| Title |
From Trash to Cash: How Blockchain and Multi-Sensor-Driven Artificial Intelligence Can Transform Circular Economy of Plastic Waste? |
| ID_Doc |
28068 |
| Authors |
Chidepatil, A; Bindra, P; Kulkarni, D; Qazi, M; Kshirsagar, M; Sankaran, K |
| Title |
From Trash to Cash: How Blockchain and Multi-Sensor-Driven Artificial Intelligence Can Transform Circular Economy of Plastic Waste? |
| Year |
2020 |
| Published |
Administrative Sciences, 10.0, 2 |
| DOI |
10.3390/admsci10020023 |
| Abstract |
Virgin polymers based on petrochemical feedstock are mainly preferred by most plastic goods manufacturers instead of recycled plastic feedstock. Major reason for this is the lack of reliable information about the quality, suitability, and availability of recycled plastics, which is partly due to lack of proper segregation techniques. In this paper, we present our ongoing efforts to segregate plastics based on its types and improve the reliability of information about recycled plastics using the first-of-its-kindblockchain smart contractspowered bymulti-sensor data-fusion algorithms using artificial intelligence. We have demonstrated how different data-fusion modes can be employed to retrieve various physico-chemical parameters of plastic waste for accurate segregation. We have discussed how these smart tools help in efficiently segregating commingled plastics and can be reliably used in the circular economy of plastic. Using these tools, segregators, recyclers, and manufacturers can reliably share data, plan the supply chain, execute purchase orders, and hence, finally increase the use of recycled plastic feedstock. |
| Author Keywords |
plastic recycling; circular economy; plastic waste; waste segregation; blockchain; smart contracts; multi-sensor; data fusion; artificial intelligence; AI; sustainability; waste-to-value; landfills |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000551252900006 |
| WoS Category |
Management |
| Research Area |
Business & Economics |
| PDF |
https://www.mdpi.com/2076-3387/10/2/23/pdf?version=1625478356
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