High-quality recycling through self-learning and resilient recycling networks using a combination of agent-based modelling and life cycle assessment

Abstract

Especially in the case of long-lived products, the crucial questions about the proper implementation and assurance of high-quality recycling targets often only arise after decades. Furthermore, information about material composition is often not sufficiently known and communicated to the end user. With the presented extended socio-technical approach of a self-learning and resilient recycling network, which should include manufacturers and operators of wind farms, dismantlers, waste processors and recyclers, as well as authorities and players from research and development, such problems can be adequately addressed. On the one hand, this requires knowledge tools to ensure a high-quality material cycle, such as databases in which installed products and their characteristic values for the masses and materials used are documented. In addition, material flow modeling to track material flows generated for the end-of-life (EoL) of products including life cycle assessments of recycling and disposal routes, as well as forecasting tools for expected waste volumes are needed. On the other hand, a simulation tool such as agent-based modeling (ABM) is also needed to map courses of action and their impacts, taking into account stakeholders’ interests in terms of target formulation of the recycling network. The example of wind turbine rotor blades is used to show how such an approach can be used for a meaningful recycling network, which supports the operator responsibility of wind farms as well as the extended producer responsibility of wind turbines with regard to sustainable recycling of long-lived products. The developed tools and especially their active combination are presented. In addition, the example of rotor blades is used to present the concrete possibilities for resource-saving control of the material flows.