Characterizing the Urban Mine—Simulation-Based Optimization of Sampling Approaches for Built-in Batteries in WEEE

Abstract

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.

Waste electrical and electronic equipments as urban mines in Burkina Faso: Characterization and release of metal particles

“…Interferences must have intervened in the digestion of the other metals due to the heterogeneity of the particles. Literature available data confirm the influence of sampling on the variability of metal content from WEEEs (Laubertova et al, 2019;Mählitz et al, 2020).…”

Contributions of extended batch tests for assessing technical recyclability: A case study of low‐value battery flows

“…Data from different studies on other treatment processes must be collected in a harmonized way to determine the product recyclability independent of a process, providing comparable results that are representative for the corresponding product (Commission Regulation (EU) No 493/2012, 2012). Making results from the EBT approach available, for example, in an urban mine knowledge database (Huisman et al., 2017; Korf et al., 2019; Mählitz et al., 2020) as a possible extension of the Raw Material Information System (RMIS) (EU Science Hub, 2021), is seen as a long‐term goal of improving RA. This knowledge allows the calculation of recyclability in different scenarios and the assessment and definition of targets effectively focusing on the most (environmentally) relevant materials.…”

“…This development has resulted in (a) reduction or substitution of cost‐intensive battery material such as cobalt and nickel (Huisman et al., 2020) and (b) polymetallic subsystems enhancing battery performance while reducing costs. The resulting product diversity, in combination with varying lifetimes for EEE‐specific battery systems (Mählitz et al., 2020) and the hoarding of batteries by end users (Pistoia, 2005), has led to a mixture of battery subsystems in the urban mine with high variability and heterogeneity (Huisman et al., 2017).…”

Thermodynamic rarity of electrical and electronic waste: Assessment and policy implications for critical materials

“…Applying these procedures was necessary because of the rapid innovation and varying lifespan of electronics (Bakker et al., 2014), the characterization of the WEEE population is a complex task (Rigamonti et al., 2017). Indeed, all WEEE categories include a large number of devices characterized by a complex mixture of materials and components that changes over time and space in percentage and size in each WEEE category (e.g., fridge, lamps, monitors) as well as in similar equipment (Mählitz et al., 2020). In addition, although the collaboration with the WEEE processor allowed the understanding of the main EoL treatment processes of EEE, only limited aggregate and detailed company inventory data were available.…”