Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10745
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Main Title: Characterizing the Urban Mine—Simulation-Based Optimization of Sampling Approaches for Built-in Batteries in WEEE
Author(s): Mählitz, Paul Martin
Korf, Nathalie
Sperlich, Kristine
Münch, Olivier
Rösslein, Matthias
Rotter, Vera Susanne
Type: Article
Language Code: en
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.
URI: https://depositonce.tu-berlin.de/handle/11303/11855
http://dx.doi.org/10.14279/depositonce-10745
Issue Date: 4-Sep-2020
Date Available: 6-Nov-2020
DDC Class: 333 Boden- und Energiewirtschaft
Subject(s): built-in batteries
WEEE
urban mine
sampling
UNUkeys
minimum sample size
data-driven simulation
bootstrapping
recycling-oriented characterization
Sponsor/Funder: EC/H2020/641999/EU/Prospecting Secondary raw materials in the Urban mine and Mining waste/ProSUM
TU Berlin, Open-Access-Mittel – 2020
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Recycling
Publisher: MDPI
Publisher Place: Basel
Volume: 5
Issue: 3
Article Number: 19
Publisher DOI: 10.3390/recycling5030019
EISSN: 2313-4321
Appears in Collections:FG Kreislaufwirtschaft und Recyclingtechnologie » Publications

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