classification
Baidu maps
(De)centralized transportation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.8
Papers:
8.4K
Citations:
2.2W