| Title |
Artificial Intelligence in the Sorting of Municipal Waste as an Enabler of the Circular Economy |
| ID_Doc |
28241 |
| Authors |
Wilts, H; Garcia, BR; Garlito, RG; Gómez, LS; Prieto, EG |
| Title |
Artificial Intelligence in the Sorting of Municipal Waste as an Enabler of the Circular Economy |
| Year |
2021 |
| Published |
Resources-Basel, 10.0, 4 |
| DOI |
10.3390/resources10040028 |
| Abstract |
The recently finalized research project "ZRR for municipal waste" aimed at testing and evaluating the automation of municipal waste sorting plants by supplementing or replacing manual sorting, with sorting by a robot with artificial intelligence (ZRR). The objectives were to increase the current recycling rates and the purity of the recovered materials; to collect additional materials from the current rejected flows; and to improve the working conditions of the workers, who could then concentrate on, among other things, the maintenance of the robots. Based on the empirical results of the project, this paper presents the main results of the training and operation of the robotic sorting system based on artificial intelligence, which, to our knowledge, is the first attempt at an application for the separation of bulky municipal solid waste (MSW) and an installation in a full-scale waste treatment plant. The key questions for the research project included (a) the design of test protocols to assess the quality of the sorting process and (b) the evaluation of the performance quality in the first six months of the training of the underlying artificial intelligence and its database. |
| Author Keywords |
artificial intelligence; circular economy; municipal solid waste |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000643669400001 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://www.mdpi.com/2079-9276/10/4/28/pdf?version=1617937875
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