Real circular economy of natural fiber-based material systems (CE4NWS)

• Subject Area: Materials Engineering ; Computer Science ; Mechanics and Constructive Mechanical Engineering ; Polymer Research ; Production Technology ; Systems Engineering ; Economics
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 549138046

Natural fibre-based material systems (NMS) provide a transformation pathway toward sustainable production and consumption. Their material properties such as low density, high tensile strength, high impact resistance and low thermal conductivity qualify NMS for various applications in automotive, construction and other industries. Currently, NMS are typically used for energy production at the end of their life cycle. As the volume of End-of-Life products and components made from NMS increases in the future, valuable resources will be wasted. There are barriers to the transformation from linear to circular economy. In particular, there is a lack of knowledge about (secondary) materials and their processing, sorting, classification and further reuse in closed- and open-loop recycling processes. Additionally, barriers affect the development and operation of a robust recycling network at regional or national level. The research aims to use qualitative and quantitative methods to provide a comprehensive understanding of the technical and socio-economic barriers and drivers of an effective circular economy. The focus is on secondary materials from automotive and construction sectors. The aim is to analyse the dependencies of processes and materials as well as the performance and process control of various treatment and further processing in material and chemical recycling paths in order to analyse and quantitatively describe the production of high-quality recycling fractions on the basis of series of measurements. Using a modular, digital model (framework) based on comprehensive data on the properties of the secondary material and process parameters, the optimal recycling paths and their process and material parameters for the recycling are to be identified and optimised. Artificial intelligence (AI) methods are being validated with regard to their performance and quality for Closed- and Open-loop recycling. AI methods such as Explainable AI (XAI), federated AI and Transfer Learning are applied. The development and operation of robust recycling system also requires knowledge about secondary material availability and volumes, market entry barriers, environmental impacts and the behaviour of the market and its players. A system-dynamic, agent-based approach will be methodically tested and validated. The research thus provides holistic and transferable findings and methods for the transformation from linear to circular economy systems.

• DFG Programme: Research Impulses
• Applicant Institution: Technische Hochschule Rosenheim
• Spokesperson: Professorin Dr.-Ing. Sandra Krommes
• Participating Researchers: Professor Dr.-Ing. Sebastian Bayerl; Professor Peter Karlinger; Professorin Dr.-Ing. Manuela List; Professor Dr. Jan-Diederich Lüken; Professor Dr. Andreas Michanickl; Professor Dr.-Ing. Norbert Müller; Professor Dr.-Ing. Martin Versen; Professor Martin Würtele