Weee4magnets: optimizing the use of waste electrical and electronic equipment for the production of permanent magnets.

Abstract

The recovery of waste electrical and electronic equipment (WEEE) has become a major issue for the solid waste management. Investigating new ways of WEEE disposal has become mandatory in most countries of the world. Designing a value chain to recover useful secondary raw materials and specifically magnetic materials for the production of permanent magnets is a promising direction to this end. Initially, an overview of the literature was carried out regarding the project and the three pilots it includes. Then we delved into the specific pilot and the 3 companies that make it up. Historical data on the classification of WEEE and their magnetic content specifically for NdFeb and Sr-Ferrite magnet materials were then reviewed. This was followed by a general classification of the WEEE containing the largest amounts of NdFeb magnets, and how these flows have evolved from the beginning of 1990 until today. Designing an optimal network for WEEE plays a crucial role in determining the overall energy cost of the recovery system. In this thesis, considering the uncertainty in grid operation, a preliminary linear programming model was developed for WEEE recovery to address the problem, which primarily stemmed from recovery uncertainty related to magnetic content percentage proportions and energy costs. These factors comprise synthetic data and notably deviate from constraints, enabling decision-makers to adjust the robustness level of the operating system. The computations were performed using Excel and the results indicated that, given the input parameters, the linear programming model successfully calculated the total energy cost required for WEEE reuse to produce permanent magnets. At this early stage, the project is constrained by a significant shortfall in essential data: namely, baseline figures required for benchmarking purposes and operational data from the value network's factories. It is our expectation that, in the future stages of our research, these data gaps will be filled. This development is anticipated to provide a robust foundation for refining the proposed model and extracting further analytical insights.