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Methodology paper - Composition data collection and consolidation

Zenodo (CERN European Organization for Nuclear Research)

Abstract

The FutuRaM project aims to develop the Urban Mine Platform, a comprehensive knowledge base on the availability and recoverability of secondary raw materials across the European Union, Iceland, Norway, Switzerland, and the United Kingdom. The project focuses on six main waste streams: waste batteries, end-of-life vehicles, waste electrical and electronic equipment, construction and demolition waste from buildings and wind turbines, slags and ashes, and mining waste. These waste streams represent significant sources of critical and strategic raw materials. To estimate the availability of secondary raw materials from 2010 to 2050 under different scenarios, the project develops stock, flow, and recovery models based on detailed composition data. This data describes the structure of products, their components and material fractions, and elemental content, providing essential insights into the quantity, distribution, and potential recovery of critical raw materials in waste streams. The aim of milestone 23 of the FutuRaM project is to provide consolidated composition datasets for all waste streams that can be used for subsequent stock and flow and recovery modelling as well as for recoverability and secondary raw material assessment. The FutuRaM composition data structure distinguishes between hierarchical layers within a waste stock or flow. Each layer in the hierarchy is composed of the entities of the previous one. This hierarchical structure enables a systematic and scalable description of composition across diverse waste streams. To better reflect the varying characteristics of the waste streams, the composition data model employs two distinct approaches, a product-centric approach and a deposit-centric approach. Based on this structure, composition data was collected using specifically developed templates enabling the collection of data according to the composition data model from various sources. For the consolidation of the collected raw data, a harmonized method was developed in order to transform the scattered raw data into consistent and generic datasets with the average composition of relevant products (or product groups) and waste stocks and flows. Data points with different data structures and levels of detail can be combined into generic compositions for products and waste stocks and flows by transforming them into the desired data structure and defining appropriate levels of detail. This methodology paper provides an overview of the composition data collection for each waste stream. The consolidation method is described and specifications for each waste stream illustrated. Challenges during the process are highlighted and recommendations for further improvement of the processes are provided. Furthermore, in this methodology paper, the considerations regarding future composition and composition data validation are presented. V2: Hyperlink errors corrected

Authors 18

  1. Technische Universität Berlin

    Affiliation as printed

    Technische Universität Berlin

  2. Technische Universität Berlin

    Affiliation as printed

    Technische Universität Berlin

  3. Technische Universität Berlin

    Affiliation as printed

    Technische Universität Berlin

  4. Geological Survey of Sweden

    Affiliation as printed

    Geological Survey of Sweden

  5. Leiden University

    Affiliation as printed

    Leiden University, Institute of Environmental Sciences

  6. Swiss Federal Laboratories for Materials Science and Technology

    Affiliation as printed

    Swiss Federal Laboratories for Materials Science and Technology

  7. United Nations Institute for Training and Research · United Nations University · United Nations University

    Affiliation as printed

    United Nations Institute for Training and Research

    United Nations University

  8. United Nations Institute for Training and Research

    Affiliation as printed

    United Nations Institute for Training and Research

  9. Fraunhofer Institute for Reliability and Microintegration

    Affiliation as printed

    Fraunhofer Institute for Reliability and Microintegration

  10. Bureau de Recherches Géologiques et Minières

    Affiliation as printed

    Centre scientifique et technique du BRGM à Orléans

  11. Bureau de Recherches Géologiques et Minières

    Affiliation as printed

    Centre scientifique et technique du BRGM à Orléans

  12. Swiss Federal Laboratories for Materials Science and Technology

    Affiliation as printed

    Swiss Federal Laboratories for Materials Science and Technology

  13. Flemish Government · Flemish Institute for Technological Research

    Affiliation as printed

    Flemish Government, Economy, Science and Innovation, Flemish Institute for Technological Research

  14. United Nations Institute for Training and Research

    Affiliation as printed

    United Nations Institute for Training and Research

  15. Fraunhofer Institute for Reliability and Microintegration

    Affiliation as printed

    Fraunhofer Institute for Reliability and Microintegration

  16. Flemish Government · Flemish Institute for Technological Research

    Affiliation as printed

    Flemish Government, Economy, Science and Innovation, Flemish Institute for Technological Research

  17. Flemish Government · Flemish Institute for Technological Research

    Affiliation as printed

    Flemish Government, Economy, Science and Innovation, Flemish Institute for Technological Research

  18. Technische Universität Berlin

    Affiliation as printed

    Technische Universität Berlin

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