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.
Highlights
Artificial intelligence and the use of robots in sorting could, in the future, make a significant contribution to the production of high-quality secondary raw materials from waste, in the sense of a recycling economy
Within the framework of a pilot study financed by the European Institute of Innovation and Technology (EIT) Climate-KIC, a European research consortium led by Ferrovial Services, NTU International and the Wuppertal Institute tested and evaluated such an artificial intelligence (AI) solution
Using the process presented, 500 to 1000 pieces per fraction were collected for the 13 different materials for robot identification training and manually fed to the sorting belt
Summary
Artificial intelligence and the use of robots in sorting could, in the future, make a significant contribution to the production of high-quality secondary raw materials from waste, in the sense of a recycling economy. The results have been evaluated with regard to sorting quality and purity of the relevant material flows, as well as initial estimates from a socio-economic perspective. The results allowed first conclusions to be drawn on both opportunities and risks of digital waste management: heterogeneous waste streams, such as household waste, still pose considerable challenges for robotic systems and offer impressive learning curves for the development of sorting quality during the test phase. It is clear that technological solutions alone cannot make the leap to recycling management—but they are becoming an increasingly important part of a transformation of the entire value chain, from product design to disposal
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