Exemple of a valorization network based on a 10% collection rate from potential biowaste in the Grand Lyon territory (France)
| Main Author: | Thiriet |
|---|---|
| Format: | info dataset Journal |
| Bahasa: | eng |
| Terbitan: |
, 2018
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1314618 |
Daftar Isi:
- This study, conducted in the frame of the H2020 DECISIVE project, aims at developing a method to design a decentralized and small-scale AD (mAD) network in urban and peri-urban areas. A mixed integer linear program (MILP) was set up based on the proposed system. It aims at minimizing the impacts of the biowaste and the digestate transportation by minimizing their payload-distances while taking into account notably the technical constraints of the newly developed micro-AD. A Geographic Information System (GIS) based methodology was developed to feed the MILP model with very fine-scale data required to optimized a proximity treatment system. The method allows to locate and estimate the biowaste generation and to locate the digestate outlets, the agricultural areas, and to estimate the maximal amount of digestate usable. The candidate sites for mAD are identified with a GIS multi-criteria analysis that includes the environmental regulations, some urban planning rules and the site accessibility and heat outlet valorization. The method developed is successfully applied in the territory of The Grand Lyon Metropole (534 km2), located in France. The MILP model succeeds at providing a solution even with the very large problem studied (≥108 possible combination). Different scenarios can be easily tested to meet the potential needs of the stakeholder: the quantity of biowaste to treat, the type of sources to target, the synergy with the current treatment solution, etc. The data are provided through the open, non-proprietary GeoPackage files (GPKG) commonly recognized in GIS tools. A complementary xml file describe the metadata in compliance with the Inspire directive. A complementary pdf file describe the fields of the datasets.