Innovative collaboration: How the KISS project works
The primary goal of the KISS project is the development of an AI platform that classifies used plastics based on their quality and type using advanced algorithms.
The foundation of the RIGK-led initiative is built on two key components: data collected on recyclate quality during the project and existing material flow data on plastic recovery. This information is supplemented by comprehensive laboratory testing and analyses conducted by RIGK’s PlastCert department and external partners RAM and Veridis. The objective is to precisely analyze the material composition and determine the optimal recycling processes for different types of plastics.
Building on this foundation, plastship – a RIGK subsidiary responsible for developing the AI – is establishing a comprehensive database on recycled plastics. This database serves as a critical component for AI classification, collecting information about the quality and application potential of recyclates to ensure the AI makes informed decisions for identifying the best recovery processes.
Project partner RAM plays a crucial role by analyzing specks and inclusions in plastics, taking key production parameters into account. The goal is to improve the quality of recyclates, reduce waste, and enhance the overall efficiency of recycling processes. The recyclate data integrated into the database enables insights into correlations and causations within the recycling process. These findings will subsequently be incorporated into AI analysis and used by RAM to optimize production processes in their own project domain.
Another key partner, Veridis, specializes in analyzing the material composition of plastics. Their tests and analyses provide precise data that help the AI identify the best recovery processes.
Early results and ongoing testing
Between July and December 2024, initial test series were successfully conducted, providing valuable data for developing the AI platform.
“The tests are a pioneering effort to establish the first assessment system for recyclates, create data where none existed before, and understand how we can classify and process used plastics even more efficiently,” explains Andreas Bastian, Managing Director of plastship.
The project-specific database ensures that all test results are efficiently captured and systematically analyzed. This creates a solid foundation for making recyclates comparable by quality, allowing the AI to continually enhance the recovery system.
Impact on the circular economy and sustainability
The KISS project significantly advances the circular economy in Hesse and beyond. By generating new data and leveraging AI, the project ensures that used plastics are processed more efficiently, resource utilization is optimized, and the environmental footprint is reduced. Improved recycling processes and the use of recyclates also lower CO2 emissions, contributing directly to environmental protection.
“We are confident that the results of this project will make a significant contribution to the circular economy and sustainability not only in Hesse but also on a broader scale,” says Anne Biehl, Business Development Manager and Project Lead at RIGK.
Outlook and future development
The KISS project is supported over three years by the Hessian Ministry of Economics, Energy, Transport, and Housing under the "Resource Transition Hesse" funding initiative and is accompanied by Technologieland Hessen. Initial results highlight the vast potential of AI-driven optimization of material flows. The project team is optimistic that this technology can be further expanded and applied to other regions in the coming years.
With a strong team and a clear strategy, the KISS project is poised to make a meaningful contribution to the circular economy and drive resource conservation in the years ahead.
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