| Title |
Industrial Symbiosis: Exploring Big-data Approach for Waste Stream Discovery |
| ID_Doc |
8126 |
| Authors |
Song, B; Yeo, Z; Kohls, P; Herrmann, C |
| Title |
Industrial Symbiosis: Exploring Big-data Approach for Waste Stream Discovery |
| Year |
2017 |
| Published |
|
| DOI |
10.1016/j.procir.2016.11.245 |
| Abstract |
Industrial symbiosis is a way to realize circular economy in which waste streams from industrial production and related activities are collected, reused or recycled into resources. The concept is increasingly incorporated into new company design and industrial park development. There also exist waste management companies who collect and process certain waste streams for economic gains. However, industrialized cities typically experience difficulty to increase their total recycling rate due to the lack of complete and detailed data on the types, quantity, and location of waste streams generated, and hence the economically-viable collection and processing of many waste streams. This paper explores and discusses the feasibility and methods for a big data approach to obtain necessary data for discovery of potential industrial symbioses within the perimeter of a large industrialized city. The aim is to realize the objective of embedding industrial symbioses as an essential part of sustainable urban living and management. (C) 2017 The Authors. Published by Elsevier B.V. |
| Author Keywords |
Big Data; Industrial Symbiosis; Waste reuse and recycle; urban sustainability |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000404511900061 |
| WoS Category |
Engineering, Industrial |
| Research Area |
Engineering |
| PDF |
https://doi.org/10.1016/j.procir.2016.11.245
|