ProSUM -Latin for "I am useful" -aims to provide better information on raw materials from secondary origins. It focuses in particular on the content of Critical Raw Materials (CRMs) from Batteries (BATT), Waste Electrical and Electronic Equipment (WEEE), End of Life Vehicles (ELV) and Mining Wastes (MIN) available for processing in Europe. However, data for these products are usually very scattered amongst a variety of institutions, including government agencies, universities, NGOs and industry. This deficit is addressed in this H2020 funded project. ProSUM will establish a European network of expertise on secondary sources of CRMs, vital to today's high-tech society. It coordinates efforts to collect secondary CRM data and collate maps of stocks and flows for materials and products in the "urban mine". The project will construct a comprehensive inventory identifying and mapping CRM stocks and flows across the European Union (EU). Via a user-friendly, open-access Urban Mine Knowledge Data Platform (EU-UMKDP), it will combine and relate them to primary raw materials data from the EU-FP7 Minerals4EU project and communicate the results online through the future European Geological Data Infrastructure (EGDI) at large. It will also provide update protocols, standards and recommendations to maintain and expand the EU-UMKDP in the future.
Comprehensive knowledge of built-in batteries in waste electrical and electronic equipment (WEEE) is required for sound and save WEEE management. However, representative sampling is challenging due to the constantly changing composition of WEEE flows and battery systems. Necessary knowledge, such as methodologically uniform procedures and recommendations for the determination of minimum sample sizes (MSS) for representative results, is missing. The direct consequences are increased sampling efforts, lack of quality-assured data, gaps in the monitoring of battery losses in complementary flows, and impeded quality control of depollution during WEEE treatment. In this study, we provide detailed data sets on built-in batteries in WEEE and propose a non-parametric approach (NPA) to determine MSS. For the pilot dataset, more than 23 Mg WEEE (6500 devices) were sampled, examined for built-in batteries, and classified according to product-specific keys (UNUkeys and BATTkeys). The results show that 21% of the devices had battery compartments, distributed over almost all UNUkeys considered and that only about every third battery was removed prior to treatment. Moreover, the characterization of battery masses (BM) and battery mass shares (BMS) using descriptive statistical analysis showed that neither product- nor battery-specific characteristics are given and that the assumption of (log-)normally distributed data is not generally applicable. Consequently, parametric approaches (PA) to determine the MSS for representative sampling are prone to be biased. The presented NPA for MSS using data-driven simulation (bootstrapping) shows its applicability despite small sample sizes and inconclusive data distribution. If consistently applied, the method presented can be used to optimize future sampling and thus reduce sampling costs and efforts while increasing data quality.
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