WE.2.E || Mobilizing LCA Resources Through Digital Collaboration
Brinkmann, Tobias; Steinfeldt, Michael; Spuziak-Salzenberg, Detlef; Arndt, Carmen; Carstens, Anna; Albers, Henning; Stührmann, Torben; Germer, Frauke
Especially in the case of long-lived products, the crucial questions regarding the proper implementation and assurance of high-quality recycling targets often arise only after decades. In addition, information about the material composition is often not sufficiently known and adequately documented to the end user. With the extended socio-technical approach of a self-learning and resilient recycling network, such problems can be adequately addressed. On the one hand, this requires knowledge tools to ensure a high-quality material cycle, such as databases for documenting the installed products with their characteristic values for masses and materials used, material flow modeling to track the material flows generated for the end-of-life (EoL) of the products including Life Cycle Assessments (LCA) of recycling and disposal routes, as well as forecasting tools of expected waste quantities. On the other hand, a simulation tool such as agent-based modeling (ABM) is also needed to map options for action and their effects, considering the interests of the stakeholders in relation to the target formulation of the recycling network. The example of rotor blades from wind turbines is used to show how such an approach can be used for a meaningful recovery network. The developed tools and especially their active combination will be presented. In addition, the concrete possibilities for resource-saving control of the material flows of long-lived products will be presented using the example of rotor blades.